The 100% Container Scanning Legislation

Transcription

An analysis of waiting lines and economic costs
Thesis Maritime Economics & Logistics
By:
Miguel Omar Tovar Fernandez
Supervisors:
Dr. Koen G. Berden
Prof. Marco Sorgetti
Rotterdam, 26 June 2009
ECORYS Nederland BV
P.O. Box 4175
3006 AD Rotterdam
Watermanweg 44
3067 GG Rotterdam
The Netherlands
T +31 (0)10 453 88 00
F +31 (0)10 453 07 68
E [email protected]
W www.ecorys.com
Registration no. 24316726
ECORYS Macro & Sector Policies
T +31 (0)10 453 87 53
F +31 (0)10 452 36 60
KK/MF CD17500
Acknowledgements
Academic thanks:
I am please to have worked under the guidance of Professor Koen Berden who believed
since the beginning in this project. Also I want to thank my supervisor Marco Sorgetti
who has been very patient in the development of this study. Special thanks to Professor
Romert Dekker who contributed with his time and effort in this study. Also I want to
thank Dra. Moira Murphy my academic inspiration through these years. I also want to
thank the MEL staff for all their encouragement throughout this year.
Sponsor thanks:
I want to specially thank to Adrian Nunez who has trusted in me unconditionally since we
met. I want to thank Oscar and Claudia Ruiz and their lovely children who have
expressed their friendship more than one time throughout the years. Thanks to Estela
Fernandez who has inspired me throughout my life. To Mister and Missis Espinoza who
have supported me unconditionally the past years. Finally special thanks to Raul and
Diana Villegas for trust in me and our friendship for more than 20 years.
Special thanks:
To Andres Melendez, Susana Lievano, Lorena Lievano and Dolores Case, Ivan, Ayca,
Nareen and Cherif for all the support this year.
I dedicate this study with all my love to God and my family: Tita, Pinna, Sergio, Regina
and Belen. Without them my life would be senseless.
Table of contents
Summary
9
1 Introduction
1.1 Problem Scope
1.2 Study Argument
1.3 Justification of the Research
1.4 Research Question
1.5 Methodology and Data Collection
1.5.1 Methodology
1.5.2 Data Collection
1.6 Overview and structure of the Study
11
11
12
12
13
14
14
15
15
2 Supply Chain Security and General Aspects of the 100% Container
Scanning Legislation
2.1 Introduction
2.2 Supply Chain Security
2.2.1 What is Supply Chain Security?
2.2.2 Measures and Activities Adopted by Supply Chain Security
2.2.3 Implications and costs of the security programs
2.3 Current Supply Chain Security Programs
2.3.1 Customs Trade Partnership Against Terrorism (C-TPAT)
2.3.2 Megaports Initiative
2.3.3 Container Security Initiative (CSI)
2.3.4 Secure Freight Initiative (SFI)
2.3.5 Other Security Initiatives
2.4 A new Law has born: 100% Container Scanning Legislation
2.5 Challenges and questions
2.6 U.S. Commercial Partners
2.7 Chapter Conclusion
17
17
17
17
18
18
19
19
19
20
20
21
22
25
28
29
3 Technical aspects of the 100% container scanning legislation
3.1 Introduction
3.2 Process of Integrated Inspection System
3.3 Integrated Inspection System Equipment
3.4 Health issues related to NII container scanning
3.5 Chapter Conclusion
31
31
31
34
39
39
4 Review of literature on waiting lines and economic costs
4.1 Introduction
41
41
KK/MF CD17500
4.2 Academic research
4.2.1 Questions regarding operations and waiting lines
4.2.2 Questions regarding Cost factor
4.3 Report to Congress on Security Freight Initiative (SFI) pilot programs
4.4 Evaluating the viability of 100 percent container inspection at America’s
ports
4.5 Chapter conclusion
5 The models and data
5.1 Introduction
5.2 Description of the Models
5.2.1 Queuing model formulas
5.2.2 Single channel waiting line model with Poisson arrivals and
exponential service times: M/M/1
5.2.3 Multiple channel waiting line model with Poisson arrivals and
exponential service times: M/M/k
5.2.4 Waiting Line Simulation model formulas
5.3 Cost Analysis
5.3.1 Waiting costs
5.3.2 Transfer Costs
5.3.3 Service costs
5.3.4 Total costs
5.4 Data Collection
5.5 Scenarios
5.5.1 Scenario 1A
5.5.2 Scenario 2A
5.5.3 Scenario 2B
5.5.4 Scenario 2C
5.5.5 Scenario 2D
5.6 Small and Big port selection
5.6.1 Dubai – Small port
5.6.2 Hong Kong – Big port
5.7 Conclusions
41
42
42
45
50
50
51
51
51
51
52
53
55
56
56
56
56
57
57
57
57
58
58
58
58
58
59
59
60
6 Measuring the Congestion Impact: Analysis and Results of Waiting Lines
6.1 Introduction
6.2 Forecast and Container Flows
6.3 Results and analysis of the Queue Model
6.4 Results and analysis of the Simulation Model
6.5 Model Comparison and Waiting Time Analysis
6.6 Conclusions
61
61
61
67
82
96
102
7 Measuring the Economic Impact: Analysis and Results of Waiting Lines
7.1 Introduction
7.2 Direct and Indirect costs of the Legislation
7.2.1 Direct Costs
7.2.2 Indirect Costs
7.3 Economic Analysis of Waiting Lines
103
103
103
103
106
107
7.4 Optimal Selection of Systems
7.5 Cost Analysis of Waiting Lines
7.5.1 Cost Analysis Scenario 1A
7.5.2 Cost Analysis Scenario 2A
7.5.3 Cost Analysis Scenario 2B
7.5.4 Cost Analysis Scenario 2C
7.5.5 Cost Analysis Scenario 2D
7.6 Sensitivity of results to the percentage of Containers Scanned
7.6.1 50% Container Scanning
7.6.2 25% Container Scanning
7.7 Conclusion
113
116
116
117
118
118
119
121
121
123
124
8 Conclusions
8.1 Final thoughts
8.2 Limitations of the study
8.3 Areas for future research
127
127
131
131
Annex I
133
KK/MF CD17500
References
Summary
Since the terrorist acts experienced on September 11, 2001, the U.S. government has
developed security programs and initiatives to enhance the security levels to protect
people, but also goods flowing throughout the global supply chain. Each security program
developed by the U.S. government with the cooperation of the private sector has been
implemented in all the nodes of the global supply chain, including the Maritime sector.
From all the security programs adopted by the U.S. government none has been so
controversial than the House of Representatives 1 (H.R.1) also know by the shipping
community as The U.S. 100% Container Scanning Legislation”. This new security
mechanism adopted by the U.S. government, establishes that all the maritime U.S.-bound
containers be scanned at foreign ports of loading or else be denied entry into the United
States with a planned implementation date of July 2012.
This unilateral legislation adopted by the U.S. government has been received with full
scepticism and controversy by the members of the shipping community and foreign
nations. Many stakeholders of the shipping sector and foreign governments have
expressed its inconformity about the new mechanism. These arguments question the
feasibility and costs that will be incurred in order to fulfil this new requirement. Some of
these opinions have roughly tried to measure the possible impacts on costs and delays.
However, these estimations lack of scientific research, trying to focusing only in the
assessment of the operational costs. Although, these estimations have left aside the costs
that the waiting lines are going to represent. Thus, the objective of this study is to
contribute to the topic, through a congestion and economic analysis of waiting lines
derived form the implementation of the 100% Container Scanning Legislation.
This study parts from a single question:
What will be the impact on waiting lines and therefore operational costs if the 100% container scanning
legislation is implemented?
In order to assess the impact of the law, this study has divided the waiting lines analysis
in two important areas: Congestion impact and Economic impact. With the aid of Queue
Theory and Simulation models, this study is designed to assess the congestion delays and
economic costs derived from the new legislation. In order to measure the possible
outcomes, this study has based its calculations in five possible scenarios that might result
form the implementation of the Law in 2012. Each scenario is measured by a big and
small port examples based on current U.S. container exports. This Study is valuable in
several important ways, especially because is the first attempt to estimate the waiting
lines derived from the implementation of the 100% Container Scanning Legislation and
because promise the lecturer to change its perspective about this new issue.
9
1 Introduction
1.1
Problem Scope
The terrorist attacks suffered on September 11, 2001 changed completely the perspective
of security around the world. Since then, the U.S. government and foreign countries have
adopted new measures and security standards to secure not only the passenger industry,
but also the movement of goods through the global supply chain. These new standards
have been developed and implemented through several security programs in order to
protect the integrity of the flow of passengers and goods. These security programs have
been developed and implemented by government dependencies and international
organizations, with the cooperation of the private sector. The shipping Industry as one of
the principal pillars of world trade has not been the exception, which has been
participated cooperatively with national and international governments and dependencies
to enhance the security supply chain.
Derived from the 9/11 Commission Report, in the first session of the 110th Congress has
enacted the House of Representatives 1 (H.R.1) legislation, also called the “Implementing
Recommendations of the 9/11 Commission Act of 2007” was signed on August, 2007 by
President George W. Bush. Under title XVII “Maritime Cargo” of the Implementing
Recommendations of the 9/11 Commission Act of 2007, on subsection 1701, sub point
(a) Container Scanning; amends Section 232(b) of the SAFE Port Act (6 U.S.C. 982 (b),
and establishes a “Full Scale Implementation” to all containers destined to the United
States, to be scanned at Foreign ports prior to their arrival, or else be denied entry into the
United States. The regulation of the Subsection 1701 of the Implementing
Recommendations of the 9/11 Commission Act of 2007 is known by the maritime
community as the “100% Container Scanning Legislation” and is expected to enter in to
force by July 2012.
Many reactions about this controversial law have been expressed by several stakeholders
of the shipping sector, most of them against the implementation of the 100% Container
Scanning. Foreign governments and some members of the shipping community have
expressed their inconformity due the vagueness of the law, questioning the feasibility and
the effectiveness of the new regulation. A lot of speculations and gossips have been told
about the possible effects and impact that would experience if the new law is
implemented. However, these opinions lack support because not much scientific research
has been made about the topic. A few academic researches have been made in order to
estimate the possible economic cost of the equipment installation itself, but none about
the effects of the congestion and economic effects of the waiting lines that might
represent at port terminals.
11
1.2
Study Argument
“The terrorist attacks suffered on September 11th 2001 in New York City have
encouraged the U.S. Government to increase security levels and to develop new
initiatives in order to protect people and goods throughout the global supply chain. Last
August 2007 the US Congress enacted the H.R.1 also called by the shipping community
as “The 100% Container Scanning Legislation”, effective in July 2012, and requiring
all maritime cargo containers bounding to the United States to be scanned at foreign
ports of loading. Recent studies have tried to measure the economic impact on freight
derived from the new legislation, but have fail to tackle the economic and congestion
impact of waiting lines of containers at foreign ports. Furthermore, the current
literature expresses pessimism in regards to the ability and willingness of governments,
the shipping community and international organizations to fulfill this legislation, calling
into question the effectiveness and feasibility of the mechanism. By estimating the
economic and congestion impact of the waiting lines at ports derived from the
legislation, we can assess a closer approach of the impact of the legislation, contributing
to the topic in order to determine its viability by comparing it against other possible
security alternatives. Providing a set of Queue and Simulation models, and costing tools
we can conduct an economic and waiting time analysis derived from the implementation
of the legislation. Based on the Data collection and supported assumptions, this model
approach is shown to accurately estimate the economic impact and the waiting time of
containers at foreign ports derived from the implementation of the 100% container
scanning legislation”.
1.3
Justification of the Research
Since the US government enacted the 100% Container Scanning Legislation, many critics
have questioned the feasibility of its implementation. Most of them have argued about the
fulfillment of the legislation judging the mechanism without any study that supports their
opinions. The major concerns expressed by the shipping community are the extra costs
and congestion delays that might result from this new security initiative. Some studies
have tried to assess the economic impact of the legislation calculating only the operation
costs of the Non-Intrusive Inspection (NII) scanner itself. However, no economic impact
of the legislation is complete ifhave left aside the economic costs generated by excessive
waiting times derived from congestion. Even though the costs of the waiting lines are
indirect costs attributable to the cost of service, the importance to include the waiting line
costs into a cost-analysis is in order to measure the efficiency of the service. The time
spent by containers in a waiting line to obtain a service incur in operational costs which
diminishes the productivity of the overall welfare of the supply chain. If excessive
waiting lines are not tackled by the service provider, the productivity of the service will
decrease, resulting in reallocations of consumers looking for other service providers or
alternative solutions to avoid the congested system. Thus, in order to complement other
studies and support or refuse some opinions made by the shipping community, it is
necessary to conduct a scientific study of the waiting lines with reasonable estimates of
the waiting costs and service costs in order to have a good assessment of the economic
impact of the legislation. The mission of this study is not to determine who will bare the
12
total costs of the legislation, the answer is simple: the final consumer; the objective of this
study is to assess a closer estimate of the economic burden for the supply chain derived
form the new security policy.
1.4
Research Question
The 100% Container scanning Law implemented by the United States is a regulation
introduced recently to the shipping industry. Although the application of the Law by the
US Government is still years ahead, the possible impacts that might result in the shipping
sector are already enough to concern shippers, organizations, terminals and foreign
governments. Due to the newness of the issue, and the lack of methodological studies that
have been conducted after the enactment of the Legislation by Congress and President
George W. Bush in July 2007, this study is the first scientific attempt to estimate the
economic and congestion impacts of waiting lines of the 100% Container Scanning
Legislation. In the context of this pioneering study we are parting from our study research
question:
What will be the impact on waiting lines and therefore operational costs if the 100% container scanning
legislation is implemented?
Derived from this research question we can separate the study of the impact of the 100%
container scanning in two areas of waiting lines: Congestion and Cost analysis of the
waiting lines.
With regard to the waiting lines or queues it is important to answer the most common
questions expressed by the shipping community: Are the foreign ports going to collapse
due congestion because this new legislation? Is possible to scan containers in high
volumes at terminals? How much time the containers would have to spend on queue in
order to be scanned? Are the flows of containers bounding to the United States changing
their courses because the new legislation? Are going to increase transshipments derived
from the implementation of the law? What would be the average number of containers in
the waiting line? What would be the average length of the waiting line? These sub
questions regarding the waiting lines will be implicitly answered in this study.
In the case of the Cost Analysis, it is important to answer the questions: how much would
be the economic cost to scan every U.S.-bound container? How much would increase the
freight in economic terms the Law implementation? How much would be the costs of the
waiting lines? How much would represent the cost for the global supply chain? How
many scanner systems are required to provide an optimal service? How much would
represent the transfer costs for the transshipped containers? Are the small ports in
disadvantage form the big ports regarding the scanning service cost? Also these sub
questions on cost analysis will be implicitly answered in this study.
It needs to be noted that the aim of this study is explicitly not to carry out a full costbenefit analysis of the law, but merely provide a solid analysis of the (economic) cost side
of the 100% Scanning Legislation and the behavior of waiting lines.
13
Why is it important to conduct a waiting lines analysis and answer the research questions
and the sub questions? This study is remarkably valuable for many reasons. It is
important because so far no study has assessed the operational impact of waiting lines due
the implementation of the 100% Container Scanning Legislation. It is important because
the until now the economic costs of the waiting lines derived form the legislation have
been underestimated in previous cost analyses. It is important because it contributes to the
shipping industry to have a scientific estimation of the behaviors of waiting lines after the
implementation of the law. It is important because the analysis and model utilized in this
study can be applied to any port around the world, including U.S. ports in case reciprocity
is demanded by foreign countries to the United States. It is important because the
implementation of the legislation currently is one of the major concerns in the shipping
industry due its magnitude. It is important because lack of scientific studies about the
topic, creates uncertainty and speculation of the outcomes of the implementation of the
law. This research is also important, because it is an interesting topic for the shipping and
the non-shipping community. And finally, it is important because after reading the full
study, a change in the current perception of the reader about the 100% Container
Scanning Legislation is likely.
1.5
1.5.1
Methodology and Data Collection
Methodology
In order to conduct this research in this study will be use two quantitative approaches to
measure the waiting lines: the Queuing Theory and Simulation methods. The Queue
model is based in mathematical formulas and relationships that can be used to measure
the performance of a waiting line (Anderson, Sweeney, Martin, Williams, [2008 p. 545]).
The simulation model it is a method for learning about the performance of the system by
experimenting with a model that represents the real system (Anderson, Sweeney, Martin,
Williams, [2008 p. 586]). In chapter 5 these models will be explained in detail. This study
uses the following models to assess the impact of the 100% container scanning
legislation:
1. Single channel waiting line model with Poisson arrivals and constant service time
“M/M/1 Model”;
2. Multiple channel waiting line model with Poisson arrivals and constant service
time “M/M/k Model”;
3. Waiting Line Simulation, with exponential arrivals and general service times.
The economic analysis of waiting lines
Next to the impact on waiting line behavior, it is necessary to conduct a cost analysis in
order to measure the efficiency of the waiting line and to find an optimal number of
service channels (scanners in our study) for the system. The addition of an extra scanner
of service is linked with the cost of operating an additional scanner. The addition of an
extra scanner also depends on the goals of waiting service established by the
administration.
TC= cw L + cs k
14
1.5.2
Data Collection
The data collection was obtained from secondary and primary data. All the figures
employed in this analysis are supported by related literature of the topic. The literature
has been obtained from academic studies, private and government research. The volumes
of containers bounding to the United States utilized in our study are based in the figures
estimated by PIERS (2008), collected from vessel; Manifests and bills of lading;
excluding postal and militarily shipments. All the information employed is supported by
published articles from experts in their area of study. As well, an interview with the
security port authority of the port of Rotterdam has been held trough a systematic
questionnaire in order to obtain as much as data possible from primary source about the
possible impacts of the 100% Container Scanning Legislation.
1.6
Overview and structure of the Study
The study is structured in 8 chapters. The current chapter 1 is the introduction of the study
and the research question and sub questions. In the second chapter will be described the
concept of the Supply Chain Security, the current security programs for the Maritime
sector, and General aspects about the100% container scanning legislation. The third
chapter is focused on the technical aspects of the legislation: process, equipment utilized
and the health issues. The fourth chapter analyses the current studies regarding the
Legislation and literature review about the topic. The fifth chapter describes the
methodology and quantitative models utilized in our economic and congestion analysis of
the waiting lines in chapters six and seven. The sixth chapter assesses and explains the
results of the congestion impacts by the waiting lines derived from the application of the
selected models. Chapter seventh conducts an economic analysis of the waiting lines
derived from the application of the legislation. Finally, the eighth chapter concludes the
study with the final thoughts of the research. As well are described some limitations of
the research in order to conduct the study. Finally to end the study, future research is
suggested in order to keep contributing findings to the problem study.
15
2 Supply Chain Security and General Aspects of
the 100% Container Scanning Legislation
2.1
Introduction
The terrorist attacks on September 11th, 2001 in New York City represented a threshold
in the history of the United States, forcing immediate actions by the U.S. government in
order to secure their territory to avoid further strikes. Living in a globalize world, these
attacks not only affected US American citizens way of life, but also have implied changes
for other countries too. These changes not only took place in the passenger sector, but
also in the security practices of the world trade itself. A new concept was born “Supply
Chain Security” referring to the new practices by the international trade community in
order to enhance the security levels in the flows of world trade. These practices became
security programs developed by government dependencies, international organizations
and private sector. Each of these programs has their own objectives and security measures
designed to secure each stage of the global supply chain. From all of the security
programs adopted by the U.S. government, no one has been more controversial for the
shipping community than the 100% Container Scanning Legislation. In this chapter will
be described the meaning and objectives of the Supply Chain Security. Then will be
explained the most relevant current security programs implemented for the shipping
sector in order to secure the global supply chain. After this, a critical analysis of the
legislation will be performed to measure its feasibility the possible challenges that might
face in the meanwhile to its implementation in July 2012. And finally at the end of the
chapter will be described the major U.S. container exporters to the United States, which
will be the more impact countries due the implementation of the legislation.
2.2
2.2.1
Supply Chain Security
What is Supply Chain Security?
According to Chopra and Meindl (2007 p.3)
“A supply chain consists of all parties involved, directly or indirectly in fulfilling a customer request,
including not only the manufacturer and the supplier, but also transporters, warehouses, retailers, and
even customers themselves”.
After the terrorist attacks of the 9/11 on the Twin Towers in New York City, the world
and the international trade community realized the necessity to increase their security
measures in order to prevent further attacks not only to the passenger sector, but also to
the international trade sector. As a result the term “Security Supply Chain” was
17
employed, making the security factor part of the equation of the supply chain. The
Security Supply Chain focuses in two principal objectives. The first objective is to
prevent any threats and attacks that harm the natural flow of goods throughout the global
supply chain that might represent economical and/or human losses. The second objective
is to avoid the utilization of the international supply chain as mode of transport of any
type of illegal goods, radiological materials, or any other substances or objects that might
represent any risk to the world trade community and its member states. In order to reach
these objectives the world trade community has participated in several security programs
applying security standards and measures within their organizations.
2.2.2
Measures and Activities Adopted by Supply Chain Security
International Governments, global and local organizations, and the private sector have
developed security programs to enhance the security level of the supply chain. These
security programs have adopted measures and practices applied in three major areas of
the supply chain. The first area is focused to secure the flow of goods throughout the
global supply chain, from its origin to its final destination. The second group is based in
the different transport modes used move goods from one stage to another within the
supply chain. The third group aims to secure the connection nodes throughout the global
supply chain. In this sense derived from the differences of many supply chains resulting
from different types of movements of goods in the world trade, the U.S. Department of
Homeland Security have identified 16 similar nodes within the global supply chain. The
measures and practices adopted in these three areas include security standards within the
organizations and modes of transport. These measures include but are not limited to the
exchange of information of entities between connection nodes, inspection and validation
of the contents within the transport modes, the use of new and current technology to
enhance security levels, joint cooperation between partner countries, administration
security standards adopted within the organizations, analysis of historical data from
shippers and cargo, among other measures.
2.2.3
Implications and costs of the security programs
The implementation of a security program may lead to other implications for the world
trade. These implications might represent an increase in costs, delay and congestion in the
movement of goods throughout the connection nodes, excessive inspection measures,
overlapping of jurisdictions between local and international dependencies, excessive
requisites and permits required by governments in order to move goods throughout the
global supply chain, among other possible repercussions. These implications derived
from implemented security programs could represent higher costs and delays in the
international flow of goods. In order to avoid these possible repercussions, a security
supply chain program must be measured in terms of operational viability and incurred
costs. If the security chain program is balanced in terms of feasibility and costs we can
say it is an efficient security strategy. Most of the efficient security strategies are
implemented at equal or lower cost basing its security standard on prevention and
responsibility of its own area of work within the supply chain. The importance to develop
18
an efficient security strategy is in order to avoid any disruption the steady flow of goods
throughout the supply chain that might affect the overall welfare of the world trade.
Currently there are several security programs in order to protect the welfare of the global
supply chain, especially in the shipping industry which represents more 90% of the
containerized world trade. These programs have different objectives and jurisdictions to
protect the integrity of the supply chain. In the next section will be briefly described the
most relevant currently implemented security programs related with the security of
containerized cargo by the shipping industry throughout the supply chain.
2.3
Current Supply Chain Security Programs
The principal objective of security supply chain is to improve safety to the flow of goods
and this can not be accomplished without proper programs. The most important current
programs implemented by the U.S. government are the CTPAT, the CSI, the Megaports
initiative and the SFI. In the following sub sections are briefly described these major
security programs currently implemented by the U.S. government. As well, it will be
mentioned other relevant security programs created by the U.S. government and other
International Organizations in order to enhance security in the shipping industry and the
supply chain. The information describing the security programs was retrieved from the
US government and International Organizations web pages.
2.3.1
Customs Trade Partnership Against Terrorism (C-TPAT)
The C-TPAT program was created in November, 2001 by the U.S. Customs and Border
Protection (CBP). The Objective of the C-TPAT is to secure the international supply
chain, working jointly through the voluntary participation of the trade community in order
to protect and facilitate trade of the United States and its commercial partners. The core
of this initiative is to prevent terrorism and the introduction of weapons of mass
destruction encouraging their commercial partners to practice security standards within
their operations, through a certification granted by the CBP. In return by the CTPAT
certification, the CBP offers preferential treatment to its commercial partners, through
reduced inspections at arrival ports, faster processing crossing at borders, among other
incentives. As a result of the C-TPAT program is to have a high number of certificate
commercial partners bounding shipments to the United States that accomplish the security
criteria by the CBP, in order to focus their resources in the inspection of high risk
shipments (CBP,[2006]).
2.3.2
Megaports Initiative
The Megaports Initiative is developed by the National Nuclear Security Administration
(NNSA), a branch of the Department of Energy (DOE) of the United States. The
objective of the Megaports Initiative is to work jointly with the foreign partners in order
to equip their seaports with modern technology in order to detect radiation materials. The
Megaports initiative as a layered component of the security strategy of the U.S.
19
government, aims to detect any radiological materials capable to be used in the
elaboration of weapons of mass destruction. The equipment of radiation detection
includes state-of-the-art radiation detection equipment, software packages, and
communication systems in order to identify any presence of nuclear material. Currently
the Megaports Initiative has been implemented in twelve countries, with plans to expand
the program to other 18 countries. The Megaports initiative also works jointly with other
U.S. government dependencies and programs like C-TPAT, the Container Security
Initiative (CSI), the Security Freight Initiative (SFI) also mentioned in this section
(NNSA, [2006]).
2.3.3
Container Security Initiative (CSI)
Created by The US Customs and Border Protection (CBP), the objective of the Container
Security Initiative (CSI) program is to inspect at foreign ports all the U.S.-bond
containers that might represent a potential risk of terrorism to the United States. The CSI
is a voluntary program between the CBP and the host country, in which personnel of both
entities work together in order to identify and detect potential threats concealed inside the
containers bounding to the United States. There are two major missions performed by the
CSI program. First, CBP officers must pre-screen all the containers bounding to the
United States. The pre-screening of containers is done by Automated Targeting Systems
(ATS) consisting in software systems relating data analysis, risk analysis, user interfaces
and other sources of information related with the cargo itself. And the second mission of
the CSI is to scan through a Non-Intrusive Inspection system all the U.S.-bound
containers that might represent a risk for the welfare of the United States. One of the first
principles of the CSI is to work together with the cooperation of the host country without
harming their national sovereignty. In reciprocally retrieve from the participation of the
CSI ports, the host officers are invited to work jointly with the CBP officers at U.S. ports
to inspect containers bounding to their nations and learn about the procedures of
inspections carried in the United States. (CBP, [2006]; DHS, [2006], [2007]).
2.3.4
Secure Freight Initiative (SFI)
Derived from the Section 231 of the SAFE port Act of 2006, The Department of
Homeland Security (DHS) and the Department of Energy (DOE), in cooperation of
participant governments, created the Secure Freight Initiative (SFI) program. This
program has the principal objective of the SFI to evaluate the possibility to scan on a
100% volume all the containers bounding to the United States. The SFI program is
conformed by three program approach to enhance security through the supply chain: the
International Container Security; Security Filling; and the Global Trade Exchange (GTX).
Under the International Container Security program are performed the pilot program with
the participant governments, evaluating the Integrated Scanning System (ISS) on a 100%
basis with the mission to detect terrorist attacks utilizing nuclear and other radiological
materials through the supply chain that might represent a threat to the United States. The
container scanning through ISS is preformed through non-intrusive radiographic imaging
(NII) and a passive radiation detection through Radiation Portal Monitors (RPM)
equipment located at the shipping terminal. There are three ports were tested in a full
20
scale implementation: the Port Qasim, Karachi in Pakistan; Puerto Cortes, in Honduras;
and Southampton in United Kingdom. At a limited capacity implementation, there are:
Singapore (Brani Terminal), Busan in South Korea; the port of Salalah in Oman; and
Hong Kong (Modern Terminal) (SFI [2006]).
2.3.5
Other Security Initiatives
Other several security programs are currently implemented for the security supply chain
and for the shipping community including:
The 24 hour rule: is a mechanism were every container destined to United States must
submit to the dependency a manifest of information 24 hours before the container is
loaded into the vessel or to any Non-Vessel Operating Common Carriers (NVOCC)
(CBP, [2003]). The Automated Targeting Systems (ATS), already discussed in the CSI
program. The International Port Security Program (IPS) developed to protect the global
shipping industry by the facilitation of security improvements in ports around the world
(USCG [2008]). The International Ship and Port Facility Security Code (ISPS Code):
created by the International Maritime Organization (IMO) containing mandatory security
standards for port facilities and vessels (IMO, [2002]). The WCO SAFE Framework of
Standards: created by the World Customs Organization (WCO) basing in standards of
cooperation between customs authorities from partner countries (WCO, [2006]). The
employment of Container Security Devices (CSD): requested by the CBP and DHS, new
technology devices have been tested to enhance security throughout the supply chain
(DHS, [2007]). The Maritime Domain Awareness (MDA): is one of the plans developed
to support the National Strategy for Maritime Security (DHS [2005]). The Nationwide
Automatic Identification System (NAIS): Implemented by the U.S. Coast Guard (USCG)
is an identification system created to support MDA of the US nation territorial waters and
adjacent sea areas (USCG 2008a). The Automatic Identification System (AIS): is an
international standard of communication of information, including vessel identity,
position, speed, course, destination and other data for navigation safety and maritime
security (DHS, [2007]). Transportation Worker Identification Credential (TWIC): it
requires the transportation and/or port workers to use an issued identification card with
biometric information, allowing security personnel to control access to secure areas of
vessels and port facilities. (TSA, 2008). Security Filling (10+2) Initiative: as part of the
SFI program, consists to submit to the CBP approved Electronic Data Interchange (EDI)
10 data elements from the importer, plus 2 more from the carrier (CBP, [2008]). CBP
Cargo Screening: Currently the CBP screens 100% of the containers destined to the
United States through Automated Targeting Systems (ATS), including cargo remaining
on board (DHS, [2007]). Beside the current security programs implemented, a new
legislation has been created threatening the welfare of the shipping industry. In the next
section will be described the development of the creation of the 100% Container
Scanning Legislation.
21
2.4
A new Law has born: 100% Container Scanning Legislation
Derived from the 9/11 Commission Report aimed to protect further terrorist attacks to the
United States, the first session of the 110th Congress has enacted the House of
Representatives 1 (H.R.1) legislation, also called the “Implementing Recommendations
of the 9/11 Commission Act of 2007”. In title XVII “Maritime Cargo” of the
Implementing Recommendations of the 9/11 Commission Act of 2007, on subsection
1701, sub point (a) Container Scanning; amends Section 232(b) of the SAFE Port Act (6
U.S.C. 982 (b), and establishes a “Full Scale Implementation” to all containers destined to
the United States, to be scanned at Foreign ports prior to their arrival. The regulation of
the Subsection 1701 of the Implementing Recommendations of the 9/11 Commission Act
of 2007 is known in the maritime community as the “100% Container Scanning
Legislation”. The following material was taken from the H.R.1 or Implementing
Recommendations of the 9/11 Commission Act of 2007 and differs from its original form
(Implementing Recommendations of the 9/11 Commission Act of 2007 [2007]):
“…
TITLE XVII---MARITIME CARGO
SEC. 1701. CONTAINER SCANNING AND SEALS.
(a) CONTAINER SCANNING.—Section 232(b) of the SAFE
Ports
Act (6 U.S.C. 982(b)) is amended to read as follows:”
In section 1701 (a) refers to the Container Scanning issue, amending the section 232 (b)
of the SAFE Port Act 2006. In recall of the SAFE Port Act section 232 (b), the section
contemplated the Full Scale (100% Scanning) implementation to all containers bounding
to the United States, but also established the necessity of the DHS to determine that the
pilot programs were fully tested and implemented. Section 1701 (a) amends section 232
(b) of the SAFE Port Act 2006 into 9 sub sections, the first two “In general” description
and “Application” are the following:
‘‘(b) FULL-SCALE IMPLEMENTATION.—
‘‘(1) IN GENERAL.—A container that was loaded on a vessel
in a foreign port shall not enter the United States (either
directly or via a foreign port) unless the container was scanned
by nonintrusive imaging equipment and radiation detection
equipment at a foreign port before it was loaded on a vessel
‘‘(2) APPLICATION.—Paragraph (1) shall apply with respect
to containers loaded on a vessel in a foreign country on or
after the earlier of—
‘‘(A) July 1, 2012; or
‘‘(B) such other date as may be established by the
Secretary under paragraph (3). …”
Clearly stated in sub section (b)(1) all containers entering the United States must be
scanned at foreign ports prior to their arrival to US domestic ports, otherwise access will
22
be denied. The section also mentions the utilization of nonintrusive imaging equipment
and radiation detection equipment to perform the scanning at foreign ports. The deadline
of the implementation will be on July 1st, 2012 or any other date determined by the DHS.
It is clear that the 100% container legislation was approved without following the
specifications by the SAFE Port Act of 2006, which require full pilot programs
implementation reports and DHS approval before a full scale implementation. Section
1701 (b) continues with subsection (3):
‘‘(3) ESTABLISHMENT OF EARLIER DEADLINE.—The
Secretary shall establish a date under (2) (B) pursuant to the
lessons learned through the pilot integrated scanning systems
established under section 231….”
Then the secretary (DHS) could establish a new deadline derived from the lessons learned
from the pilot programs. In these sections it seems that the Congress tried to fix a problem
that they already knew they were not taking into consideration. The pilot programs (SFI)
were neither contemplated nor the need of DHS approval in order to implement a full
scale container scanning. The following section 1701 (b) continues with the possible
extensions of the Law by the DHS in subsection (4):
‘‘(4) EXTENSIONS.—The Secretary may extend the date
specified in paragraph (2) (A) or (2) (B) for 2 years, and may
renew the extension in additional 2-year increments, for containers
loaded in a port or ports, if the Secretary certifies
to Congress that at least two of the following conditions exist:
‘‘(A) Systems to scan containers in accordance with
paragraph (1) are not available for purchase and installation.
‘‘(B) Systems to scan containers in accordance with
paragraph (1) do not have a sufficiently low false alarm
rate for use in the supply chain.
‘‘(C) Systems to scan containers in accordance with
paragraph (1) cannot be purchased, deployed, or operated
at ports overseas, including, if applicable, because a port
does not have the physical characteristics to install such
a system.
‘‘(D) Systems to scan containers in accordance with
paragraph (1) cannot be integrated, as necessary, with
existing systems.
‘‘(E) Use of systems that are available to scan containers
in accordance with paragraph (1) will significantly
impact trade capacity and the flow of cargo.
‘‘(F) Systems to scan containers in accordance with
paragraph (1) do not adequately provide an automated
notification of questionable or high-risk cargo as a trigger
for further inspection by appropriately trained personnel…”
23
The legislation provides the authority to the DHS to extend the deadlines for the
implementation of the 100% container scanning if at least two of the previous conditions
exist: Condition (A) is practically fulfilled because the equipment exists and has been
already used in the pilot programs of SFI. Condition (B) the scanning systems do not
have a sufficient low false alarm rate, but how much is low rate? The NII scanning
equipment does not provide a false alarm of high risk material, only a picture of the inside
of the container. Condition (C), the foreign port does not have physical characteristics to
install the scanning equipment, what physical characteristics? Space in the port yard?
Condition (D) does not specify which current systems should interact with the scanning
equipment. Condition (E) states that if the utilization of the container integrated system
would affect significantly the trade capacity of the flow of cargo, but how could it
determine the impact on ports? who will determine the impact?. Finally, condition (F) as
well as in condition (B) the NII systems do not provide false alarms from risk materials,
only a picture of the container. It seems that all the conditions stated in the sub section (4)
appear confusing in order to delay by two years the implementation of the legislation on a
specific port. Subsections (5) (6) and (7) state the exemption to the military cargo and
some specifications to extend the period of implementation, not analyzed in this study.
Section 1701 (b) subsection (8) establishes the scanning technology standards required to
perform the scanning process at foreign ports:
‘‘(8) SCANNING TECHNOLOGY STANDARDS.—In implementing
paragraph (1), the Secretary shall—
‘‘(A) establish technological and operational standards
for systems to scan containers;
‘‘(B) ensure that the standards are consistent with
the global nuclear detection architecture developed under
the Homeland Security Act of 2002; and
‘‘(C) coordinate with other Federal agencies that administer
scanning or detection programs at foreign ports.
The
DHS is responsible to determine the technological and operational standards for the
scanning equipment, which must meet the standards developed at the Homeland Security
Act of 2002. To this regard, the DHS or the US Government can not impose a
specification to any foreign port that would violate the principal of national sovereignty.
Some foreign governments already applied scanning measures to protect their trade
flows, but the equipment utilized to perform these inspections varies in standards and
specifications. Last portion of section 1701 (b), the subsection (9) states:
‘‘(9) INTERNATIONAL TRADE AND OTHER OBLIGATIONS.—In
carrying out this subsection, the Secretary shall consult with
appropriate Federal departments and agencies and private
sector stakeholders, and ensure that actions under this section
do not violate international trade obligations, and are consistent
with the World Customs Organization framework, or other
international obligations of the United States.
…’’.
24
At the end of section 1701 (b), possible overlaps with other initiatives like the SAFE
Framework of Standards created by the World Customs Organization (WCO) are
contemplated. In this aspect, the third core element of the SAFE Framework Standards by
WCO, establishes that:
“At the reasonable request of the receiving nation, based upon a comparable risk targeting
methodology, the sending nation's Customs administration will perform an outbound inspection of highrisk containers and cargo, preferably using non-intrusive detection equipment such as large-scale X-ray
machines and radiation detectors” (WCO, 2007).
This core element could diverge from the 100% scanning, because under the SAFE
Framework, the outbound container is inspected by request of the receiving port
authority. Also stated in the third core element, the inspection performed by local
authorities by request of the receiving port should preferably, but not compulsory, be
done by Non-Intrusive detection equipment (NII) and radiation detectors.
2.5
Challenges and questions
Many organizations, governments, shippers and even US government dependencies have
pointed out the possible problems, challenges and questions that have come up derived
from the creation of the 100% container scanning legislation. The legislation is full of
ambiguities and is far away from being cleared to the shipping community. In the
following subsections concerns and common questions that have been tossed by the
stakeholders are mentioned.
SFI program ignored
Created by the SAFE Port Act of 2006, the Security Freight Initiative (SFI) was designed
to obtain experience from the 100% container scanning at selected ports. Then why the
SFI program was ignored by Congress? Once the pilot programs were fully deployed, the
SAFE Port Act required the approval by the DHS before continuing implementing full
scanning in other ports, but why was this regulation underestimated? Due to the
legislation approval before the SFI pilot programs evaluation, the objective of its creation
lacks a lot of sense. It is possible that even when the SFI program was ignored, many of
the actual concerns regarding the legislation will be answered with the lessons obtained
from these pilot programs. The experience resulted from current ports under SFI program
will be a tool to evaluate the objectivity of the full scale scanning.
Entity performing the scanning
Legislation does not establish the entity that will perform the scanning prior to departure
to the United States ports. Would the foreign customs authorities perform the scanning
before the container is loaded into the ship? Or would US customs authorities inspect the
out bounding containers at foreign ports similar to the CSI program? The legislation is
not clear, even private entities are being considered as a possible entity that might execute
the scanning. In the scenario that any external entities perform the scanning, would the
US government trust these parties in order to transmit security data and perform
inspections that might result in a terrorist attack in case of failure? As mentioned before
25
the legislation is too ambiguous in this matter, further explanation will be needed in order
to avoid overlap of jurisdictions.
Purchase and maintenance of the technology
Section 1701 (b) fails to define which entity will buy and keep up the equipment to
perform the scanning. To this respect, there is a lot of speculation by the stakeholders but
not knowing who would burden the costs of the legislation, would it be the US
government? Would it be the foreign governments through their customs agencies?
Would it be the foreign ports authorities? Or maybe would it be the terminal companies?
The answer is not stated in the legislation, but if we consider the SFI program as an
example, the US government assumed all the costs in the first phase of the pilot
programs. Would the United States Government do the same in the rest of the ports that
ships directly to the United States? It seems less likely to happen due to the fact that
around 600 ports bounds direct containers to the United States and the amount of money
that this investment might represent would be out of the DHS budget.
Cooperation of other governments
Others main concern about the 100% container scanning legislation is the failure to get
cooperation from other foreign governments. This implementation has been imposed
unilaterally by the US government and no arguments of foreign governments were taken
into consideration. It is strange that although the US government is conscious about the
importance of the opinion and collaboration of the stakeholders, like stated in other
security programs and legislations, this time they were wiped out. If the US government
and its dependencies are aware of the importance of considering all stakeholders of the
supply chain, why was this decision to scan on a full scale all containers bound to the
United States, so arbitrary? May be that is why major shipping organizations and foreign
governments are opposing the 100% container scanning legislation as a feasible measure
to enhance security of supply chain.
Reciprocity
If the goal of the 100% scanning is to enhance the security of supply chain, what would
be the impact if foreign governments demand the same level of security to their ports?
This term of reciprocity has not being considered by the US government, a 100%
container scanning in all the exports from the United States would be harmful to the rest
of the countries where not only economic limitations are present, but also the
infrastructure constraint at ports too. If the US government requests a full scanning on the
inbounds to the United States, but do not offer the same response to its commercial
partners, this could be considered as a non-tariff barrier. In this regard, if the US is
unlikely to offer 100% container scanning on its exports, but demanding to its imports,
this situation might lead the United States to several lawful implications. The US
commercial partners could start a legal trail through the World Trade Organization
(WTO).
Data generated
The legislation requires that all containers bound to the United States be scanned by nonintrusive imaging and radiation equipment at foreign ports. The legislation fails to define
what will be done with the data generated from the scanning process. Will this
information be sent to the US government for further analysis? Will this gathered
26
information be sent for analysis? Or will it be analyzed by US officers at foreign ports?
Another important aspect about the data generated is to determine its ownership. If the
scanning process is performed by a third party, for example, a foreign port authority or a
terminal operator, who will be the data owner? Where will it be stored and by whom?
Would the data generated be considered as of exclusive use of the US Government? Or
which others entities would have access to this information?
Technology utilized
The legislation is also vague in order to define the requirements of the equipment utilized
in the scanning process. In Section 1701 (b) subsection (8) of the H.R.1 states that the
Secretary (DHS) is going to define the scanning technology standards that should be used
at foreign ports to perform the scanning inspection. If the US government tries to impose
specific technology standards, this might affect interests of foreign governments. Many
governments have already acquired scanning equipments to conduct inspections, but all
these equipments vary in specifications and capacities. Several of these equipments are
able to deploy a Non-Intrusive inspection to a container, but would not be able to identify
a hidden bomb or a nuclear material concealed inside the container. If the US government
keeps enforcing foreign governments to acquire specific equipment with technological
standards demanded by the DHS, it could be violating the principle of sovereignty.
Another important technology issue is the healthy aspect of truck drivers, which some of
them are skeptical to drive through NII equipments due the secondary effects.
Human Resources
Regarding the process of the information generated, how much personnel would be
required in order to conduct the analysis of the data? Currently, the information gathered
by the SFI program is sent to the National Targeting Center in the United States where is
reviewed by a 40 person staff (Carluer [2008, pp. 148]). If the legislations is
implemented, the figure would have to increase enormously in order to process all the
information generated through 600 foreign ports. Also, the service to analyze the data
generated should be offered every day 24 hours a day, due the different time zones of
foreign ports. Another important aspect to consider is the scenario that during the
scanning process at foreign ports a high risk container is detected, how should the
personnel react in this kind of situation? Would the personnel conducting scan
inspections receive training in order to know what to do in these cases?
Transshipments
In the pilot programs performed in the first phase of SFI, transshipments arriving from
other ports have not been considered. In this matter, the legislation only establishes that
the container must be scanned at foreign port before is loaded into a vessel. To this
respect there could be two explainable scenarios. The first scenario is that no
transshipments will be allowed, thus all the scans should be performed at the originating
port. For example, this situation might result similar as when a passenger takes an
airplane heading to a specific destination but could end in a hub airport before arriving to
its final destination. In this case the passenger only passes through a scanning point at the
origin airport not at every stop during its journey. The second scenario, the transshipments
are allowed but this process, as mentioned before, has not been proven. Many experts
consider the transshipments as a serious issue of the implementation. (Carluer [2008, pp.
148]).
27
Burden of the legislation
Considering that the legislation is implemented and the foreign governments must carry
the burden of the implementation, it seems like the US government is transferring their
security responsibilities to its commercial partners. First of all and mentioned before, the
US government through its legislation can not force other governments to act as they
want, at least not theoretically speaking. Then transfer the responsibility and the costs
implied in the implementation of the 100% container scanning legislation, violating the
rule of nation sovereignty, looks too convenient for the US government. Another
important aspect is the difference in capacities between foreign ports bounding containers
to the United States might result in different size to burden the legislation costs. Derived
from economies of scale, bigger ports like Hong Kong could bear the costs incurred,
while smaller ports like Tanzania could not put up with costs due to higher impact per
container shipped.
2.6
U.S. Commercial Partners
In the previous section we discuss the origin and implications of the 100% Container
Scanning Legislation. In the last part of the chapter we are presenting some statistics
about the commercial partners of the United States which will be the affected ports
derived from the implementation of the Legislation. The total container market estimated
for the 2007 represents more than 18 million of TEU per year. From the top ten exporters
by waterborne containers to the United States, all of them with the exceptions of
Germany, Italy and Brazil, are from the Asian continent. The number one partner by far is
China, representing 47% if the total container market, without including Hong Kong
which is the third commercial partner by waterborne container. Only the top ten exporters
represent more than 72% of the total U.S.-bound container market. The top twenty five
represent 89% of the total container market. And the top 40 country exporters to the
United States represent 95% of the total U.S. bound container market. It is obvious that
the legislation will impact on a higher scale to the Asiatic countries which have more
participation of the U.S. container market. Figure 2.1 presents the top 25 trading partners
by waterborne container.
28
Figure 2.1
U.S. Waterborne Container Import by Trading partner 2007 (PIERS, [2008])
11%
1%
1%
1%1%
1%1%
1%1%
1%
1%
1%
1%
1%
2%
47%
2%
2%
2%
2%
2%
3%
3%
3%
3%
4%
China
Japan
Hong Kong
South Korea
Taiwan
Germany
Italy
Brazil
Thailand
Vietnam
India
Indonesia
Netherlands
Malaysia
Belgium
United Kingdom
Chile
Costa Rica
France
Honduras
Guatemala
Spain
Philippines
Pakistan
Turkey
Rest of the World
Source: PIERS (2008).
2.7
Chapter Conclusion
Since the terrorist attacks of 9/11 all the people have experienced the consequences of
those terrorist attacks in some way; a good example is shown at the airports. Before the
attacks the check-in process was relatively expedite, nowadays many security measures
are followed before the airplane takes off, representing long waiting lines. These security
measures have not only been applied to the airline industry, also many changes have been
made in other transport systems. The shipping sector is not the exception, many efforts
and changes have been made by the stakeholders in order to cooperate with the
improvement of security standards. The shipping community is aware about the
implications of further attacks might affect their assets and is willing to participate in
conjunction with government dependencies to preserve the integrity of the supply chain.
Then, why the approval of the 100% container scanning legislation was not well received
by the shipping industry? There is not a single answer to this question; first the process of
the legislation has certain discrepancies. The implementation of a full container scanning
in the shipping industry was not a recommendation of the 9/11 Commission to protect the
wellbeing of the United States. Second, the possibility to evaluate the feasibility to scan
containers on full scale in a shipping port should be derived from the resulted experiences
29
of the pilot programs implemented by the US government. These pilots were executed
under the SFI program at seven different ports from different capacities, but the
legislation was approved before the program ended. The lessons obtained from the SFI
program were ignored before deciding to request the same measures in the rest of the
ports around the world. Finally, another huge factor contributing to the unpopularity of
the law was the lack of support by the shipping industry, which also was overlooked
before the implementation of such legislation.
Even though the controversy and skepticism by some sectors of the shipping industry
about whether or not the legislation will be implemented on 2012, the fact is that the
100% Container Scanning Legislation has been enacted and until other decision is made
the legislation will be deployed in 2012. However all the unanswered questions and
challenges that the law might face in the upcoming years, the objective of this study is not
to determine the feasibility or benefits of the legislation, but to analyze the congestion and
economical impacts of the waiting lines derived from the implementation of the
Legislation. In order to reach the objectives traced in our study, it is necessary to
understand the process of the scanning itself. According to this, chapter 3 describes the
technical aspects of the 100% container scanning legislation.
30
3 Technical aspects of the 100% container
scanning legislation
3.1
Introduction
In this chapter technical aspects will be discussed of 100% container scanning legislation:
the process of container scanning, equipment applied to perform the inspections and
health issues that must be addressed. The process described in this chapter is based on the
Integrated Inspection System utilized in the pilot programs of Secure Freight Initiative
(SFI) and was designed specifically for a volume of 100% container scanning. The
current process implemented on SFI pilot programs is a good example of further
procedures that U.S. government would demand on July 2012. As mentioned on chapter
2, the process of container scanning was not specified in the legislation and the shipping
community is expecting the Department of Homeland Security (DHS) to clarify this
issue. After explaining the SFI container process will be described the current equipment
utilized in the scanning process and the available suppliers in the market offering these
types of products. The equipment described is utilized at different stages of the Integrated
Inspection Scanning process. Each device employed pursues different objectives with
specific characteristics in order to guarantee a secure inspection mechanism. The
development of future technologies is an important aspect to consider that might increase
significantly the capacity of container scanning. These technological improvements could
solve current challenges expected at terminal operations. One final aspect covered in this
chapter is the healthy concerns of the equipment utilized in the inspection process.
Customs officers and truck drivers are exposed to radiation emissions during the
inspection process, thus safety standards must be applied in order to avoid human risks.
3.2
Process of Integrated Inspection System
As part of SFI, the Integrated Scanning System (ISS) project is responsible to guarantee a
100% container scanning process to all the containers bounding to the United States at
selected ports. To assure a 100% scanning volume the U.S. Customs Border and
Protection (CBP) utilizes an Integrated Inspection Scanning system. This system involves
methods and mechanisms combining passive radiation detection and Non-Intrusive
Imaging (NII) equipment. The process is performed by the U.S. government in
conjunction with the host country. The DHS through the CBP is in charge of the NonIntrusive Inspection (NII) and the Department of Energy (DOE) is in charge to detect any
source of radiological material. Next will be briefly described the complete process of the
Integrated Scanning System (ISS) performed at the SFI port of Southampton (Carluer
[2008]).
31
1. The truck arrives with the container through the main gate of the terminal. An
administrative procedure is performed in order to check the arrival of the
container;
2. Once the container checks its entrance to the terminal drives through the
Radiation Panel Monitors (RPM). The RPM are passive equipment, which means
that no radiation is emitted from them. The alarm only activates when radiation is
detected while the container passes through the panels. The radiological
inspection is performed by DOE (known as Megaports Initiative described in
chapter 2) and is applied to all the containers out-bounding from the foreign port,
no matter their final destination. If the container is radiation-free and bounds
outside the United States, goes directly to the container yard. If the alarm detects
the presence of radiological material, a second analysis is performed. This second
inspection focus on detect the source of radiation, either through a 3D graphic by
Advanced Spectroscopic Portal (ASP) or by manual inspection. If the alarm is
adjudicated the container continues the process, if the problem is not adjudicated
the threat is confirmed and all the area must be evacuated;
3. If the container destiny is outside of the United States the container goes to the
terminal yard, but if the container is US-bound a non-intrusive inspection (NII) is
carried out (scanning). An X-ray or Gamma ray image is taken and analyzed by
CBP and local officials at site. If everything is normal the container goes to the
terminal yard. If after the analysis the CBP and local officials have doubts about
the content of the container a Second Inspection is performed with the similar
procedure as the alarm inspection from RPM. In this process the truck driver
whether or not step down from the truck to perform the scanning (Depending in
the type of equipment utilized to obtain the NII image);
4. The information is encrypted and sent it immediately to the National Target
Center in Virginia, U.S.A. to be analyzed. The data from radiation and the nonintrusive image is associated through the optical character recognition (OCR)
which links the data with the container ID (Huizenga [2008]).
Important note: the Integrated Inspection Scanning system consists in radiation detection
and non-intrusive imaging. The process may differ in between SFI ports according the
characteristics of infrastructure of each port. Second analysis inspections could take place
after the RPM or after NII, according officer’s analysis.
In Figure 3.1 shows on detail the Integrated Inspection system of SFI port developed at
Southampton, United Kingdom.
32
Figure 3.1
Integrated Scanning System Process at SFI port (Southampton Port, UK)
33
3.3
Integrated Inspection System Equipment
In this section current equipment utilized in the process of the Integrated Inspection
System at SFI ports will be analyzed. Even though the DHS has not established the
technological parameters of the equipments that will be requested in the 100% scanning
law, the equipment utilized in the SFI pilot programs is a good example of the available
technology in the market. The analysis of the equipment will be conducted through each
stage of the process describing the performance, types available, principal suppliers and
other relevant aspects of the equipment. In the last part of the section, will be briefly
explain the innovative equipments that could lower the negative challenges of the
legislation. The future equipment must be capable to perform at faster process of
scanning and provide better quality images to facilitate the inspections of the officers.
These new technologies have being evaluated by the DHS and could be applied in a
relative short time. The technological enhancements represent a relief for the implications
derived from the legislation.
Radiation Portal Monitors
The Radiation Portal Monitors (RPM) is a mechanism utilized in the Megaports Initiative
and is performed by the National Nuclear Security (NNSA) a branch of the Department
of Energy. The principal objective of the Megaports Initiative is the detection, prevention
and proliferation of nuclear materials and weapons of mass destruction (WMD) through
the supply chain. The Megaports Initiative is compromised to scan every container no
matter if is import or export, precedence or final destination in every port participating.
The RPM consist in two pillars containing two organic plastic scintillator detectors. These
pillars could be separated from 3 to 10 meters, depending on sensitivity requirements.
These portals detect the presence of gamma and neutron radiation originated from
radiological materials utilizing plastic Polyvinyl Toleune (PVT). An alarm is activated by
a red light in order to advise the detection or presence of radiological material. The
naturally occurring radioactive material (NORM) or Innocent alarms are inoffensive
materials detected by the RPM such as Kitty litter, Pineapples, Avocados, fertilizers and
other products containing potassium. The convenient speed to pass through the RMP’s is
8 Km/hr. Among the current suppliers of RPM in the market are Smiths Detection,
Nuctech, SAIC, Rapiscan and TSA Systems. These suppliers have a full line of different
models of RPM with certain distinctions between them. In Table 3.1 is described the
technical specifications of an example model from TSA Systems (TSA [2008]).
34
Table 3.1
Technical Specifications RPM
Specifications
Description
Supplier
TSA Systems
Model
VM250AG
Gamma Sensitivity
1,000g of ^235U (HEU) or 10g of ^239 Pu
Neutron Sensitivity
Less than 200g of ^239 Pu in a shielded container
Passage speed
5 mph or 8 Km/hr
Detectors
Two, 30"h x 6"w x 1.5"d (76 x 15 x 4cm) organic plastic silenciators detectors per pillar
False Alarm Rate
Less than 1 per 1,000 passages
Alarm Indication
Alarms are indicated by a red strobe light mounted on the master pillar.
Display
Alphanumeric LCD, 4 lines, 16 characters
Communications
Equipped with RS-232 and Ethernet communications capability
Flash memory (256 KB) is used to storage average hourly background data and alarm
Data Storage
data
Power Requirement
90-250 Vac, 47 – 63 Hz, less than 100 VA
Dimensions
120"h x 10"w x 10"d per pillar
Weight
300 lbs (136 Kg) per pillar
Environmental
-30 to 122 F (-34 to 50 C) outdoor most climates
Source: SAIC.
Figure 3.2
Radiation Portal Monitor (RPM) Inspection
Radiation Portal Monitor (RPM)
Radiator Portal Monitor Profile (CBP [2008]).
No-Intrusive Inspection Equipment (NII)
The Non Intrusive Inspection equipment provides an image of the contents without the
need to open the container. This image is produced by X-ray systems from energy
spectrum normally by 6 MeV or higher; or by Gamma Ray systems through Cobalt 60
orCesium-137. It is important to mention that the NII systems does not emits any alarm
derived from Radiological or Nuclear materials, is just an inside image of the container.
Some equipment differs in the process of scanning, while a group of equipments require
the truck driver to stay inside the cabin during the process, other systems demand the
truck driver to exit the unit. Health issues related with the radiation emitted from the NII
will be discussed in the following section. Among the principal suppliers of scanning
devices in the market are Smiths Detection based in Europe, Nuctech from china and the
U.S. American firms SAIC and Rapiscan (Carluer [2008 p.58]). Each supplier has
positioned differently in the market, while Smiths Detection has a better image but
35
expensive prices, Nuctech offer the best prices with a service program included. On the
other hand, the American companies SAIC and Rapiscan, have positioned in between the
European and Chinese companies. In Figure 3.3 shows the three types of NII scanners in
the market Fixed, Relocatable and Mobile.
Figure 3.3
Types of Non-Intrusive Image Scanners (NII)
Nuctech Fixed NII
Nuctech Relocatable NII
Nuctech Mobile NII
The latest NII models are designed based on the idea of a multi-task mechanism (Carluer
[2008]) which avoids unloading containers as far as possible throughout the supply chain.
This allows and letting the truck driver stay at the cabin during the scanning process until
is completed for expedite service. In Table 3.2 is described the technical specifications of
an example NII model from SAIC (SAIC [2008]).
Table 3.2
Technical Specifications Non-Intrusive Inspection systems (NII)
Specifications
Description
Supplier
SAIC
Model
VACIS P-7500
System
X-Ray
Energy Spectrum
7.5 MeV
Type
Fixed - Pass-Through
Steel penetration
300 mm or 11.8 inches
Resolution
12.5 mm or 0.5 Inches
Throughput
150 containers per hour in Free Flow Operation
Passage scan speed
8 mph or 13 Km/hr
Radiation dose
2.6µSv (257) per scan
Dimensions
6 mts h x 8 mts w x 3 mts d
Contrast sensitivity
3% or better
Imaging Software
Microsoft's Windows
Power
230 +- 10% VAC, 50/60 Hz, 30 KVA per lane
Environmental
-30 C to +50 C (22 F to 122 F)
Source: SAIC.
In the Figure 3.4 are shown examples of the two types of scanners available in the market
Gamma Ray and X-ray. The NII equipment based on Gamma Ray are less expensive, but
have less definition compared with the X-ray models. Other advantages of Gamma ray
models are easy to maintain compared with X-ray models and provide faster scan speed.
36
The right choice of equipment will depend on the objectives and necessities of each
authority and with the future specifications adopted by DHS.
Figure 3.4
Gamma ray and X-ray Images from NII equipment
Gamma Ray NII Image (SAIC [2008])
X-Ray NI Image (CBP [2006])
Second Inspection Equipment
The second inspection is done upon the detection of presence from the RPM alarm. In
this part of the process the container is separated in a specific area to perform a secondary
inspection. The objective is to isolate the source of radiation detected by the RPM portals.
The equipment utilized by the inspectors is the hand-held Radiation Isotope Identification
Device (RIID) or an Advanced Spectroscopic Portal (ASP) device to identify the
radiation isotope. The RIID and ASP are devices capable to differentiate between a
NORM alarm or a nuclear materials and weapons of mass destruction (CBP [2008b]).
The RIID devices allow the inspectors to locate radioactive sources, measure the dose or
hazard level and analyze the data for risk assessment (SAIC [2008c]). Besides the big
four companies (Smiths Detection, Nuctech, SAIC and Rapiscan) in the scanning devices
also other smaller companies produce RIID equipment. In Figure 3.5 are shown an
example of RIID equipment.
Figure 3.5
Second Inspection equipment Radiation Isotope Identification Device (RIID)
RIID light-hand equipment (SAIC [2008a])
RIID Spectrograph (CBP)
In the case of ASP systems companies like Raytheon, Ortec, and others provides this type
of technology. The ASP systems allow officers to detect illicit materials in a larger scale.
Is a passive system with high purity Germanium as the gamma ray detector and reports
real time through the software any alarm of radiological material (Ortec [2008]). These
equipments differentiate NORM alarms from truly illicit radioactive materials. This
37
distinction provides a huge advantage as a result of lower false alarms with faster traffic
flows. In Figure 3.6 are shown examples of ASP devices.
Figure 3.6
Second Inspection equipment, Advanced Spectroscopic Portal (ASP)
ASP spectrograph (CBP [2006])
ASP portal monitor
Innovative equipment
The development of new technology could ease the congestion concerns of some
shipping stakeholders. The efforts to enhance the capacity of the equipment will help to
address current challenges presented at SFI pilot programs. For example, the latest
equipment created by the Chinese company Nuctech for the port of Ras AL Khaimah in
Dubai, has the capacity to scan up to 200 containers per hour. Also a complement mobile
system bought by the Emirates port is capable to scan up to 40 containers per hour
(Carluer [2008 p.71]). Other technological progresses have been applied to latest
equipments developed by the major scanning providers (Smiths Detection, Nuctech,
SAIC and Rapiscan). Among these innovations are the automatic identification of suspect
objects; identification of suspicious materials “tagged” with different colors; adoption to
reproduce colors to facilitate the analysis work; solutions to optimize contrasts; light
intensification to penetrate specific spots in the container; and automation and
technological support to capacitate operators in order to take better decisions (Carluer
[2008 p.65]).
The private sector has been applying newer technology to current equipment utilized at
terminals in order to enhance productivity at ports. The Customs Border and Protection
(CBP) and the Domestic Nuclear Detection Office (DNDO) have been evaluating these
innovations to determine its feasibility. For example, one of the biggest challenges
presented in the pilot programs are the transshipments at ports. To solve this problem, the
private sector has developed a new crane-base radiation detection system mounted in the
spreader-bar. This innovation already has been proved in the port of Oakland, California
and in a near future also will be tested in the port of Tacoma, Washington (Huizenga
[2008 p.6]). Similar applications have been made to straddle carriers in the port of
Bahamas, being the first moving platform tested in a port. The port of Salalha in Oman
has been evaluating a new radiation detection mobile platform system that was developed
to improve the scanning process of transshipments (Huizenga [2008 p.5]). All these
innovations are in process of evaluation by the authorities and could be applied in a short
38
time. A concept of a straddle carrier Portal Monitor (Mechanical Solutions Group [2008])
and Spreader Bar Detection System (Veritainer [2008]) are shown in Figure 3.7:
Figure 3.7
Innovative equipment of ISS
Conceptual Straddle Carrier
3.4
Spreader-Bar Detection System
Health issues related to NII container scanning
The workers participating in the development or application of security programs
involving Non-intrusive imaging systems are exposed to radiation emissions that could be
harmful in higher amounts. The US government and workers Unions have ensured to
maintain these workers under maximum standard levels of radiation. In 1997 the Customs
and Border Protection established the standards for employees working with gamma and
X-ray systems. The limits were set equally as the general public or US non-radiation
workers, with levels of 100 millirem (mrem) in a year, differently from the radiation
worker levels of 2,000 millirem (mrem) a year. In order to comply this regulation, general
workers like CBP officers and truck drivers must stay under 50 microrem (µrem) by hour
(1 mrem equals 1000 µrem). This means that officers and truck drivers can not be
exposed more than 2000 hours a year in facilities were NII equipment are used (DHS
[2007]). In this sense the World Shipping Council has addressed the necessity to establish
health standards for the use of this type of equipments (WSC [2007]). Even though in the
United States have set their maximum requirements of radiation dosages, in other
countries might have different levels of acceptance. This situation represents a big issue
in some countries where truck drivers and unions are not willing to pass through x-ray or
gamma equipments. The Suppliers of the NII equipment are aware of healthy issues
concerning the performance of their products and have tried to keep the radiation
emissions at low levels. Although, the necessity by the authority to obtain equipment with
higher capacities and powerful resolution images, has encourage the designers to utilize
higher levels of radiation.
3.5
Chapter Conclusion
The technical standards that will be utilized for the 100% container legislation is an
important issue that must be address in a short time by the DHS. Although the
39
government has not specified the process and the requirements of the equipment that will
be utilized, the experiences obtained at SFI ports are good examples of future processes
demanded by the U.S. authority. The improvement of current technologies and the
development of new ones will be crucial in order to implement the legislation on July
2012. Theoretically, available technologies already perform more than 150 scans per hour
and only a few ports in the world have these export volumes to US-bound. The
development of new technologies could solve current challenges, like transshipments,
that already represent a threat for the application of the law. The private sector is
designing new applications of current technologies to improve the performance and
efficiency at ports. These applications are looking to avoid chokepoints that might disrupt
the flow of goods at terminals during transshipments. Even though if the operational and
technical aspects could be solved in the upcoming years, the cost factor incurred to apply
these technologies represent the biggest challenge of the legislation. The failure to
address health issues regarding the utilization of radiological equipment like the NII is
another important issue that has not been considered by the U.S. government. In the next
chapter will be analyzed the current literature regarding the economical and operational
impacts of the 100% container scanning legislation.
40
4 Review of literature on waiting lines and
economic costs
4.1
Introduction
In this chapter will be analyzed the scientific studies, government reports and declarations
related with the implications of the 100% container scanning law. Even though there are
not many formal economic studies available, the existing ones have valuable information
to assess the economic and congestion impacts of the legislation. The only existing
academic material available was written by Frederic Carluer from the University of
LeHavre. The study called “Global Logistic Chain Security: Economic Impacts of the US
100% Container Scanning Law” (Carluer [2008]) was commissioned by the World
Customs Organization (WCO) and released on June 2008. Another important literature
analyzed in this chapter is the first report of the Security Freight Initiative (SFI), released
on June 12, 2008 by the Department of Homeland Security (DHS) through the U.S.
Customs and Border Protection (CBP [2008a]). Also another important study to consider
is the work of Martinosi, S.E, Ortiz, D.S. and Willis H.H in the collaboration with
“Chapter 12: Evaluating the viability of 100 percent container inspection at America’s
ports”, of the book The Economic Impacts of Terrorist Attacks by Harry Richardson,
Peter Gordon and James Moore II (Martinosi, Ortiz & Willis [2006]). In this study the
coauthors attempt to measure the 100% container scanning impact at US ports, opposite
to our objective but with remarkable material to assess the problem. These three studies
will be the pillars of this chapter divided in one section each. Every section will be
complemented with other significant studies and valuable comments expressed by
stakeholders regarding the 100% scanning Law.
4.2
Academic research
Michael Danet, Secretary General of the World Customs Organization assigned the
University of LeHavre to conduct a study to measure the impact derived from the
application of the 100% container scanning law. The Professor Frederic Carluer assumed
the responsibility to perform the study requested by WCO with the help of his assistants
Yann Alix and Oliver Joly. This study is very relevant because is the only research
available so far, dedicated specifically to measure the economic impact of the 100%
container scanning law. The study is divided in three sections, the first part analyzes the
Macro-economic aspects of the legislation, the second part analyses the Micro-economic
aspects, and the last part of the study creates a few scenarios in order to measure the
possible course that the 100% container scanning law might take. In our review will focus
only on section two Micro-Economic analysis which is the section concerning to our
study. The second part of the Micro-economic analysis, the author conducts a survey of
41
60 questions to port authorities and port Customs services in order to obtain data from
these stakeholders. The selected ports were: Le Havre, France; Rotterdam, The
Netherlands; Rio de Janeiro, Brazil; Dakar, Senegal; Dubai, UAE; Montevideo, Uruguay;
Casablanca, Morocco; Hong Kong, HK; Singapore, SG; Abidjan, Ivory Coast; and
Finally the Euro-tunnel in Coquelles, French side. Only the most relevant questions were
selected to be analyzed, according cost structure and operation delays (results from the
French Eurotunnel, were not included):
4.2.1
Questions regarding operations and waiting lines
14- What is the average scanning time for a container?
Table 4.1
Average container scanning time (Questionnaire, Carluer [2008])
Ports Selected
Average scanning time for a container
Casablanca
9-11 minutes (strictly speaking there are no transfer operations in Casablanca)
Dakar
3 minutes (excluding transfer operations)
Le Havre
Under 10 minutes (excluding transfer operations)
Montevideo
Under 10 minutes (excluding transfer operations)
Rio de Janeiro
2 minutes (and 15 to analyze the image)
Rotterdam
10 minutes (40 minutes including transfer operations)
Dubai-Jebel Ali
3 to 5 minutes (excluding transfer operations)
Hong Kong
Not specified
Singapore
No reply
Around 10 minutes in all including 1 minute for the scan itself and 6 for direct image
Abidjan
analysis
The scanning times vary between the ports surveyed, spending two minutes minimum per
scanning up to eleven minutes maximum. As well only some of the ports specified the
time spend analyzing each image from six to fifteen minutes range. The questionnaire
was not very clear in order to request the time spent in the analysis of each image. This
lack of information is important to measure because the service time of scanning not only
depends on the technical equipment capacity also must be accounted the performance of
the human factor. As complement of this question, In question 16 asked about the transfer
time from the terminal entrance to the scanner location, six ports did not reply the
question and the rest of the ports ranged from two to fifteen minutes.
4.2.2
Questions regarding Cost factor
42-What is your estimated total cost of scanning one container?
The only ports who answered were Casablanca, Dakar, Le Havre, Montevideo, Rio de
Janeiro and Abidjan. The amount per container scanned raged from EUR 5 to EUR133.
The difference between these costs is enormous and would generate an erroneous
calculation if a cost analysis is conducted with the average of these figures. With these
42
unit costs per scan, would be difficult analyze the total impact of the legislation. In order
to conduct an appropriate cost analysis and derived of multiple differences between ports,
specific adaptations in the cost variables must be done individually. To finish with the
related questions regarding cost, last question is analyzed.
Continuing with the study of Professor Carluer, he dedicates a sub section to measure by
itself the Cost analysis of the 100% container scanning law. He starts with the following
statement:
“As a preliminary initial cost analysis, our interviews with experts (manufacturers and administrators)
provide an approximate quantification of the unit cost per scanned container. Obviously, depending on
the quality, performance and use of the material, and the size of the specific port site (in terms of traffic
in particular), the overall cost of the scanned container will be different” (Carluer [2008 p.158]).
In this sense, the author realizes the difficulty to conduct a cost analysis derived from the
multiple variables in order to determine a general economic impact of the legislation to
any port. The pioneering calculation that he is proposing is full of assumptions to estimate
the average cost per container scanned. To conduct its analysis the author took as
example two different scanners: scanner 1, the Pass-Through 6 Mev and scanner 2, the
relocatable fixed scanner 6 Mev Double Tunnel. The cost of scanner 1 is estimated at
USD 1.6 Million and Scanner 2 is estimated at USD 2 million. The total indirect costs
include the infrastructure (site and dedicated building with all the necessary facilities), the
work shifts depending on the number of containers scanned, 7 operation days a week,
human resources needed for the scanner management (system operators, imaging
operators, IT manager, marshals and other administrative staff). The customs duties at the
scanner installation are assume to be zero, while vat is payable at 20% and the costs of
the land made available by the port are no taken into account (Carluer [2008 p.160]). The
calculations are shown in the Table 4.2 and represented graphically in Figure 4.1
(approximate estimate of the direct unit cost of a scanned TEU container as a function of
overall capacity).
Table 4.2
Unit Cost of scanned TEU container in USD by type of scanner (Questionnaire, Carluer [2008])
Number of Containers scanned
per year
Scanner 2: relocatable fixed
Scanner 1: Pass-Through 6 Mev
scanner 6 Mev Double Tunnel
5000
440
400
35000
63
57
75000
31
30
105000
21
27
140000
20
21
225000
12
52
420000
10
63
43
Figure 4.1
Estimate of direct unit cost of a scanned TEU container as function of overall capacity (Carluer, [2008])
The calculations made by Professor Carluer show the volume capacities of two scanning
models compared with the costs incurred by each machine. While scanner 1 “PassThrough 6 Mev” becomes more cost-efficient as scanning volumes rise, the Scanner 2
“Relocatable fixed scanner 6 Mev Double Tunnel” reaches its efficiency at 140,000
scanning volume, after that point an additional machine is needed to support higher
demand. The costs incurred by scanner 1 goes from USD 440 at 5,000 scanning volume
to USD 10 at 420,000. For scanner 2, the range goes from 400 at 5,000 scanning volume
to USD 21 at 140,000 scanning volume, when exceeds this capacity its unit costs rise
again up to USD 63 per container at a volume of 420,000. These calculations give a close
perspective of the reality, but with its limitations like the author comment, derived from
the differences in costs between ports. Instead of present the final figures represented in a
table and graph, it would be better to have a detailed explanation of the incurred costs.
The author only dedicates four and a half pages of his entire book to explain the costanalysis conducted by him. One final and remarkable annotation he mentions at the end
of his cost-analysis section was a foreseen calculation of the required personnel that the
US government will be required to analyze all the scanning images produced by all
foreign ports if the law is implemented.
“Assuming that an image is 10 megabytes and that an operator can interpret 10 images per hour (or
1,600 per year based (its easy to imagine the colossal database required). At over 18 million US-bound
containers today (and potentially 30 million in 2012), this would amount to employing 1,125 staff at the
Washington nerve centre (1,875 in 2012)” (Carluer [2008 p.162]).
44
Currently there is only 50 person staff analyzing the images produced from the CSI and
SFI programs.
The author concludes his book with a remarkable statement:
“It goes without saying that operators will therefore have the opportunity to pass on the equivalent of this
additional cost to the final consumer, which will slow down international trade dynamics and
consequently world growth (but to that extent and with what gain?). The question which still cannot be
answered at present is the indirect cost of applying the 100% scanning law and, in parallel, the direct
and indirect cost of a major terrorist act on the world logistics chain and more particularly the United
States”. (Carluer [2008 p.181]).
4.3
Report to Congress on Security Freight Initiative (SFI) pilot programs
This section is focused to analyze the results of the first phase of the Security Freight
Initiative. Last June 12, 2008 was held the hearing by the U.S. Senate Committee on
Commerce, Science and Transportation, focused to discuss lessons learned from the SFI
initial pilot programs as mandate by the SAFE Port Act 2006. In the examination lead by
Chairman Frank Lutenberg witness the representative of the U.S. Border Customs and
Protection, Jayson Ahren; David Huizenga, representative of the Department of Energy;
and Stephen Caldwell, from the Government Accountability Office. A final report of the
lessons learned of the pilot programs was presented to the Congress and each witness
pronounced their statement of the results from the SFI pilot programs. In the following
section will be discussed the general provisions, achievements and challenges detected by
the three Dependencies in the first phase of SFI program.
General provisions
As part of the statement of Customs and Border Protection (CBP) by Jayson Aharen, the
Deputy Commissioner expressed the confidence by the CBP to carry a protection security
system founded on a risk-base and layered enforcement approach. This security system
includes advance information collection, sophisticated technology, and with the
establishment of partnerships with the trade community and foreign countries (Aharen
[2008 p. 1]). In this sense, the Assistant Deputy Administrator for the Department of
Energy (DOE) also agrees on the same issue:
“The collective mission of the Defense Nuclear Nonproliferation (DNN) is to detect, prevent, and reverse
the proliferation of weapons of mass destruction. Our programs are structured in support of multiple
layers of defense against nuclear terrorism and state-sponsored nuclear proliferation”. (Huizenga
[2008]).
In short words, both dependencies base the security of the supply chain creating several
security programs from different scopes, involving several dependencies, mechanisms
and stakeholders from the shipping industry. Ahern continues
“While increased resources for programs such as SFI have enhanced our ability to address maritime
container security vulnerabilities, it is important also to recognize that 100 percent scanning DOES NOT
45
equal 100 percent security and that no single layer or tool in our risk-based approach should be
overemphasized at the expense of the others” (Ahern [2008]).
It is clear that Deputy Ahern considers the 100% scanning legislation as a demanding
resources mechanism and opposes with the layered approach policy, which spreads all its
resources among different security programs of the supply chain. The objective of the
risk-base approach is to address all the available resources among multiple security
programs throughout the supply chain. Assistant Deputy Administrator comments in this
respect:
“Overall, the concept of scanning US-Bound containers overseas has a proven to be viable in some
cases, but we continue to believe that a risk-based approach to deployment of these systems is the best
us of available resources” (Huizenga [2008 p. 2]).
Either way, both bureaucrats agree that 100% scanning law demands high resources to be
accomplished. Instead to destine all the resources into a single program could be more
beneficial to address those resources in other vulnerable areas of the supply chain.
Achievements of the SFI pilot programs
Not many benefits were addressed to the first phase of the Security Freight Initiative pilot
programs. One of the benefits reached in the SFI was the transmission of the generated
data by the scanning system.
“SFI has also demonstrated that scanning data in near-real time for review and analysis”.
(Ahern [2008 p. 6]).
This is one of the current concerns of the shipping community, what would happen with
the data generated from the container scanning and if these data would be able to be
analyzed by personnel in Washington? The answer is yes, it was possible in the pilot
programs, but if this program is implemented in the rest of the ports as the legislation
establishes, the amounts of images and information generated would reach skyrocket
levels as professor Carluer mentioned (Carluer [2008 p.162]). This data increase would
demand hundreds of analysts to review all the information generated by the scans from
allover the world. Another achievement mentioned by the DOE witness was the
partnership with the host governments in order to implement the pilot programs. Without
their support the implementation of the programs would have not been possible. David
Huizenga mentioned:
“I cannot underscore enough that SFI or Megaports Initiative implementation cannot be successful
without the partnership of the host nation, port authority, terminal operators, and other key stakeholder
at the port” (Huizenga [2008 p.7]).
The effectiveness of the technology has been relatively good with some problems but all
have been solved in short time. One important aspect to mention was the free flow of
traffic with minor queues through the pre-gate area. This is another major concern by the
shipping community, which considered that the flows of containers will be disrupted by
the new legislation. One final benefit expressed by the Commissioner Ahern was the
46
better efficiency flows that containers arriving from SFI experienced at US ports through
less examination then containers arriving from non-SFI ports.
Challenges
Several challenges were expressed by the three witnesses, some of them coincide to each
other. Commissioner Ahern stated:
“While we continue to learn important lessons in these initial pilot locations, CBP will focus future
scanning deployments on high-risk trade corridors that represent the greatest threats to the United
States” (Ahern [2008 pp.3-4]).
In this sense the Commissioner consider the 100% scanning implementation could be
feasible but only for high risk corridors, not to be implemented globally. Next are
presented only the most significant challenges according CBP (related to our study)
presented in the SFI pilot programs (Ahern [2008 p.4]):
•
Sustainability of the scanning equipment in extreme whether conditions and certain
port environments;
•
Varying costs of transferring the data back to the United States (National Targeting
Center) in real-time, etc.;
•
Re-configuring port layouts to accommodate the equipment without affecting port
efficiency;
•
Developing local response protocols for adjudicating alarms;
•
Addressing health and safety concerns of host governments and respective trucking
and labor unions;
•
Identifying who will incur the costs for operating and maintaining the scanning
equipment;
•
Addressing data privacy concerns in regards to the scanning data;
•
Concluding agreements with partnering nations and terminal operators to document
roles and responsibilities regarding issues such as: ownership, operation, and
maintenance of the equipment; sharing of information; and import duty and tax
considerations;
•
Discussing the potential requirements for reciprocal scanning of U.S. exports.
The Department of Energy (DOE) presented the following challenges (related with our
study) of the SFI pilot programs:
Transshipment
“First, while the operational Megaports and the SFI pilot ports have shown that gate traffic can be easily
capture by taking advantage of existing chokepoints into and out of a port, transshipped cargo continues
to present a significant challenge for both SFI and Megaports implementation. Because shorter dwell
times for containers, space constraints, availability of shipping data and the difficulty of identifying
chokepoints within the container terminals, capturing transshipments without seriously impacting port
operations requires new and creative solutions” (Huizenga [2008 p.5]).
Summarizing, the real challenge of the legislation will be the transshipments. In some
way the problems with the waiting lines at gate have been solved in the pilot programs,
47
but the problem of the transshipments represent a major threat for the 100% scanning
legislation.
Costs
“Even if technology is developed to effectively scan 100% of US-bound containers with both the
detection and the imaging systems without impacting port operations, it may not necessarily be a cost
effective risk management strategy to equip the 700+ ports that ship directly to the United States”
(Huizenga [2008 p.6]).
In this sense, the Assistant Deputy Administrator Huizenga made a remarkable comment
about the costs incurred in order to implement the legislation. Even though technological
improvements could be reached in near future, if the legislation is not cost-effective
feasible, will not be possible its implementation in 2012. Table 4.3 was obtained from the
Final Report of SFI pilots, shows the incurred costs by CBP and DOE in the
implementation in three SFI ports (CBP [2008]):
Table 4.3
DHS and DOE SFI Costs (Report to Congress on Integrated Scanning System, [2008])
DHS Cost Element
DOE Cost Element
Analytical Study
$ 200,000
Equipment
$ 5,046,757
Communications
$ 2,709,879
Installation
$ 15,365,581
Equipment
$ 10,155,000
Testing
$ 465,000
Hardware
$ 2,996,194
Maintenance
$ 550,000
Hardware (service license)
$ 82,132
Communications
$5,935,582
Port deployment support
$ 463,923
Program Office Support
$ 1,657,500
Software development
$ 10,080,884
Software license
$ 628,486
Software support
$ 140,535
Training
$ 231,502
Training
$ 1,913,000
Travel
$ 1,099,093
Travel
$ 106,687
DHS Total
$ 30,445,128
DOE Total
$ 29,382,607
Source: Report to Congress on Integrated Scanning System.
So far the SFI program in the three full scale ports and one limited scale port has scanned
a total of 172,980 containers. If a simple analysis is performed:
Unit cost per container scanned= $ 30,445,128 (DHS costs) + ($ 29,382,607 DOE)
Total container scanned 172,980
Unit cost per container SFI = USD $ 345 per container
This figure will slow down as other limited scale ports conclude their pilot programs, but
is a good estimation of how much could economically impact the implementation of the
100% container scanning law.
Finally the last statement is from the U.S. Government Accountability Office (GAO).
This dependency has conducted several reports on maritime issues in order to assess the
48
performance of government dependencies. This investigative agency has the objective to
examine the use of the public founds, evaluate federal programs and provide analyses of
current governmental issues. In their statement the GAO found the following challenges
(related to our study) (Caldwell [2008 pp. 4-5]).
Resource Responsibilities: the GAO questions who will bear the additional costs incurred
on staff, maintenance and infrastructure in order to implement the legislation.
Logistics: the GAO questions incurred costs and time required to bring the containers
from the area stored to the scanning area. Also mentions the transshipments as a major
challenge derived of the short time and small area to maneuver and perform scanning
inspections.
Technology and infrastructure: the weather conditions may damage the equipment
utilized at foreign ports causing congestions. Technological infrastructure will be required
in order to submit high volumes of data to the National Targeting Agency in Virginia,
USA.
The GAO coincides with the DOE about the challenge of transshipments. Also mentions
the possibility that equipment suffers a breakdown derived from environmental
conditions. In this sense, what will happen if a scanner brakes down during port
operations? All the exports bounding to U.S. will be stopped? As well the cost factor
concerns for the GAO, mentioning that is unclear who will bear the implementation costs
of the legislation.
To end this section, is clear both postures of the CBP and DOE, they do not recommend
the implementation of the 100% container scanning in a full scale in 2012. As final
remarks the Assistant Deputy Administrator David Huizenga concludes:
“In summary, we have learned a lot from SFI pilot implementation. The SFI deployments in Honduras,
the United Kingdom and Pakistan indicate that that scanning US-bond maritime containers is possible
on a limited scale” (Huizenga [2008 p.3]).
Even though the postures of both agencies try to diminish the implementation of the
100% container scanning legislation, the Congress will continue pushing the application
of the Law. After the statements of three witnesses, the Chairman Lautenberg kept
questioning Commissioner Ahern about when the DHS will have ready the 100%
scanning implementation (U.S. Senate Committee on Commerce, Science and
Transportation [2008]). It seems that this battle has just started, Chairman Lautenberg
argued:
“The bush administration has long believed that a layered approach is adequate for securing our ports.
But in practice, this “layered” approach has been more like a piecemeal one, leaving our country and
our economy more vulnerable” (Lautenberg [2008]).
49
4.4
Evaluating the viability of 100 percent container inspection at
America’s ports
The study conducted by Martinosi, Ortiz and Willis is described in chapter 12 of the book
“The Economic Impacts of Terrorist Attacks” by Harry Richardson, Peter Gordon and
James Moore II. This study published in 2006 accounts targets the same problem (100%
container scanning) policy but applied to the U.S. American ports. The objective of this
study was to perform a cost-benefit analysis to a 100% container scanning policy of
containers arriving to the United States. The study has a remarkable methodology to
estimate the policy’s implementation costs, according with the possibilities of a terrorist
attack. In this study is better explained the methodology followed to measure the costs
and performance of a container inspection, then the study carried by Carluer. In this study
not only contemplates the costs incurred from the application of the policy, also accounts
the possible benefits obtained by its implementation. In order to asses the cost-benefit of a
100% scanning policy in United States, the coauthors assumed a steady flow of
containers demanding as much equipment as needed. Once again the methodology does
not focuses on the impact of the waiting lines and is more concerned about the total costs
incurred, than the implications in congestion derived from the policy. Besides the small of
the study, the content has a valuable and useful methodology to be applied on limited
scale to our analysis.
4.5
Chapter conclusion
After reviewing the available material we can conclude that so far there is not a complete
study of the impacts of the 100% container scanner. No study has performed waiting lines
analysis derived from the implementation of the law. The recent study of Professor
Carluer from the University of LeHavre lacks of methodology about how was conduced
his cost analysis research. Also lacks to estimate the impacts of congestion at ports
derived from the law implementation. Even though, gives a good overall view about the
problematic of the legislation and gives an approximate estimation of the reality. The
report presented by the CBP and DOE can be summarized that both dependencies
consider the legislation as a waste of resources that could have been applied to secure
other areas of the supply chain. Although the skepticism by CBP and DOE to continue
developing the SFI program. On the other hand, the Congress is decided to implement the
legislation sooner or later. With the figures obtained from the final report of the SFI
program was estimated the cost per container scanned from the pilot programs. Also
mentioned in this chapter was the remarkable methodology employed by Martinosi, Ortiz
and Willis to assess the cost-benefit analysis of a 100% container scanning policy, but
focused in U.S. American ports. In the following chapter will be describe the
methodology that will be applied to conduct a full scale analysis based on economic and
operational impacts.
50
5 The models and data
5.1
Introduction
In chapter two we identified the major concerns of the shipping community expected
from the 100% Container Scanning: Costs and Delays of shipments. In this chapter we
will briefly describe the methodology employed to assess the economic and congestion
impact of the 100% Container Scanning Law. We will describe the Queuing Models and
Simulation Models utilized to analyze the waiting lines and the economic costs of the
legislation. Also included in this chapter will be an explanation of the sources of the Data
used in this study, and the scenarios assumed in order to assess the impact of the
legislation in different situations.
5.2
Description of the Models
In this study two different quantitative approaches were used to measure the impact of
waiting lines of the 100% container Scanning Legislation. The first method is the
Queuing model consisting in mathematical formulas and relationships used to assess
operating characteristics of waiting lines (Anderson, Sweeny, Williams, Martin [2008
p.545]). The second method utilized is the Waiting Line Simulation model which
contains mathematical expressions and logical relationships combining probabilistic and
controllable inputs, in order to produce an output that represents a real system (Anderson,
Sweeny, Williams, Martin [2008 p.586]). The Queue Modeling analyzes waiting lines
using formulas to calculate a steady flow of the system, according to the average arrivals
and the total capacity of service channels. These queuing formulas are also known as
static simulation models, meaning that the model applied does not change over time. On
the other hand, the Simulation model is a dynamic approach, where the state of the model
changes, or evolves over time, according to the availability of the channels (scanners) to
provide the service.
5.2.1
Queuing model formulas
For the case of Queuing Model will be used the following classification based in the
Kendall notation: M/M/k
Where:
M= denotes a Poisson probability distribution for the arrivals
M= denotes the exponential time distribution for the service rate
51
k= denotes the number of channels providing the service.
5.2.2
Single channel waiting line model with Poisson arrivals and exponential service times:
M/M/1
The following formulas are used to assess the steady state of the operating characteristics
for a single channel model with Poisson arrivals and exponential service times (Anderson,
Sweeny, Williams, Martin [2008 pp.550-551]), where:
λ = The mean number of container arrivals per time period (Mean arrival rate)
µ = The mean number of services per time period (the mean service rate)
With the use of the M/M/1 formulas we can measure the probability that no containers are
in the system represented by P0 and assessed by:
λ
P0 = 1 −  
µ
(5.1)
With the aid of µ and λ we can determine the average number of containers in the waiting
line of any stage of the ISS represented by Lq
Lq =
λ
µ (µ − λ )
(5.2)
With equation 5.2 we can measure the average number of containers in the system
represented by L and determined by equation 5.3:
L = Lq +
λ
µ
(5.3)
With the average number of containers in the waiting line denoted by equation 5.2 and λ
we can calculate the average time a container spends in the waiting line represented by
Wq and measured by:
Wq =
(5.4)
52
Lq
λ
With the aid of Wq and µ we obtain the average time a container spends in the system
represented by W and calculated by:
W = Wq +
1
µ
(5.5)
In order to assess the probability that an arriving container has to wait for service
represented by Pw and determined by equation 5.6:
Pw =
λ
µ
(5.6)
Finally, the probability of n containers in the system is represented by Pn and measured
by formula 5.7:
n
λ
Pn =   Po
µ
(5.7)
5.2.3
Multiple channel waiting line model with Poisson arrivals and exponential service times:
M/M/k
The following formulas are used to assess the steady state of the operating characteristics
for a Multi channel model with Poisson arrivals and exponential service times (Anderson,
Sweeny, Williams, Martin [2008 pp.555-556]), where:
λ = The mean number of arrivals per time period (Mean arrival rate)
µ = The mean number of services per time period (the mean service rate)
k= The number of Channels
According to the M/M/k Model the probability that no units are in the system is
represented by Po and is determined by the equation 5.8:
Po =
1
(λ / µ )
(λ / µ ) k  kµ 
+


∑
n!
k!  kµ − λ 
n =0
k −1
n
(5.8)
53
Once Po is determined, we can obtain Lq which represents the number of containers in
the waiting line:
Lq =
(λ / µ ) k λµ
Po
(k − 1)!(kµ − λ ) 2
(5.9)
In order to calculate the average number of containers in the system represented by L, the
average time a container spends in the waiting line represented by Wq and the average
time a container spends in the system represented by W under a multiple channel model,
the same formulas are utilized for a multiple channel than for a single channel measured
by formulas 5.3 through 5.5:
Number of containers in the system:
L = Lq +
λ
µ
(5.3)
Average time a unit spends in the waiting line:
Wq =
Lq
λ
(5.4)
Average time a unit spends in the system:
W = Wq +
1
µ
(5.5)
In order to obtain the probability that an arriving unit has to wait for service represented
by Pw with the aid of Po equation 5.13 is determined by:
1λ 
Pw =  
kµ
k
 kµ 

 Po
 kµ − λ 
(5.13)
To obtain the probability of n containers in the system represented by Pn we use the
formulas 5.14 and 5.15:
54
(λ / µ ) n
Pn =
Po ⇒ n ≤ k
n!
(5.14)
Pn =
(λ / µ ) n
Po ⇒ n > k
k!k ( n − k )
(5.15)
Finally, in order to asses the behavior of the waiting lines when the arrival times are
greater than the service times, we have developed an original new formula for the M/M/1
and M/M/k queuing models represented by M q . M q assess the average number of
containers increasing the waiting line per time period when λ>µk:
when , λ > µk
M q = n(λ − µk )
(5.16)
λ , µ = const.
Where:
n= denotes the number of time period when the server is blocked
λ= denotes the arrival average time
µ= denotes the service average time
k= denotes the number of servers
5.2.4
Waiting Line Simulation model formulas
5.2.2.1 Interarrival times distribution
In our Simulation model we are assuming an exponential probability distribution of
Interarrival times is determined by formula 5.17. The exponential (Mayerson [2005 p.p
140-142]):
P(X>m +n| X > m) = P (X>n)
(5.17)
Where:
X is a random number representing the number of minutes that we have to wait for our
first container arrive. This equation says that the conditional probability of having to wait
more than n additional minutes, given that we have already waited any number of minutes
“m” is independent of the number of minutes that we have already waited. This is called
no-memory property. The exponential distribution uses only one parameter the mean (µ).
55
5.2.2.1 Service times distribution
Normal probability distribution of Service Times.
The normal probability distribution can fit for any unknown quantity that can be
expressed as the sum of many small contributions that are independently drawn from
some fixed probability distribution (Mayerson [2005 p.109]).
P(X<w) =NORMDIST ((w-µ)/σ)
(5.19)
5.3
Cost Analysis
To conduct an economic analysis of the waiting lines, first is necessary determine the
total cost of the waiting line. In this sense, will be applied the model suggested by
Anderson, Sweeny, Williams and Martin, ([2008 pp.561-563]):
5.3.1
Waiting costs
From all the costs the most difficult to measure are the waiting costs of congestion. The
objective of this study is not toe asses the waiting costs of congestion and because these
costs will vary between ports, we are basing our calculations in the figure estimated by
the American Highway shipping Alliance (2000). This congestion costs are forecasted to
the year 2012 in order to have a close estimation of the cost when the legislation will be
implemented.
5.3.2
Transfer Costs
The second indirect cost included in this study is the transfer costs. These costs are
incurred only by transshipped containers and represent the extra move of container from
the terminal yard to process the scanning and return back to the container yard.
5.3.3
Service costs
As service cost are included all the costs attributable to the NII scanning itself. These
costs are calculated in base of the cost report presented by the pilot programs held by the
Security Freight Initiative (SFI) mentioned in chapter 2. These costs include but are not
limited to equipment costs, labor, installation, software development, installation,
communications, training, maintenance, among other costs related with the scanning
service.
56
5.3.4
Total costs
The total cost model for a waiting line is denoted by:
cw = the waiting cost per time period for each unit
L = the average number of units in the system
cs = the service cost per time period for each channel
k = the number of channels
TC = Total cost per time period
The total cost is the sum of the waiting cost and the service cost, denoted by the following
formula (transfer costs not included):
TC = cw L + cs k
(5.20)
5.4
Data Collection
The data collection was obtained from secondary and primary data. All the figures
employed in this analysis are supported by related literature of the topic. The literature
has been obtained from academic studies, private and government research. The volumes
of containers bounding to the United States utilized in our study are based in the figures
estimated by PIERS (2008), collected from vessel; Manifests and bills of lading;
excluding postal and militarily shipments. All the information employed is supported by
published articles from experts in their area of study. As well, an interview with the
security port authority of the port of Rotterdam has been held trough a systematic
questionnaire in order to obtain as much as data possible from primary source about the
possible impacts of the 100% Container Scanning Legislation.
5.5
Scenarios
In order to apply the selected methodology is necessarily to create scenarios that represent
the possible outcomes of the legislation in the future. These scenarios will be applied to a
Big port and to a Small port in order to assess the different impact of the legislation.
5.5.1
Scenario 1A
The first scenario to be analyzed, it assess the impact of the 100% Container Scanning
Legislation in the foreign ports with forecasted container flows projected to 2012. In this
scenario, waiting lines will be analyzed, with the parameters of the current technology
available in the market. Also, this scenario assumes that foreign officers, or scanner
technicians, conduct an analysis of the images taken by the Non-Intrusive Inspection
system, also known as scanner. Finally the scenario only accounts for Gate Flows through
the Integrated Inspection System (IIS).
57
5.5.2
Scenario 2A
In Scenario 2A, it is assumed that the forecasted container flows is projected to 2012. In
this scenario, the waiting lines will be analyzed, with the parameters of the current
technology available in the market. Also, this scenario assumes that foreign officers, or
scanner technicians, conduct an analysis of the images taken by the Non-Intrusive
Inspection system, also known as scanner. Finally, the scenario only accounts for Gate
flows and Trans-shipment flows through the Integrated Inspection System (IIS).
5.5.3
Scenario 2B
In the third scenario, it is assumed that the container flow is projected to 2012. Also in
this scenario, the waiting lines will be analyzed, with the parameters of the current
technology available in the market. Different from previous scenarios, this one assumes
that no analysis is performed by foreign officers, or scanner technicians, at the foreign
ports. Also, as a result of no analysis required, the Second Inspection is removed. This
scenario only accounts for Gate Flows through the Integrated Inspection System (IIS).
5.5.4
Scenario 2C
In Scenario 2C, it is assumed that the forecasted container flows is projected to 2012.
Different from previous scenarios, this one analyzes the waiting lines with the parameters
of the new and improved technology available in the future. Also, this scenario assumes
that foreign officers, or scanner technicians, conduct an analysis of the images taken by
the Non-Intrusive Inspection system, also known as scanner. Finally the scenario only
accounts for Gate flows and Trans-shipment flows through the Integrated Scanning
System (ISS).
5.5.5
Scenario 2D
In this scenario, forecasted container flows for 2012 are considered. It also considers the
availability of new technology in the future. An analysis of the images by foreign
officers, or technicians, is included in this scenario. Gate and Trans-shipment flows are
considered as well. And finally under this scenario, a Hub and Spoke effect is accounted
for, assuming that not all the ports are able to buy an Integrated Inspection System, and
reallocating flows of containers bound for the United States to Hub ports which have the
technology to provide the service.
5.6
Small and Big port selection
In order to assess the different impacts of the Legislation in the five different scenarios
described in chapter 6, in this chapter we will compare the impact on a small port against
a big port. As a big port example, we will take the Port of Hong Kong, and the Port of
Jebel Ali in Dubai for the example of the small port. The difference between a big port
58
and a small port is based on the current container flows, from these origin ports to the
USA, and not in the total container capacity, terminal infrastructure or economic power of
the terminal operators or port authorities. For both ports, the flows of road trucks and the
trans-shipments Countries to USA will be analyzed. The trans-shipment flows include all
the intermodalities of transport connected to the terminal (Barge, Rail and Short-sea
Shipping). Next, we will be briefly described both ports.
5.6.1
Dubai – Small port
The Middle East Port of Jebel Ali is located in Dubai, United Arab Emirates (UAE). The
UAE exported 19,665 US-bound containers in 2007 (PIERS [2008]). Currently the
United Arab Emirates are ranked in the 58th position of the U.S. waterborne container
exports to the United States, representing 0.11% of the market share. The Port of Dubai
has two modern container terminals; Terminal 2 is completely new and has just started
operations this year, with a capacity of 5 million TEU per year. Dubai Ports is the only
operator at the Jebel Ali Port. The port has 8 entry gates to the port, one gate to the
container terminal with 5 booths, see Figure 7.1. At the moment, the port of Dubai has
one scanner located at the entrance of the terminal (Carluer [2007]). Dubai is
characterized for being a trans-shipment port with a trans-shipment factor of 50.1% of the
total volume (Drewery [2004]).
5.6.2
Hong Kong – Big port
The port of Hong Kong is located in Southeast Asia, and will be considered independent
of China in this study. Hong Kong exported 644,399 US-bound containers in the last year
(PIERS [2008]). Hong Kong is ranked in the third position of the total waterborne
container exports to the United States, representing 3.47% of the market share. Currently
the Port of Hong Kong has 9 container terminals with five operators: Modern Terminals
Ltd.( in 4 terminals), Dubai Port International Ltd. (1 terminal), Hong Kong International
Terminals Ltd. (4 terminals) plus 1 terminal shared with COSCO Information and
Technology Ltd. and Asia Container Terminals Ltd. (1 terminal)(Wikipedia [2008]). At
the moment there are 8 scanners at the port (Curlier [2007]). According to the SFI report,
the scanner used in the pilot program is located at the Kwai Tsing MTL terminal. The
Port of Hong Kong currently presents serious space problems and congestions derived
from high container flows. The Port of Hong Kong has several entries to the port, two
entry booths per terminal. Hong Kong is characterized to have big gate flows with a
trans-shipment factor of 30.3% of the total volume (Drewery [2004]). Table 7.1
summarizes the figures of our examples of a big and small port.
59
Table 5.1
Small and Big port comparison
Description
Port
US-bound Container volume 2007
Big Port
Dubai
Hong Kong
19,665.00
644,399.00
U.S. Rank Partner
3
58
Container Terminals
2
9
Transshipment Factor
5.7
Small Port
50.1%
30.3%
Number of Booths per Terminal
5
2
Current Number of Scanners
1
8
Number of Terminal Operators
1
5
Conclusions
After a systematic design, we have created a Model that can be applied to any port in
order to provide a close estimate of the impacts of the law. The structure of the model
was built to create a close approach of the reality. Even though, it would be difficult to
assess a universal impact of the legislation due the diversity of variables between ports,
the model employed in this study can be applicable to any port in the world. In
conclusion, a good model has been created, able to be applied to any big or small port.
60
6 Measuring the Congestion Impact: Analysis
and Results of Waiting Lines
6.1
Introduction
In this chapter, we will introduce the results of the five scenarios for each sample port
explained in Chapter 5. First, we will forecast the expected container volumes for each
port for the year 2012 when the 100% Container Scanning Legislation will be
implemented. After estimating the upcoming U.S.-bound container flows for each port,
we will divide these volumes in trans-shipments and gate flows in order to evaluate our
scenarios. Then, with the aid of Queue Modeling, we will assess the overall behavior of
the system for each scenario. We will include the results of the Waiting Line Simulation
model, considering container arrivals at peak hours in order to have a closer estimate to
the real system. After the analysis through both methods, we will compare the results and
analyze the obtained figures. At the end, we conclude the chapter with the most
remarkable findings derived from our waiting lines analysis.
6.2
Forecast and Container Flows
In order to calculate the impact of the legislation first is necessarily to calculate the total
flow volumes of export from Dubai and the volumes bounding from Dubai to the United
States for 2012. The table 6.1 shows the volumes bounding for the past eleven years.
Table 6.1
Historic container flows from Hong Kong and UAE to United States of America
YEAR
Hong Kong US-Exports
United Arab Emirates US-
Total US Container
Exports
Imports
1997
630,618
10,052
7,787,430
1998
799,448
13,091
8,919,223
1999
1,020,940
15,038
9,960,465
2000
961,917
19,269
11,086,604
2001
960,830
19,507
11,268,347
2002
1,198,060
18,427
12,915,512
2003
1,292,006
19,294
13,899,132
2004
1,138,791
20,996
15,805,478
2005
832,106
22,406
17,388,117
2006
719,957
22,004
18,634,753
2007
644,399
19,665
18,548,120
61
Source: PIERS (2008).
From these past data we can assume that the future will be like the past. If we calculate a
mean and a standard deviation of these figures, we probably will forecast similar figures
from the lately trend presented in the last years. Table 6.2 represents the Mean and
Standard Deviation of previous years.
Table 6.2
Growth statistics per country (Hong Kong/UAE)
United Arab Emirates
Total US Container
US-Exports
Imports
Mean
927,188
18,159
13,292,107
Standard Deviation
223,292
3,856
3,863,385
Grow Statistics
Source: own calculations.
As we can see the values of the means occurred before 2001 for Hong Kong and Dubai
and before 2004 for the Total Container Imports. This could drive us to a wrong forecast
because the values do not appear to be drawn from one stationary distribution over 11
years. But applying logarithmic grow rates of the figures we can a more stable and
accurate pattern of the export volumes to the USA. These logarithm growths of the last 11
years are calculated in Table 6.3:
Table 6.3
Logarithm of the growth per country (Hong Kong/UAE)
United Arab Emirates USYEAR
Exports
Total US Container Imports
1998
0.237
0.264
0.136
1999
0.245
0.139
0.110
2000
-0.060
0.248
0.107
2001
-0.001
0.012
0.016
2002
0.221
-0.057
0.136
2003
0.075
0.046
0.073
2004
-0.126
0.085
0.129
2005
-0.314
0.065
0.095
2006
-0.145
-0.018
0.069
2007
-0.111
-0.112
-0.005
Source: Own calculations.
Now with these figures we calculate the Means and Standard Deviations of each variable
described in Table 6.4:
Table 6.4
Logarithm of the growth statistics (Hong Kong/UAE)
Growth Statistics
Total US Container
US-Exports
Imports
Means
0.002
0.067
0.087
Standard Deviation
0.189
0.123
0.049
62
The annual grow rates of the variables can be found above and below in the data series,
so there is no obvious difference between the grow rates earlier and later in the series.
Now the growth ratio is computed from the logarithmic growth rate by exponential
function, obtaining the forecasts in Table 6.5 for the next five years.
Table 6.5
Forecasted U.S. container flows for 2012 (Hong Kong/UAE)
United Arab Emirates USYEAR
Exports
2008
665,056
22,568
19,922,391
2009
678,186
23,453
21,822,599
2010
702,561
24,609
22,191,445
2011
685,000
24,424
22,734,998
2012
694,005
28,477
23,393,254
Dubai US-bound containers represent only 1% of the total exports (Carluer [2008]) and
Hong Kong US-bound containers represent 11.5% (Report to Congress Integrated
Scanning System, 2008). Based in these figures and with the above forecasts, the
transshipment factors of each port from Table 6.5 are created the following figures 6.1 to
6.3.
63
Figure 6.1
Forecasted Container Flows Small port
United Arab Emirates US-Exports
30,000
CONTAINER
25,000
20,000
15,000
10,000
5,000
20
12
20
11
20
10
20
09
Source: PIERS and own forecast.
YEAR
Figure 6.2
20
08
20
07
20
06
20
05
20
04
20
03
20
02
20
01
20
00
19
99
19
98
19
97
0
Forecasted Container Flows Big port
1,400,000
CONTAINER
1,200,000
1,000,000
800,000
600,000
400,000
200,000
20
12
20
11
20
10
20
09
YEAR
Figure 6.3
20
08
20
07
20
06
20
05
20
04
20
03
20
02
20
01
20
00
19
99
19
98
19
97
0
Total Forecasted Container Flows to United States of America
CONTAINER
25,000,000
20,000,000
15,000,000
10,000,000
5,000,000
YEAR
20
12
20
11
20
10
20
09
20
08
20
07
20
06
20
05
20
04
20
03
20
02
20
01
20
00
19
99
19
98
19
97
0
Now with the projected container flows for the year 2012, and with the transshipment
factors: Dubai 50.1 and Hong Kong 30.3 (Dewery [2004]) of each port we can calculate
the gate and transshipment flows for each port. The Table 6.6 summarizes the forecasted
64
container flows for each port. With the help of Table 6.6 we can generate the average
arrival rates for the gate flow and the transshipment flow for both flows. Table 6.7
summarizes the average arrivals for each port in a daily and hourly basis. An important
notation in Table 6.7 is the third row where specifies the volumes for the port of Hong
Kong, derived the high export rate to containers bounding to United States and the
number of current terminals (nine total) and the total of operators (five), we don’t have
the exact figures of market share for each terminal or operator. Because of this, it is
necessary to assume a uniform spread of container flow of export of the total containers
bounding to the USA equally throughout the nine terminals of the port in order to assess
the impact of the legislation in the port of Hong Kong. This assumption wont affect or
disrupt the analysis of this study if is take into account that the figures shown in the
analysis represent only one ninth of the total flow of the port or the following figures
must be multiplied by nine if we want to assess the total impact on the Hong Kong port.
Table 6.7
Average Arrival Rate Gate and Transshipment per port (Hong Kong/UAE)
Average Arrival Rate Gate
Port
Day
Dubai
Hong Kong
Average Arrival Rate Transshipment
Hour
Day
Hour
38.93
1.62
39.09
1.63
1325.26
55.22
576.12
24.00
147.25
6.14
64.01
2.67
Hong Kong per
Terminal
Also with the help of Table 6.6 and the study of gate activity at terminals by Saanen
(2008), it is generated the Table 6.8 which accounts the peak arrival rate of trucks through
the terminal gate.
Table 6.8
Peak Arrival Rate per port (Hong Kong/UAE)
Peak Arrival Rate Gate (Saanen [2008])
Shift C 5%
Port
Day
Shift B 40%
Hour
Day
Hour
Arrival Rate
Shift A 55%
Day
Hour
Transshipment
Day
Hour
Dubai
1.95
0.08
15.57
0.65
21.41
0.89
39.09
1.63
Hong Kong
66.26
2.76
530.11
22.09
728.90
30.37
576.12
24.00
7.36
0.31
58.90
2.45
80.99
3.37
64.01
2.67
Hong Kong
per Terminal
65
Table 6.6
Forecasted Gate and Transshipment Flows for Small and Big port
US-Bound
Non-US Bound
TOTAL
Total Rest of
YEAR
Total US-Bound
Gate
Transshipment
Exports
Gate
Transshipment
Exports
2007
19,665
9,813
9,852
1,927,171
961,658
965,513
1,946,836
2008
22,568
11,261
11,306
2,211,639
1,103,608
1,108,031
2,234,207
2009
23,453
11,703
11,750
2,298,431
1,146,917
1,151,514
2,321,885
2010
24,609
12,280
12,329
2,411,729
1,203,453
1,208,276
2,436,339
2011
24,424
12,188
12,237
2,393,590
1,194,401
1,199,188
2,418,014
2012
28,477
14,210
14,267
2,790,699
1,392,559
1,398,140
2,819,175
Hong Kong
US-Bound
Non-US Bound
TOTAL
Total Rest of
YEAR
Total US
Gate
Transshipment
Exports
Gate
Transshipment
Exports
2007
644,399
449,146
195,253
4,314,670
3,007,325
1,307,345
4,959,069
2008
665,056
463,544
201,512
4,452,981
3,103,728
1,349,253
5,118,037
2009
678,186
472,695
205,490
4,540,895
3,165,004
1,375,891
5,219,080
2010
702,561
489,685
212,876
4,704,101
3,278,758
1,425,343
5,406,662
2011
685,000
477,445
207,555
4,586,525
3,196,808
1,389,717
5,271,525
2012
694,005
483,722
210,284
4,646,816
3,238,831
1,407,985
5,340,821
66
6.3
Results and analysis of the Queue Model
In this section, the M/M/1 and M/M/k queuing models are applied to analyze the
behaviors of the waiting lines throughout the Integrated Scanning System (ISS). In
previous queuing studies (Martinosi, Ortiz, Willis [2006]), container or truck arrivals
have been classified as M/M/1 and M/M/k systems. Utilizing the Kendall notation, the
M/M/1 and M/M/k systems represent exponential arrival times (Poisson probability),
exponential service times (Poisson probability) and a single or multi-channel service
(Anderson, Williams, Sweeny, Martin [2008 p.563]). One of the reasons to use
exponential distributions in the waiting line problems is because this distribution obeys
the no-memory property, which means that the next Interarrival time does not depend on
earlier Interarrival time (Maki, Thompson [2006]). In the following scenarios, we are
considered the mean arrival rate of containers per hour and the mean service rate for each
server of Gate, RPM and NII equipments. Under the application of the M/M/1 and
M/M/k, these models do not consider peak arrival times of containers, only average
arrivals. M/M/1 and M/M/k are optimization static models, which means that the
formulas utilized assess the steady state of the operating characteristics, and that the
system does not change or evolve overtime. In section 6.3, we will assess peak arrivals of
containers through the gate.
Scenario 1A: Forecasted container volumes (2012) with current technology and NII (Non
Intrusive Inspection) analysis. Only Gate flows through the system. M/M/k Model.
In the first scenario is analyzed the five possible queues through the Integrated Scanning
System (ISS). These queues are present in the Gate, Radiation Portal Monitors (RPM),
Alarm Inspection, Non Intrusive Inspection (NII) equipment or commonly known as
scanner, and the Second Inspection mechanism. In this scenario the gate process provides
service to all the containers arriving inland by truck to the terminal, including the U.S.
Bound and the Non U.S. Bound, with an average service rate of 60 containers per hour.
The Radiation Portal Monitors (RPM) are also utilized by all the containers arriving by
truck through the gate no matter their final destination with an average service of 150
containers per hour. The Alarm inspection is triggered 1.5 percent of the total flow of the
RPM’s with an average service rate of 4 days or .01 per hour, assuming manual
inspection by the authority. The NII equipment is utilized only by the U.S. Bound
containers with a 12 minutes average per service or 5 scans hour rate including an
analysis of the image by the officers taken by the scanner. And finally the Second
Inspection, which represent approximate 1% of the containers that the officers consider
necessary to inspect manually from the NII system. This inspection is a similar process as
the Alarm Inspection which also takes 4 days on average of service or 0.01 per hour.
Applying the M/M/1 queuing formulas from the chapter 6, Table 6.9 summarizes the
Operating characteristics of the scenario 1a for the small port of Dubai.
Table 6.9
NII Operating Characteristics M/M/1 Queuing Model, Small Port scenario 1A
Operating Characteristics
Code
Gate
RPM
Alarm
2nd
Inspection
Inspection
(1.5%)
NII
(1%)
Servers
K
5.00
5.00
1.00
1.00
1.00
The mean of container arrivals per hour
λ
160.59
160.59
2.41
1.62
0.02
67
The mean number of services per hour
Utilization factor
Alarm
2nd
Inspection
Inspection
Code
Gate
RPM
(1.5%)
NII
(1%)
µ
60.00
150.00
0.01
5.00
0.01
(λ/µ)
54%
21%
23125%
32%
156%
Lq
0.19
0.00
NA
0.16
NA
L
2.86
1.07
NA
0.48
NA
Wq
0.00
0.00
NA
0.10
NA
W
0.02
0.01
NA
0.30
NA
Pq
0.16
0.01
NA
0.32
NA
Mq
0.00
0.00
2.40
0.00
0.01
Ll
39.40
14.74
NA
13.75
NA
La
135.49
50.69
NA
47.29
NA
Average number of Containers in the
waiting line
system
Average time a container spends in the
waiting line (hour)
system
Probability an arriving container finds
queue
Average number of containers
increasing in the waiting line per time
period when λ>µk
Average Length of Containers in
System (Mts)
Average Occupying Area of Containers
in System (Mts^2)
From the previous table we can perform our first analysis of the flows of containers of a
small port through the 5 possible queues in the Integrated Scanning System. From the
Gate point, we see a steady service with 2.86 containers on average in the system,
representing an average length of 39.4 meters occupying 135.49 square meters divided
between the 5 booths in the terminal. The RPM’s point is even more relaxed than the
Gate point due its high capacity of service 150 containers per hour, representing no
queues in this point as well as the Gate point. In the case of the Alarm inspection and the
Second Inspection, the operating characteristics show a NA (Not Applicable) factor. This
Non Applicable factor is present because the Mean of container arrivals (λ) is greater than
the mean number of services (µ) per period of time (hour in our example). When “λ>µ” is
present in an M/M/1 or M/M/k models, the Queuing Model formulas are disrupted
avoiding the possibility to assess the operating characteristics of the model. When this
condition exists, it means that the queuing line will grow indefinitely due the service
facility does not have the sufficient capacity to provide the service to the arriving
containers (Anderson, Sweeney, Williams and Martin [2008]).
In the current literature of queuing theory, any model requires the condition of “λ/µ<1”,
otherwise the queue will grow without limits, but there is no current formula to assess the
impact of increase on the queue. To solve this problem in chapter 6 was introduced an
original formula developed to assess the growth rate of the queue when the mean of
container arrivals is grater than the mean of number of services (λ>µ). This relation was
named M factor assumes a constant service time and constant arrivals, and represented by
the letters “Mq” described in the operating characteristics of Table 6.9. The M factor for
the scenario 1A is 2.4, meaning that per every period of time the services is blocked by
68
grater arrivals then the service capacity, the queue line will grow on average 2.4
containers per period of time (in this case by hour) indefinitely. This new factor is not
science rocket, but is very efficient measure to understand and analyzed the impact of the
waiting lines when the situation of λ>µ is present.
Accordingly to the presence of the “Mq” code the impact of the new policy of 100%
container scanner will affect the Alarm Inspection and the Second Inspection on scenario
1A for a small port with current technology only considering gate flows. The legislation
will increase the flow of containers through these two inspection points and the capacity
of service, which is 4 days in average or .01 per hour, is insufficient to cover the arrivals
generated by the new security mechanism. In the case of the NII equipment which is our
principal objective of analysis, it can be observed that one scanner is enough to provide
the service to the average arriving containers. According to the “Wq” formula, the
average time a container spends in the waiting line is 0.10 hours or 6 minutes. To
understand better the function of the NII system, Figure 6.4 shows the probability to find
“n” number of containers in the system. As we can see, the probability to find no
container in queue is almost 70% of probability, while more then three containers on
queue seem less probable.
Probability to find “n” number of containers in the Small port NII system
80%
70%
60%
Probability
50%
40%
30%
20%
10%
0%
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
Figure 6.4
Number of Containers in the NII System
The queuing theory has mathematical formulas and relationships used to understand the
performance of a waiting line. Even though, sometimes is difficult to understand the
operating characteristics of the system only with the resulting figures of the queuing
formulas. In this respect, it is easier to analyze the model results if are combined with the
representation of the container arrivals and the service time through a graph. In Figure 6.5
are represented the container arrivals through the gate for the example of the port of
Dubai. The graph simulates in blue line 1.62 containers as the average arrival (λ) and the
service rate in purple line with 5 containers per hour (µ) for the small port throughout 3
days of operation. In the graph is introduced the peak arrival time recalling the arrival
distribution through the gate at terminal suggested by Saanen (2008). Also represented in
the graph is the M factor in two lines, average and peak both with zero value. The M
69
factor average is “Mq” represented in the operating characteristics of the table 6.9 and the
M factor peak which is just introduced as the increasing waiting line at peak time when
the system is blocked when λ>µ.
Figure 6.5
NII Container Arrivals and service rate, Small port scenario 1A
6.00
5.00
Containers
4.00
Average Arrival
3.00
Peak Arrival
2.00
M Factor Peak
1.00
Service Rate
0.00
22
:
00
2:
00
6:
00
10
:0
0
14
:0
0
18
:0
0
22
:0
0
2:
00
6:
00
10
:0
0
14
:0
0
18
:0
0
22
:0
0
2:
00
6:
00
10
:0
0
14
:0
0
18
:0
0
M Factor Average
Hours (3 Days)
As we can observe the peak arrivals of containers are growing as the time passes during
the day until a huge drop appears at the beginning of the first shift of the next day.
Because these peak fluctuations through the day appear under the line of the service rate,
the line of M factor peak and the M factor average will remain in a level of zero. This
means that even though the expected fluctuations of the arrivals according with the
analyzed tendencies of arrival through the terminal gate (Saanen [2008]), the queue lines
will not suffer increasingly queue growths.
Now is the turn of the big port to be analyzed by the M/M/k queuing model. In the Table
6.10 are shown the Operating characteristics of the port of Hong Kong under Scenario 1A
with one NII scanner.
Table 6.10
NII Operating Characteristics M/M/1 Queuing Model, Big Port scenario 1A
Alarm
2nd
Inspection
Inspection
Code
Gate
RPM
(1.5%)
NII
(1%)
k
2.00
2.00
1.00
1.00
1.00
the mean of container arrivals per hour
λ
47.22
47.22
0.71
6.14
0.06
the mean number of services per hour
µ
60.00
150.00
0.01
5.00
0.01
(λ/µ)
0.39
0.16
67.99
1.23
5.89
Lq
0.14
0.01
NA
NA
NA
L
0.93
0.32
NA
NA
NA
Wq
0.00
0.00
NA
NA
NA
Description Characteristics
Servers
Utilization factor
waiting line
system
waiting line (Minutes)
70
Description Characteristics
Alarm
2nd
Inspection
Inspection
Code
Gate
RPM
(1.5%)
NII
(1%)
W
0.02
0.01
NA
NA
NA
Pq
0.22
0.04
NA
NA
NA
Mq
0.00
0.00
0.70
1.14
0.05
Ll
13.75
13.75
NA
NA
NA
La
47.29
47.29
NA
NA
NA
system
queue
period when λ>µk
System (Mts)
Average Occupying Area of Containers
in System (Mts^2)
The characteristics of the two ports are similar in the case of the Gate and RPM points.
This is due the high capacity compared with the mean arrival of containers. As well as in
the small port, the big port also experiences service problems in the Alarm and Second
inspections, representing a lack of capacity service. In the case of the NII equipment, the
M Factor is also present in the operating characteristics table, implying that one scanner is
not sufficient to provide a steady service in a big port like Hong Kong. With a constant
rate of growth of the Queue expressed by “Mq” this factor measures the speed of the
growth of the queue at 1.14 containers per hour after the system is blocked by higher
arrivals then service capacity. Figure 6.6 represents the behavior of the container arrivals
and the service time under scenario 1A for the port of Hong Kong. Because in this
scenario the arrival rate is grater than the service capacity per hour, the M factor “Mq”,
represented by the light blue line (M Factor Average), will grow constant indefinitely.
The red line is also the M factor but considering the peak arrivals, which even increase
more than the M factor average and drops after the next shift, but never returns to a
steady situation, reaching indefinitely growth.
Container arrivals and service capacity Big Port scenario 1A one NII
140.00
120.00
100.00
CONTAINERS
AVERAGE
ARRIVAL
PEAK
ARRIVAL
80.00
M FACTOR
(PEAK)
60.00
M FACTOR
(Average)
SERVICE
40.00
20.00
18
:0
0
14
:0
0
10
:0
0
6:
00
2:
00
22
:0
0
18
:0
0
14
:0
0
10
:0
0
6:
00
2:
00
22
:0
0
18
:0
0
14
:0
0
10
:0
0
6:
00
2:
00
0.00
22
:0
0
Figure 6.6
HOURS (3 Days)
71
In this case an extra NII scanner is required to provide a steady flow of service in the port
of Hong Kong. Table 6.11 summarizes the operating characteristics of a big port with two
scanners providing the service.
Table 6.11
Operating Characteristics of Queue Model for Big Port Scenario 1A
Alarm
2nd
Inspection
NII (12
Inspection
Code
Gate
RPM
(1.5%)
min)
(1%)
Servers
k
2.00
2.00
1.00
2.00
1.00
λ
47.22
47.22
0.71
6.14
0.06
Utilization factor
µ
60.00
150.00
0.01
5.00
0.01
(λ/µ)
39%
16%
6799%
61%
589%
Lq
0.14
0.01
L
0.93
0.32
67.99
1.97
5.89
Wq
0.00
0.00
0.00
0.12
0.00
W
0.02
0.01
96.00
0.32
96.00
Pq
0.22
0.04
Mq
0.00
0.00
0.70
0.00
0.05
Ll
13.75
13.75
935.13
27.10
81.01
La
47.29
47.29
3215.36
93.17
278.54
waiting line
0.74
system
Average time a container spends in
the waiting line (Minutes)
the system
queue
0.47
period when λ>µk
System (Mts)
Average Occupying Area of
Containers in System (Mts^2)
According with the M/M/2 queuing model with the increase in one extra NII scanner, the
port of Hong Kong is able to provide the scanning service for the U.S. Bound containers.
Is notable that the M factor is not present anymore due the fact that with the increase of
scanning channels the capacity of the port has doubled for a total capacity of 10
containers per hour against 6.14 average arrival rate (λ). In the figure 6.7 is shown the
probability in a big port to find “n” number of containers in the NII system. A
considerable difference of probabilities between the small ports with one NII scanner
compared with the probabilities of a big port with two NII scanners. While for the small
port the probability to find no containers in the system is almost 70 percent, for the big
port is slightly under the 25 percent of the probability as shown in the histogram.
72
Figure 6.7
Probability to find “n” number of containers in Big port two NII equipment
35%
30%
Probability
25%
20%
15%
10%
5%
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
0%
Number of Containers in the NII System
Now is time to take a look to the graph of the big port on scenario 1A with two NII
scanners, represented in the Figure 6.8. In this figure we can see the peak arrivals
throughout the day going up and down. In the graph we can see that even though the
average arrivals and service time are covered in the M/M/k queuing from a ratio of 6.14
to 10, if the peak is taken into account at the busiest hours of the day the arrivals will be
greater than the service rate. This situation will lead into an increase in the red line of the
peak M factor, where the queues will growth in a stable rate but only for some hours
returning to zero after the relaxed shift enters.
Container arrivals and service capacity Big Port scenario 1A two NII
12.00
Arrival Average
10.00
Containers
8.00
Peak Arrivals
6.00
Peak M Factor
4.00
Average M Factor
2.00
Service Rate
0.00
22
:0
0
1:
00
4:
00
7:
00
10
:0
0
13
:0
0
16
:0
0
19
:0
0
22
:0
0
1:
00
4:
00
7:
00
10
:0
0
13
:0
0
16
:0
0
19
:0
0
22
:0
0
1:
00
4:
00
7:
00
10
:0
0
13
:0
0
16
:0
0
19
:0
0
Figure 6.8
Hours
In the following scenarios was used the same methodology of Queuing models as in the
scenario 1A. In order to have a valuable, friendly and enjoyable to read for the lecturer, it
will be summarized some of the previous calculations for the next scenarios. From now
and on it will be described only the most remarkable findings of our study on each
73
scenario focusing on the development of the waiting lines and performance of the NII
scanner. The Gate and RPM points have not represented any significant queuing increase
in our study due the high capacity of service. In the case of the Alarm inspection and
Second Inspection, will be tackled in the scenarios 2C and 2D.
Scenario 2A: Forecasted container volumes (2012) with current technology and NII (Non
Intrusive Inspection) analysis. Gate and Transshipment flows through the system.
In the scenario 2A is accounted the similar situation as the scenario 1A, but with the
variant to include the transshipment flows to the Integrated Inspection System. Even
though that the transshipment containers currently represent a major threat for the
implementation of the legislation, in our further scenarios we are considering a 30 minute
average transfer time. It is assumed will be picked up by a terminal truck, which will be
constantly passing the containers 24 hours a day through the same Integrated Scanning
System (ISS) as the truck containers except for the gate point. The transfer truck will be
assumed as server in the transfer system and will be added as many as needed to provide
the service.
The increase of transshipment containers affects the arrival rate from 1.62 containers per
hour to 3.25 containers per hour on the small port case. In this sense, the NII scanner still
was able to cover the demands of service throughout the three day analysis. However,
during the busiest periods of the day, the peak arrivals over passed the service capacity of
the scanner, resulting in an increase of the M factor up to three containers at the highest
point, but decreasing in the slow shift of the next day. Another important figure to remark
is that the “L” factor or number of containers in average in the system grew from 0.48
containers per hour to 1.86 containers per average in the NII system. In the case of the
transfer service, the “Lq” factor or transshipment containers in waiting line considerably
accounts by 3.5 containers per hour with a utilization factor of 81.5% of the transfer
truck.
For the port of Hong Kong, the arrival rate including the transshipment flow to the system
increased from 6.14 considering only Gate flow to 8.8 containers per hour. In the case of
one NII scanner was not sufficient to support only the gate flow, including the
transshipment only produced the increase of speed on queue represented by the M factor
in the graph, reaching a 3.8 container rate increments per hour. In the case of the scenario
of a Big port with two NII systems, the impact of the transhipment flows affected
substantially the performance of the NII scanners. Figure 6.9 presents the impact of the
transhipment flows to the NII system of the Big port when two NII scanners are utilized
to provide the Service.
74
45.00
40.00
Arrival Average
35.00
Peak Arrivals
Peak M Factor
Containers
30.00
Average M Factor
Service Rate
25.00
20.00
15.00
10.00
5.00
7:
00
10
:0
0
13
:0
0
16
:0
0
19
:0
0
4:
00
1:
00
7:
00
10
:0
0
13
:0
0
16
:0
0
19
:0
0
22
:0
0
4:
00
1:
00
7:
00
10
:0
0
13
:0
0
16
:0
0
19
:0
0
22
:0
0
4:
00
1:
00
0.00
22
:0
0
Figure 6.9
Hours (3 Days)
In this figure we observe a clear effect of the transshipment flow in the system. With the
increase of constant containers from the transshipment flows and the peak arrivals by the
gate, the impact of the congestion is represented by the Peak M factor line (red). This
Peak factor is an additional increase in the queue, when the system is blocked by higher
arrivals than service time. If we only take into account the Queuing formula relation of
arrivals of 8.8 containers per hour and the service time of 10 containers per hour (88%),
we would probably get an erroneous interpretation of the system, assuming that two
scanners were enough to provide the service and representing 6.04 containers in the
waiting line on average. However, in Figure 6.9 shows a higher impact on queues
reaching up to 40 containers on peak hours, signifying an approximate 550 meter length
queue within the terminal and waiting times up to 5 hours. The figure give us a better
approach of the system, proving that the queue of the NII system would not increase
definitely as the queuing model establishes, but how ever considerable peaks are present
that must be considered in the busiest periods of the day. In this sense, this scenario
represents a considerable impact on queues derived from the transshipment flow in the
NII system. In this situation each terminal in the port of Hong Kong might consider the
possibility to buy an extra NII scanner to avoid high waiting lines in order to provide
better service. Also another important situation to account is the assumption of uniform
exports throughout the 9 terminals of the port of Hong Kong. If only certain terminal
offers the service for the USA the flows will reallocate disproportional and the impact
would be even harder.
For the case of the transfer service, the arrival time per hour represents 2.6 containers per
hour divided by the service rate which is 2 containers per hour one transfer truck was not
enough to provide the service. With the service of two transfer trucks the average
numbers of containers on queue was 1.07 per hour with a waiting time average of 0.4
hours or 24 minutes. According to the “W” factor in this scenario, each container spends
on average 0.9 hours just in the transfer system until enters to the Integrated Scanning
System (ISS). An extra service time must be considered in order to return the container to
the stacking area. The increase of transfer trucks must be consider carefully, because
adding more trucks represent more traffic within the terminal resulting in more
congestion. In the case of the transfer system, the queues behave for the following
scenarios the same as in scenario 2A for both ports.
75
Scenario 2B: Forecasted container volumes (2012) with current technology and No NII
analysis. Gate and Transshipment flows through the system.
In this scenario is similar as scenario 2A, but with the assumption that No analysis of
image is performed at foreign port. It is assumed that the pictures taken by the NII
equipment will be sent for analysis directly to the US targeting Service Center in Virgina,
USA. The terminal, officers or personnel in charged to perform the scanning, only will be
subject to take the NII image, avoiding further analysis and also avoiding the Second
Inspection of the container. In this scenario the scanning time in the NII system will drop
from 12 minutes to 30 seconds (based on a pass-through scanner similar as the one
described in chapter 4). This simple assumption will launch the service rate from 5
containers per hour with NII analysis to 120 containers per hour, plus the avoidance of
the second inspection system which was blocked since scenario 1A. The service rate
assumed in this scenario has been very conservative because according to some scanner
providers, theoretically new systems are able to reach more than 200 scans per hour in a
constant flow of containers (Carluer [2008]). Figure 6.10 presents the new state of the
queues for a big port with a skyrocket service increase derived from the No Analysis
assumption requiring only one NII scanner to provide the service.
Figure 6.10
Container arrivals and service capacity Big Port scenario 2B with one NII and No Image Analysis
140.00
120.00
AVERAGE
ARRIVAL
CONTAINERS
100.00
PEAK ARRIVAL
80.00
60.00
M FACTOR
(PEAK)
40.00
M FACTOR
(Average)
20.00
0.00
22
:0
0
2:
00
6:
00
10
:0
0
14
:0
0
18
:0
0
22
:0
0
2:
00
6:
00
10
:0
0
14
:0
0
18
:0
0
22
:0
0
2:
00
6:
00
10
:0
0
14
:0
0
18
:0
0
SERVICE
HOURS (3 Days)
If we compare figure 6.10 with Figure 6.9 which both represent the same arrivals, the
improvement on capacity under scenario 2B is remarkable. The difference between the
service rate and the arrival time is six to one at the highest peak arrival point. The
percentage factor of utilization of the NII scanner barely reaches the 7 percent and no
waiting line was presented in this scenario. The necessity to add a second scanner is
useless because with only one NII equipment is enough to provide the service for the gate
and transshipment container flows. For the case of a small port the positive effect is even
more due the lower container flows.
76
Scenario 2C: Forecasted container volumes (2012) with new technology and NII
analysis. Gate and Transshipment flows through the system.
In this scenario is assumed that the technology will be improved within these years until
the legislation is implemented in 2012. In this scenario is not speculative at all with the
possible developments made by scientists and security system developers. The
assumptions made to conduct the queuing analysis in this scenario are based in recent
available technology which has recently launched or has not been proved by the
Department of Homeland Security (DHS). In Table 6.12 are summarized the
technological assumptions that will be considered in this scenario.
Table 6.12
Assumed Technology improvements for 2012
Technology Improvements
ASP system
Area
Effect
Second and Alarm
Service time from 4 days to 10
Inspections
minutes
Picture Improvement with color tech of
NII inspection with analysis from 12
materials
NII
Radio Frequency Identification (RFID)
Gate
minutes to 6 minutes
Pass-through Gate registration from
Faster transfer times or specialized
transshipment NII system
1 minute to 30 seconds
From 30 minute transfer to 6 minute
Transshipments
transfer
Source: Own Assumptions.
With these technological assumptions all the systems have been improved in their
capacity rate in our analysis. With these feasible developments in the technology of
security systems, it can be reached a stable performance of the system without sacrifice
any of the inspection points of the Integrated Scanning System (ISS). Table 6.13 are
summarized the operational characteristics of the system, based in a big port with only
one NII scanner.
77
Table 6.13
Operating Characteristics M/M/1 of Queuing Model for Big Port Scenario 2c with One NII scanner
Alarm
Factor
Transfer
Gate
RPM
2nd
Inspection
NII (6
Inspection
(1.5%)
min)
(1%)
Servers
K
1.00
2.00
2.00
1.00
1.00
1.00
Λ
2.67
47.22
49.88
0.75
8.80
0.09
Utilization factor
Μ
6.00
150.00
300.00
6.00
10.00
6.00
(λ/µ)
44%
16%
8%
12%
88%
1%
Lq
0.36
0.01
0.00
0.02
6.45
0.00
L
0.80
0.32
0.17
0.14
7.33
0.01
Wq
0.13
0.00
0.00
0.02
0.73
0.00
W
0.30
0.01
0.00
0.19
0.83
0.17
Pq
0.45
0.04
0.01
0.13
0.88
0.01
Mq
0.00
0.00
0.00
0.00
0.00
0.00
Ll
13.75
13.75
13.75
13.75
100.86
13.75
La
47.29
47.29
47.29
47.29
346.81
47.29
waiting line
system
the waiting line (Minutes)
the system
queue
period when λ>µk
System (Mts)
Average Occupying Area of
Containers in System (Mts^2)
As we can see from the previous Table 6.13, all the systems are functioning properly.
One of the most important achievements with these tech innovations is the feasibility to
perform Alarm and Second Inspections with ASP technology, briefly described in chapter
3. The ASP technology is already available but relatively expensive at the moment. If the
legislation is implemented in 2012, the necessity to include ASP systems in foreign ports
must be considered seriously. Otherwise the arrival rate of alarm and second inspections
will overpass the capacity of service unless more personnel would be increased. The
transshipment transfer is assumed to reach in 2012 the same service rate than the current
NII system (5 services per hour) or develop a new scanning system specifically made for
transshipments that will improve its capacity. This is the major challenge for the security
equipment developers derived the limited space of operation within the terminals. If is not
possible to achieve remarkable improvements to provide service to transshipment
containers, the implementation of the legislation could be on jeopardy.
In the case of Gate and RPM, both checkpoints have not been major threats for the
operation of the system and either under this scenario. The gate check point varies
enormously between ports and could include several activities in the terminal like
customs procedures, physical inspection of the container and among others. In the case of
the NII system, massive scanning is currently possible, but the analysis of the human
factor distorts the capacity of the system. In order to improve the capacity of analysis,
78
definitely we can not assume that in 2012 better humans will be developed who would
analyze the images faster than today’s analysts. However, with the aid of the technology,
better equipment can be developed to facilitate the analysis of the images. These
improvements so far include but are not limitative to color differentiation of materials in
the images by the NII systems and more powerful scanners providing clearer images,
among other technological features. In this scenario is assumed that the analysis made by
the human factor can be tackled in half of the current time spend on the analysis. Even
though, if greater improvements are achieved during upcoming years, more impact on the
service capacity will be made lowering waiting lines. Accordingly to all our assumptions,
Figure 6.11 shows the behaviors of the system with new technology on a big port with
one NII scanner.
Container arrivals and service capacity Big Port scenario 2c with one NII and New Technology
45.00
40.00
AVERAGE
ARRIVAL
35.00
CONTAINERS
30.00
PEAK
ARRIVAL
25.00
M FACTOR
(PEAK)
20.00
15.00
M FACTOR
(Average)
10.00
5.00
SERVICE
Source: own calculations.
18
:0
0
14
:0
0
10
:0
0
6:
00
2:
00
22
:0
0
18
:0
0
14
:0
0
10
:0
0
6:
00
2:
00
22
:0
0
18
:0
0
14
:0
0
10
:0
0
6:
00
2:
00
0.00
22
:0
0
Figure 6.11
HOURS (3 Days)
The graph represents the performance of the arrivals and possible queues at peak hours
with new technology improved. The figure 6.11 has a similar behavior as the figure 6.9 of
scenario 2A, with the difference that in this scenario only one NII scanner has been used.
Even though, this scenario is not as efficient as scenario 2B, this one does not sacrifice
any inspection points like the NII analysis or the Second Inspection. The terminal or the
port authority should evaluate the possibility to include a second scanner to provide a
better service to the containers in order to avoid high peaks in the system as the M Peak
Factor shown. If one more NII scanner is included this M Factor disappears completely,
rising the service line to a 20 containers per hour. For the case of the small port, one NII
scanner is more than enough to cover the forecasted flows of containers through the gate
and transshipments.
Scenario 2d: Forecasted container volumes (2012) with new technology and NII analysis.
Gate and Transshipment flows and Hub and Spoke system.
In this scenario is considered the possible impact of the legislation if the small ports are
not able to acquire an Integrated Scanning System to provide a NII service to its
customers. The feasibility to implement an integrated scanning system in a port not only
depends in the economic cost by itself, but also in the efficiency to provide the service.
For example, in the case of the port of Dubai, we are assuming that the level of container
flows to the United States, represents almost 20,000 containers per year. Definitely the
United Arab Emirates have economic solvency to buy any NII system in the market and
as many scanners as they want, but its level of U.S. bound container flow do not justify
79
the investment unless higher flows are attracted. In this assumption the U.S. bound
container flows of the United Arab Emirates would be redistributed between other Asian
ports in order to be transshipped to the United States. Once again the port of Dubai is
only represents dozens of small ports based in the container flows to the United States, no
by its capacity as a whole.
Our first assumption in our model is to determine which countries can afford an
Integrated Scanning System. To do so, the countries will be separated in two blocks: Big
ports and Small ports. The base line to make this distinction will be the barrier of 25,000
US-bound containers per year according to the volumes of 2007 by PIERS (2008)
forecasted for the year 2012. In this sense, only the top 54 trading partners of United
States are above this figure, from China as a principal partner through Ireland, including
Hong Kong, which will represent a big port able to buy an NII system. From the countries
55 to 176, including United Arab Emirates, these ports will be considered as small ports
unable to acquire a NII system due capacity reasons. According to PIERS (2008), the big
ports represent almost 97% of the total U.S. container market and the rest (the small
ports) account only by a little more than 3%. This percentage of small port U.S. bound
container flow will represent in 2012 a total captive market of 493,503 containers per
year to be split between the big ports based on a Hub and Spoke system. In order to find a
methodology to reallocate this flow of captive containers, all the Countries have been
separated by Continent, with the exception of America which we sub divided in two
regions: North and South America. According to this differentiation the Big ports from
each continent will be ranked based on the total US market share from its own continent.
With this assumption the ports of one continent only compete against other countries
from the same continent to attract exclusively container flows from the small ports of
their own continent. With these assumptions we generate the Table 6.14.
Table 6.14
Forecasted Hub and Spoke effect per continent 2012
Captive
Container
Percentage of Increase in
Countries competing
flow 2012
Transshipment per continent
for captive market
Asia
83,367
0.48%
18
Africa
48,476
45%
2
Europe
151,967
5%
16
North America
96,536
9%
9
South America
63,303
5%
7
Oceania
49,854
24%
2
Total
493,503
Continent
54
Valuable remarks are obtained from Table 6.14. First of all the total captive container
flow through out the world will represent in 2012 almost 500,000 containers per year,
only 3% of the total US market flow. It seems like a not significant figure, but if the
Table is analyzed per continent, important conclusions can be made. In the case of Asia
only 83,367 containers will be captive if the 100% Container Scanning legislation is
implemented. Because the majority of the Asian ports have the highest volumes of US
bound, almost of all them are able to implement their own NII system, without the
80
necessity to reallocate their US exports to another port. These 83,367 containers only
represent an increase of 0.48% for the Asian market divided in 18 possible countries who
will try to attract those container flows. Then for the Asian market, the Hub and Spoke
effect does not seems a very attractive situation. Currently countries like Philippines and
Vietnam have announced the order of new NII equipment to be ready when the
legislation enters in 2012.
On the other hand, for the case of Africa, the volume of containers is almost half of the
captive market in Asia. However, for the African continent represent an increase of 45%
of their current US-bound container market. Besides the huge increase in the U.S. market
that the Hub and Spoke effect would represent for Africa, the only possible countries that
could acquire the NII system would be South Africa and Egypt. Then for the African
ports, the Hub and Spoke effect that the legislation will represent a more attractive
scenario than for the other ports in the rest of the world. In the case of Oceania, the effect
would be similar as for Africa with an increase of 24% of the current US bound container
market also with only two countries competing to attract the captive container flows of its
continent.
For the case of Europe, the possible outcome of attraction flows form European countries
would depend on the current behaviors of the market share in their continent. An
important observation should be noticed for the European market, even though the Hub
and Spoke effect only represents 5% of increase in the market, this continent is the
highest captive market in absolute terms with a total of 151,967 containers in 2012. The
total players competing for these container flows are 16 European countries. In this sense,
the most probable outcome would be that the major ports like Rotterdam, Antwerp,
Hamburg and probably Algeciras due its location in the Mediterranean sea would attract
the major part of these flows.
Finally for the case of North and South America, the scenario seems steady with a
possible average increase of 1% per country of their current US-bound flow. Counties
like Mexico and Canada, should be careful with their expectations of the Hub and Spoke
effect. These countries have announced major investments to attract container flows
coming from Southeast Asia. Not by coincidence, Mexico’s huge project of Puerto
Colonet planned to be built in Baja California, a Northeastern state bordering with the
American state of California, is expected to start in 2012 in order to attract container
flows from Asia bounding to United States. Even though these Countries have not
expressed directly their intention to become Hub and Spoke ports able to provide NII
service, the efforts for attract Asian flows destined to the United States are evident. With
this limited analysis, has been estimated an initial approximation of the behavior of the
Hub and Spoke effect of the Legislation. For further analysis which are out of scope in
this study, Gravity Models must been applied in order to measure a closer estimation of
the reallocation of the container flows due the Hub and Spoke effect.
81
Figure 6.12
Container arrivals and service capacity Big Port scenario 2d with one NII and New Technology and Hub and
Spoke effect
45.00
40.00
35.00
CONTAINERS
30.00
25.00
AVERAGE
ARRIVAL
20.00
PEAK ARRIVAL
15.00
10.00
M FACTOR
(PEAK)
5.00
M FACTOR
(Average)
HOURS (3 Days)
18
:0
0
14
:0
0
10
:0
0
6:
00
2:
00
22
:0
0
18
:0
0
14
:0
0
6:
00
10
:0
0
2:
00
22
:0
0
18
:0
0
14
:0
0
6:
00
10
:0
0
2:
00
22
:0
0
0.00
SERVICE
Returning to the Hub and Spoke effect in the Big port example, Hong Kong represents
only 4.67% of the total Asian US container market. According this share, the reallocation
of the captive container flows will only represent 3,088 container increases per year. In
this example the container flow per hour will increase only by one container, not a
significant effect of the Hub and Spoke system. Figure 6.12 (above) shows the effect on
the arrivals and waiting lines only for the Hong Kong port. As we can see, Figure 6.12 is
almost identical as Figure 6.11, meaning that for the Big Asian port, the Hub and Spoke
does not represent an attractive market.
6.4
Results and analysis of the Simulation Model
In Section 6.4 we applied M/M/1 and M/M/k queuing formulas useful to determine the
steady state of the operating characteristics of the Integrated Scanning System (ISS).
These types of Models are called static because the results of one trial do not affect the
result of the following trial, which means that there is any change or evolution over time.
In order to have a closer look of the behaviors of the Waiting Lines and in order to
consider the fluctuations of container arrivals in Peak Time throughout the gate, this
section will be used a different quantitative approach called Waiting Line Simulation
Model. Different from the previous models, the Waiting Line Simulation is a dynamic
model because considers one event trial affecting the result of the following event. With
the aid of a simulated clock the container arrivals can be recorded changing over time the
state of the system considering if a channel (Gate, RPM or NII) is busy or available to
provide the service. The Simulation is a model experiments the operation of a real system
combining controllable inputs, like the NII scanners used to provide de service, and
randomly generated values for the probabilistic inputs, like container arrivals and service
times (Anderson, Sweeny, Williams and Martin (2008). This approach is commonly used
to assess complex systems where queuing formulas are not available or are too complex
to measure the operation of the system.
82
Scenario 1a: Forecasted container volumes (2012) with current technology and NII (Non
Intrusive Inspection) analysis. Only Gate flows through the system. Simulation Model.
The most complex condition to assess in any waiting line system is the variations of
costumer arrivals at peak times. These disproportionate fluctuations of arrivals produce
the disruption of a waiting line system. In order to provide a model close to reality to our
Simulation Model, we are including the behavior of peak arrivals at gate suggested by
Saanen (2008). The author separates the arrival of container flows in three shifts. The first
shift starting from 22:00 hours to 6:00 hours of the next day represents 5% of the total
container flow through the gate. The second shift from 6:00 hours to 14:00 hours there is
an increase of container flow accounting a total of 40% of the total flow of the day.
Finally, the third shift and the most packed one starts from 14:00 hours to 22:00 hours
which represents the rest 55% of the total flow. Due the difference of fluctuation arrivals
between ports around the world, this study is basing the arrival pattern suggested by
Saanen, but can be tailored to any specific port throughout the record of arrivals during a
certain period of time. These peak fluctuations could be measured either by day, as we
assume in this study or by season where higher container volumes change over time on
certain months of year. Table 6.15 shows the allocation of the daily average containers of
the port of Dubai.
Table 6.15
Container allocation per shift in peak time of a Small port
Shift
Time (Minutes)
Peak %
Total Containers per Shift
Shift C (22:00-6:00hrs)
480
5%
2
Shift B (6:00-14:00hrs)
480
40%
16
Shift A (14:00-22:00hrs)
480
55%
22
Totals
1440
100%
40
This model is based on exponential arrival times, general service times and one or
multiple service channels. In this model we are assuming a general probabilistic
distribution to represent the behavior of the service times, because the highest probability
of density is at or near its mean. In simple words, this indicates that the service time
performed by any server in the ISS will perform service times close or at its average
service time (Mean). There is a very unlikely probability that a NII scanner service time
performs a 1 minute service were the Mean service time is 12 minutes (in the case of
Scenarios 1a and 2a). Table 6.16 shows the parameters used in our example of Model
Simulation for a Small port under scenario 1A.
Table 6.16
Probabilistic parameters for Simulation Model Small port Scenario 1A
Interarrival Times (Exponential Distribution)
Shift C
Shift B
Shift A
Mean
240.00
30.00
21.82
Service Times (Normal Distribution)
Gate
RPM
NII
Mean
1.00
0.40
12.00
Standard Deviation
0.05
0.01
0.50
83
Table 6.17 shows the calculations of the simulation model for the average arrivals (40
containers per day) for the port of Dubai through the ISS in scenario 1A. In order to
provide an easy to read table, rows of containers from 11 to 35 are hided.
84
Table 6.17
Simulation Table for the Integrated Inspection System, Small port scenario 1A (Stage 1: Gate System)
Service Start
Waiting
Completion
Time in Gate
Container No.
Interarrival
Time
Arrival time
Time
Time
Service Time
Time
System
Shift C
1
3:09:45
1:09:45
1:09:45
0:00:00
0:01:02
1:10:47
0:01:02
22:00hrs – 6:00hrs
2
4:39:39
5:49:24
5:49:24
0:00:00
0:00:57
5:50:21
0:00:57
Shift B
3
0:22:43
6:12:07
6:12:07
0:00:00
0:00:59
6:13:06
0:00:59
6:00hrs – 14:00 hrs
4
0:24:46
6:36:53
6:36:53
0:00:00
0:00:57
6:37:51
0:00:57
5
0:09:40
6:46:33
6:46:33
0:00:00
0:01:05
6:47:38
0:01:05
6
0:15:21
7:01:54
7:01:54
0:00:00
0:00:59
7:02:53
0:00:59
7
0:12:26
7:14:20
7:14:20
0:00:00
0:00:57
7:15:17
0:00:57
8
0:22:27
7:36:47
7:36:47
0:00:00
0:01:06
7:37:53
0:01:06
9
0:36:33
8:13:21
8:13:21
0:00:00
0:00:55
8:14:16
0:00:55
Shift
10
0:13:34
8:26:55
8:26:55
0:00:00
0:00:58
8:27:53
0:00:58
Shift A
36
0:04:57
18:26:38
18:26:38
0:00:00
0:01:04
18:27:42
0:01:04
14:00 hrs to 22:00hrs
37
0:46:41
19:13:19
19:13:19
0:00:00
0:01:03
19:14:22
0:01:03
38
0:06:52
19:20:11
19:20:11
0:00:00
0:00:56
19:21:07
0:00:56
39
0:03:24
19:23:36
19:23:36
0:00:00
0:01:05
19:24:41
0:01:05
40
0:00:33
19:24:08
19:24:41
0:00:32
0:01:00
19:25:40
0:01:32
Summary Statistics
Gate
Number Waiting
1
Probability of Waiting
3%
Average Waiting Time
0:00:01
Maximum Waiting Time
0:00:32
Utilization of Gate
4%
Number Waiting > 1 Minute
0
Probability of Waiting > 1 Minute
0%
85
Table 6.17
Simulation Table for the Integrated Inspection System, Small port scenario 1A (Stage 2: RPM system)
Time in
Interarrival
Waiting
Service
RPM
Container No.
Time
Arrival time
Start Time
Time
Time
Completion Time
System
Shift C
1
0:00:32
1:11:19
1:11:19
0:00:00
0:00:23
1:11:43
0:00:23
22:00hrs - 6:00hrs
2
0:00:37
5:50:59
5:50:59
0:00:00
0:00:25
5:51:23
0:00:25
Shift B
3
0:01:25
6:14:32
6:14:32
0:00:00
0:00:25
6:14:56
0:00:25
6:00hrs - 14:00 hrs
4
0:00:40
6:38:30
6:38:30
0:00:00
0:00:24
6:38:54
0:00:24
5
0:01:46
6:49:24
6:49:24
0:00:00
0:00:24
6:49:48
0:00:24
6
0:01:31
7:04:25
7:04:25
0:00:00
0:00:25
7:04:49
0:00:25
7
0:01:13
7:16:30
7:16:30
0:00:00
0:00:24
7:16:54
0:00:24
8
0:01:53
7:39:46
7:39:46
0:00:00
0:00:24
7:40:09
0:00:24
9
0:00:53
8:15:08
8:15:08
0:00:00
0:00:25
8:15:33
0:00:25
10
0:00:22
8:28:15
8:28:15
0:00:00
0:00:24
8:28:39
0:00:24
Shift A
36
0:00:55
18:28:37
18:28:37
0:00:00
0:00:24
18:29:02
0:00:24
14:00 hrs to 22:00hrs
37
0:00:37
19:14:59
19:14:59
0:00:00
0:00:24
19:15:23
0:00:24
38
0:00:03
19:21:10
19:21:10
0:00:00
0:00:24
19:21:35
0:00:24
39
0:00:46
19:25:27
19:25:27
0:00:00
0:00:23
19:25:50
0:00:23
40
0:01:07
19:26:47
19:26:47
0:00:00
0:00:24
19:27:11
0:00:24
Shift
Summary Statistics
RPM
Number Waiting
0
0%
0:00:00
0:00:00
Utilization of RPM
1%
Number Waiting > 0.4 Min
0
Probability of Waiting > 0.4 Min
0%
86
Service
Table 6.17
Simulation Table for the Integrated Inspection System, Small port scenario 1A (NII system)
Container
Interarrival
Arrival
Service
Waiting
Service
Completion
Time in NII
End Total
Average
time in
Time ISS
No.
Time
time
Start Time
Time
Time
Time
System
ISS
System
Shift C
1
0:00:02
1:11:45
1:11:45
0:00:00
0:12:29
1:24:14
0:12:29
0:14:29
0:18:39
22:00hrs – 6:00hrs
2
0:01:41
5:53:05
5:53:05
0:00:00
0:12:14
6:05:18
0:12:14
0:15:54
0:18:39
Shift B
3
0:01:15
6:16:11
6:16:11
0:00:00
0:12:40
6:28:51
0:12:40
0:16:44
0:18:39
6:00hrs – 14:00 hrs
4
0:01:45
6:40:39
6:40:39
0:00:00
0:11:49
6:52:28
0:11:49
0:15:35
0:18:39
5
0:00:11
6:49:59
6:52:28
0:02:29
0:11:30
7:03:58
0:13:59
0:17:25
0:18:39
6
0:01:07
7:05:57
7:05:57
0:00:00
0:11:58
7:17:55
0:11:58
0:16:00
0:18:39
7
0:00:32
7:17:26
7:17:55
0:00:29
0:11:58
7:29:52
0:12:27
0:15:32
0:18:39
8
0:01:45
7:41:54
7:41:54
0:00:00
0:11:43
7:53:37
0:11:43
0:16:50
0:18:39
9
0:00:22
8:15:54
8:15:54
0:00:00
0:10:56
8:26:51
0:10:56
0:13:30
0:18:39
10
0:01:26
8:30:05
8:30:05
0:00:00
0:11:45
8:41:50
0:11:45
0:14:55
0:18:39
Shift A
36
0:01:27
18:30:29
18:36:19
0:05:50
0:11:40
18:48:00
0:17:31
0:21:22
0:18:39
14:00 hrs to 22:00hrs
37
0:00:04
19:15:27
19:15:27
0:00:00
0:11:36
19:27:04
0:11:36
0:13:44
0:18:39
38
0:01:30
19:23:04
19:27:04
0:03:59
0:12:07
19:39:10
0:16:06
0:18:59
0:18:39
39
0:01:33
19:27:23
19:39:10
0:11:47
0:11:55
19:51:06
0:23:42
0:27:30
0:18:39
40
0:00:48
19:27:59
19:51:06
0:23:07
0:12:08
20:03:14
0:35:15
0:39:06
0:18:39
Shift
Summary Statistics
NII
Number Waiting
17
43%
0:03:17
0:23:07
Utilization of NII
43%
Number Waiting > 12 Min
0
Probability of Waiting > 12 Min
0%
87
For easier analysis Figure 6.13 shows the behavior of waiting times for each container
throughout the day. In this figure we can observe the tendency of the behavior of the
waiting of the average arrivals of containers per day. Due simulation is a dynamic model
we can have different outputs from the same scenario. When an experiment of a
simulation model is performed is called “Simulation Run”. Every time we conduct a
simulation run, the graph form and the outcomes of the model will be different from the
previous one. This dynamism of the model is derived from the probabilistic random
numbers utilized by computational spreadsheets to estimate the interarrival time and the
service time of each container. In the figures 6.14 and 6.15 represent a second and a third
run simulation respectively in order to illustrate the different outcome forms resulting
from the same model.
Figure 6.13
Simulation model small port scenario 1A (First Run)
Total Time in System
1:20:00
1:10:00
Total Time in NII
Time (Minutes)
1:00:00
Queue Time in NII
0:50:00
0:40:01
Average Time in System
0:30:01
0:20:01
0:10:01
25
27
29
31
33
35
37
39
25
27
29
31
33
35
37
39
23
21
19
17
15
13
11
9
7
5
3
1
0:00:01
Containers per Day
Figure 6.14
Simulation model small port scenario 1A (Second Run)
1:20:00
1:10:00
Total Time in NII
Time (Minutes)
1:00:00
Queue Time in NII
0:50:00
0:40:01
0:30:01
0:20:01
0:10:01
23
21
19
17
15
13
11
9
7
5
3
1
0:00:01
Containers per Day
88
Figure 6.15
Simulation model small port scenario 1A (Third Run)
1:20:00
1:10:00
Total Time in NII
Time (Minutes)
1:00:00
Queue Time in NII
0:50:00
0:40:01
0:30:01
0:20:01
0:10:01
39
37
35
33
31
29
27
25
23
21
19
17
15
13
11
9
7
5
3
1
0:00:01
Containers per Day
Now is the turn to analyze the Simulation of the big port under scenario 1A. In the case of
Hong Kong Table 6.18 shows the allocation of containers per shift throughout the day.
Table 6.18
Container allocation per shift in peak time of a Big port
Shift
Time (Minutes)
Peak %
Total Containers per shift
Shift C (22:00-6:00hrs)
480
5%
7
Shift B (6:00-14:00hrs)
480
40%
59
Shift A (14:00-22:00hrs)
480
55%
81
Totals
1440
100%
147
Table 6.19 shows the parameters used in our example of Model Simulation for a Big port
under scenario 1A with two NII scanners.
Table 6.19
Probabilistic parameters for Simulation Model Big port Scenario 1A
Interarrival Times (Exponential Distribution)
Shift C
Shift B
Shift A
Mean
65.20
8.15
5.93
Service Times (Normal Distribution)
Gate
RPM
NII
Mean
1.00
0.40
12.00
Standard Deviation
0.05
0.01
0.50
With these assumptions is conducted the Simulation model for the 147 containers per day
in the Big port in scenario 1A. The operating characteristics of the simulation model are
summarized in Table 6.20. In this scenario the Big port presents a maximum waiting time
in the NII system of 1 hour 21 minutes 21 seconds and 86 containers waiting more than
12 minutes in queue.
89
Table 6.20
Operating statistics of Big port scenario 1a, Simulation Model (One run)
Summary Statistics
Gate
RPM
NII
28
12
111
19.02%
8.15%
75.38%
Number Waiting
0:00:07
0:00:02
0:22:49
0:01:42
0:01:07
1:21:21
Utilization of Gate
11.99%
4.80%
138.56%
Number Waiting > 1 Min
Probability of Waiting > 1/0.4/12 Min
2
6
86
1.36%
4.07%
58.40%
Figure 6.16 shows the behaviour of the waiting lines of a Big port under scenario 1A with
two NII scanners.
Figure 6.16
Simulation Model big port scenario 1A two NII (One Run)
2:00:00
1:50:00
1:40:00
Time in NII System
1:30:00
Queue Time
Time
1:20:00
1:10:00
1:00:00
0:50:00
0:40:00
0:30:00
0:20:00
0:10:00
Container Arrival per Day
In this figure we see a marked tendency of the queuing time of the containers in Shift A
starting at container 86. Under this scenario the average time in system reaches almost 40
minutes per container. In this figure we observe the same fluctuations of container
arrivals suggested by Saanen (2008). As well as for the Small port, this model also
evolves over time and different outcomes are obtained from the same model. Due the
dynamic of the model, if we only consider only one simulation run to analyze the
behavior of the waiting lines, we could have a wrong assessment of the system. This is
because the probabilities of distribution one simulation model experiment do not
represent the reality of the system. In order to avoid this erroneous approach, it is
necessary to test the model through constant simulation trials. As many possible
observations are conducted more likely the results will represent the reality of the system.
With the aid of specialized spreadsheets add-ins software like Crystal Ball from
Decisioneering and @RISK from Pailsade Corporation (Anderson, Sweeny, Williams,
Martin [2008 p. 618]) we can conduct iterative simulations runs as needed. In our
analysis is considered a 7 consecutive day trail in order to capture a closer estimate of the
reality.
90
14
5
13
9
12
7
13
3
12
1
11
5
10
9
97
10
3
91
85
79
73
67
61
49
55
43
37
31
25
19
7
13
1
0:00:00
After the seven days of trial starting at 22:00 hours, seven days of simulation runs were
registered with 147 container records for each run. From the total runs and records of the
seven day trial, averages of each container were calculated in order to have a final table
with average numbers of each container arrival. The summary statistics of the 7 day trial
for the Big port are shown in Table 6.21. With this new statistics we can graph our final
simulation tested model for scenario 1A for the Big port with two NII scanners
Table 6.21
Operating Characteristics of a seven day trial of simulation Big port scenario 1A
Day
1
2
3
4
5
6
7
Average
Summary Statistics
NII
NII
NII
NII
NII
NII
NII
NII
Number Waiting
106
121
108
108
130
114
107
113.43
72%
82%
73%
73%
88%
77%
73%
77%
0:23:13
0:42:55
0:16:04
0:18:17
0:37:35
0:29:49
0:29:45
0:28:14
1:26:14
1:58:04
1:12:07
1:08:12
2:04:12
1:24:15
1:25:01
1:31:09
Utilization of NII Scanner
1.33
1.56
1.67
1.10
1.27
1.16
1.20
1.33
80
94
66
80
98
84
83
83.57
54%
64%
45%
54%
67%
57%
56%
57%
Number Waiting
> 1/0.4/12 Minutes
> 1/0.4/12 Minutes
7 day trial Simulation Model big port scenario 1a two NII (Tested Model)
120.00
100.00
Total Time in NII
Time (Minutes)
80.00
Queue Time in NII
60.00
Average Time in
System
40.00
20.00
14
1
13
4
12
7
12
0
11
3
99
10
6
92
85
78
71
64
57
50
43
36
29
22
8
15
0.00
1
Figure 6.17
Containers per Day
Figures 6.16 and 6.17 were obtained from the same scenario and from the same model.
However, the tested simulation model represents the results of the 7 day trial. In Figure
6.17 is observed a marked growth tendency of container waiting time during the day
starting from container number 59 until container 144 were the waiting time goes down
again. The total time spent in the NII system almost reaches the 100 minute by the
container number 142. By the final containers the queue starts decreasing and merging
with shift C of the next day.
The same simulation test process was made for the port of Dubai. For the following
scenarios, the same procedure was followed like in scenario 1A. In order to simplify the
91
analysis of our study we will present the results of the Tested Simulation models
conducted in our analysis.
Scenario 2a: Forecasted container volumes (2012) with current technology and NII (Non
Intrusive Inspection) analysis. Gate and Transshipment flows through the system.
Simulation Model.
In Figure 6.18 shows the behaviour of the waiting lines from scenario 2B for the Big port
with two NII scanners providing the service. The graph illustrates an almost linear growth
of the queue lines in the Non Intrusive Inspection System (NII). The average time spent
in the system by each container in Scenario 2A is above the 70 minutes per container.
However, the container number 1 spends around 12 minutes in the system, while
container 212 spent almost 200 minutes.
Figure 6.18
7 day trial Simulation Model Big port scenario 2A two NII (Test Model)
250
Time in NII
Time (Minutes)
200
Queue Time in NII
150
Average Time in
System
100
50
16
9
17
6
18
3
19
0
19
7
20
4
21
1
10
6
11
3
12
0
12
7
13
4
14
1
14
8
15
5
16
2
92
99
78
85
71
57
64
43
50
29
36
15
22
1
8
0
Containers per Day
If we assumed that all 212 containers on average spend around 70 minutes in the system,
we will create a wrong image of the reality. To balance this figures, Table 6.22 shows the
average time spent in the system divided by shifts.
92
Table 6.22
Time spent in System and Queue by container per Shift in Big port scenario 2A
Time in System
Shift
Queue Time
Average Containers per Day
Average
Standard Deviation
Range
Average
Standard Deviation
Range
Shift C (22:00-6:00hrs)
29
13.96
0.90
13.06 - 14.86
0.65
0.84
0 - 1.49
Shift B (6:00-14:00hrs)
81
37.97
9.78
28.19 - 47.75
25.95
9.77
16.18 - 35.72
Shift A (14:00-22:00hrs)
102
116.61
46.28
70.33 - 162.89
104.57
46.26
58.31 - 150.83
93
With the aid of Table 6.22 we have a better approach of the behavior of time spent in
system and queue. Instead of assume that in average all the containers spend around 70
minutes in the NII system, we can state that in Shift C the average of time spent in system
is 13.96 +/- 0.9, in Shift B is 37.97 +/- 9.78 and for time A is 116.61 +/- 46.28 minutes.
For the Queue Time we have in the Shift C an average time of 0.65 +/- 0.84 minutes, for
shift B 25.95 +/- 9.77 minutes and for Shift A 104.57 +/- 46.26. With this analysis we
have a better approach of the time spent in the NII system and the Queue Time during the
day.
Scenario 2b: Forecasted container volumes (2012) with current technology and No NII
analysis. Gate and Transshipment flows through the system. Simulation Model.
In the case of the scenario 2B for the Big port, the avoidance of the human inspection
launches the capacity of the NII scanner to a skyrocket levels. In the Simulation Test
model the behavior of the Time spent in the system and the queuing lines are almost a
straight horizontal line. The variations of the time spent by containers barley differ in half
of minute throughout the day, as shown in Figure 6.19. In this scenario only one NII
scanner is needed in order to provide a steady flow of container through the system.
Among all the scenarios that we have tested, scenario 2b result the most efficient with the
less time spent in Queue and System with the less NII scanners required for the service.
Figure 6.19
7 day trial Simulation Model Big port scenario 2B one NII (Simulation Test)
3
Time (Minutes)
2.5
Total Time in
System
2
Time in NII
1.5
Queue Time in NII
1
0.5
Average Time in
System
0
1
12
23
34
45
56
67
78
89 100 111 122 133 144 155 166 177 188 199 210
Containers per Day
Scenario 2c: Forecasted container volumes (2012) with new technology and NII analysis.
Gate and Transshipment flows through the system.
Similar as scenario 2A, Figure 6.20 shows a linear growth of time spent in system and
queue throughout the day. The difference between Figure 6.20 and 6.28 is that in scenario
2C only one NII scanner was required to provide the scanning service. Under this
scenario the new technology improvements have contributed to the increase in the
capacity of the NII system.
94
Figure 6.20
7 day trial Simulation Model Big port scenario 2C one NII (Tested)
200
Time (Minutes)
180
160
Time in NI
140
Queue Time in NII
120
Average Time in
System
100
80
60
40
20
21
1
20
1
19
1
18
1
17
1
16
1
15
1
14
1
13
1
12
1
11
1
91
10
1
81
71
61
51
41
31
21
11
1
0
Containers per Day
Also under this scenario a shift day analysis is required to asses in order to measure the
waiting time and time spent in system by each container. Table 6.23 summarizes the
average times spent by container in each time of the day.
Table 6.23
Time spent in System and Queue by container per Shift in Big port scenario 2C
Average
Time in System
Container
Shift
Queue Time
Standard
Standard
per Day
Average
Deviation
Range
Average
Deviation
Range
Shift C (22:00-6:00hrs)
29
9.25
1.23
8.02 - 10.48
2.06
1.19
0.87 – 3.25
Shift B (6:00-14:00hrs)
81
33.74
9.96
23.78 - 43.7
26.54
9.94
16.6 – 36.48
Shift A (14:00-22:00hrs)
102
110.18
41.24
68.94 - 151.42
102.96
41.23
61.73 - 144.19
The average Time in System for each shift of the day is: for Shift C 7.12 +/- 0.25
minutes, for Shift B 14.87 +/- 3.32 minutes, and for Shift A 93.61 +/- 40.03 minutes. The
average Time in Queue for each shift of the day is: for Shift C is 0 minutes, for Shift B
7.71 +/- 3.30 minutes and for Shift A is 86.40 +/- 40.09 minutes. If we compare Table
6.23 of scenario 2C with the Table 6.23 of scenario 2a are very similar, with the
difference under scenario 2c Big port with new technology only one NII scanner is
required to perform the service.
Scenario 2d: Forecasted container volumes (2012) with new technology and NII analysis.
Gate and Transshipment flows and Hub and Spoke system.
As mention before the Hub and Spoke effect, which also assumes new technology, do not
represent a big impact for Asian ports like Hong Kong. Under this scenario the increase
in the container flow was only one container per hour. Thus, similar operational
characteristics like scenario 2C are found. In Figure 6.21 is graph the Hub and Spoke
effect, considering new technology and two NII scanners to provide the service.
95
Figure 6.21
7 day trial Simulation Model Big port scenario 2D two NII (Tested)
14
Total Tim e in Sys tem
12
Time (Minutes)
10
Tim e in NII
8
6
Tim e in Queue
4
Average Tim e in System
2
19
9
20
8
18
1
19
0
17
2
16
3
14
5
15
4
13
6
11
8
12
7
10
0
10
9
82
91
73
64
46
55
37
28
10
19
1
0
Containers per Day
As we observe in Figure 6.21 the aid of one extra NII scanner assuming new technology
provides a steady flow of container flows through the NII system all day long. The
average time of service estimated in scenario 2D is around the 8 minutes in NII system.
As we mentioned before, scenario 2C assumes almost the same flow of containers with
only one extra container in Scenario 2D. If we observe Figure 6.20 and Figure 6.21 we
can assume the huge impact that an additional NII scanner with new technology might
have to the container flow, reducing the average time spent in system from 70 minutes
per container to 8 minutes per container with two NII scanners.
6.5
Model Comparison and Waiting Time Analysis
In this section we will compare the two approaches utilized in our study and analyzed the
waiting times of the scenarios. The two quantitative models applied in previous sections
rather than compete they complement each other the study of a waiting line system. As
mentioned before the Queuing model is an optimization approach which bases its
calculations on averages in order to provide estimates of the operation characteristics of
the system. On the other hand the Simulation model is a dynamic approach which
analyzes on detail the waiting line behaviors in a system taking into account the possible
fluctuations of arrivals in the system. Both models are correctly applied assuming
different approaches of the analysis of the system. The idea to compare both models do
not obey the intention to select which one is better than the other, but to compare the
different approaches of each model in order to have a comprehensive analysis. Table 6.24
compares the waiting times assessed by both models.
96
Table 6.24
Models’ Comparison by Scenario
Scenario 1a
Small Port
Description
Scenario 2a
Big port
Small Port
Big port
Queuing
Simulation
Queuing
Simulation
Queuing
Simulation
Queuing
Simulation
1.00
1.00
2.00
2.00
1.00
1.00
2.00
2.00
1.62
1.62
6.14
6.14
3.25
3.25
8.80
8.80
5.00
5.00
10.00
10.00
5.00
5.00
10.00
10.00
32.44%
43.01%
61.35%
132.86%
65.01%
66.85%
88.03%
158%
5.74
3.87
7.27
28.24
22.28
13.74
41.18
60.32
17.74
19.27
19.27
43.73
34.28
27.13
53.18
73.72
Number of
NII
Scanners
(k)
Mean of
Container
Arrivals per
hour (λ)
Mean of
Services
per hour per
total
scanners
(µk)
Percentage
of Utilization
of NII
scanners
(λ/µ)
Average
time a
container
spends in
Queue (Wq)
Average
Time a
container
spends in
System (W)
97
Table 6.24
Models’ Comparison by Scenario (continue 1)
Scenario 2b
Small Port
Description
Scenario 2c
Big port
Small Port
Big port
Queuing
Simulation
Queuing
Simulation
Queuing
Simulation
Queuing
Simulation
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
3.25
3.25
8.80
8.80
3.25
3.25
8.80
8.80
120.00
120.00
120.00
120.00
10.00
10.00
10.00
10.00
2.71%
2.92%
7.34%
7.84%
32.51%
34.53%
88.03%
83.37%
0.01
0.03
0.04
0.06
2.89
1.78
43.99
59.96
0.51
0.54
0.54
0.56
8.89
9.02
49.99
67.17
Number of
NII
Scanners
(k)
Mean of
Container
Arrivals per
hour (λ)
Mean of
Services
per hour per
total
scanners µk
Percentage
of Utilization
of NII
scanners
(λ/µ)
Average
time a
container
spends in
Queue (Wq)
Average
Time a
container
spends in
System (W)
Table 6.24
Models’ Comparison by Scenario (Continue 2)
Scenario 2d
Big port
Description
Number of NII Scanners (k)
Simulation
1.00
1.00
Mean of Container Arrivals per hour (λ)
8.84
8.84
Mean of Services per hour per total scanners (µk)
10.00
10.00
88.42%
80.68%
Average time a container spends in Queue (Wq)
45.71
80.41
Average Time a container spends in System (W)
51.71
87.64
Percentage of Utilization of NII scanners (λ/µ)
98
Queuing
Starting with the analysis of our first scenario, if we compare both models in the Small
port we observe similar figures in both cases. If more simulation runs were made to the
Simulation model these figures would approach closer to each other. Recalling Figure 6.4
the service rate line is above the average container arrivals and the peak container
arrivals. This means that the capacity of the scanners will support the possible
fluctuations during the day. Figures 6.14 through 6.16 of the Simulation Model assuming
probabilistic exponential arrivals prove the assumption of the Queuing model
representing a steady service in the system with an average time in system of 19.27
minutes with an average waiting of 3.87. On the other hand, in the same scenario the big
port shows a marked difference between models. It is worth it to recall figures 6.8 and
6.16 again:
Figure 6.8
12.00
Arrival Average
10.00
Containers
8.00
Peak Arrivals
6.00
Peak M Factor
4.00
Average M Factor
2.00
Service Rate
22
:0
0
1:
00
4:
00
7:
00
10
:0
0
13
:0
0
16
:0
0
19
:0
0
22
:0
0
1:
00
4:
00
7:
00
10
:0
0
13
:0
0
16
:0
0
19
:0
0
22
:0
0
1:
00
4:
00
7:
00
10
:0
0
13
:0
0
16
:0
0
19
:0
0
0.00
Hours
7 day trial Simulation Model big port scenario 1a two NII (Tested Model)
120.00
100.00
Total Time in NII
Time (Minutes)
80.00
Queue Time in NII
60.00
Average Time in
System
40.00
20.00
99
10
6
11
3
12
0
12
7
13
4
14
1
92
85
78
71
64
57
50
43
36
29
22
8
15
0.00
1
Figure 6.18
Containers per Day
The Queuing Model assumes a constant arrival flow of 6.14 containers per hour with a
total average service rate of 10 containers per hour utilizing 2 NII scanners showed in
99
Figure 6.8. However, during the peak hours instead of arrive 6.14 containers per hour, as
the queuing model assumes the real arrivals are 10.12 containers per hour and represented
by the Peak Arrivals line over passing the service rate line. Due the arrivals are greater
than the service rate, the system is blocked producing an increase of the Peak M factor
from 14:00 hours to 22:00 represented by the red line in Figure 6.8. During this period the
system will be blocked with a total of 81 containers experiencing increasingly queue time
in the NII system.
Now in Figure 6.18 we observe the behavior of waiting times of each container during the
day detecting a marked increase tendency in the waiting times. This constant increase in
the waiting line starts from container 62 to container 142 when the line starts decreasing
until the end of the day merging with the Shift C of the next day (not shown in the
figure). If we analyze together figures 6.8 and 6.18 we realize that the 81 containers
estimated by the Peak M factor are the same containers represented in the Simulation
model from container number 62 to 142 throughout the day. Thus, the red line of the Peak
M factor in Figure 6.8 represents the steep increase in the Queue time in NII line (also in
red color).
The assess of the Peak M factor is not as exact as resulted in this scenario, but is a good
estimation of the increase behavior of the waiting line in a system. An important notation
of the M factor is that this factor does not represent the queue time itself. This factor
explains only the extra increase of containers (or any unit) per period of time in a waiting
line when the system is blocked by greater arrivals than its capacity service; or in simple
words, the M factor is the growth speed of the waiting line when the system is blocked.
Thus, the explanation of why in average the waiting time in system doubles in the
Simulation model compared with the Queue model because takes into account the peak
arrivals of containers throughout the day.
If we analyze the Figure 6.18, we observe a steady waiting time line during the beginning
of the day through containers 1 through 61. This steady stage of the system is estimated
by the Queuing model, average arrivals with greater service time in the system. If we only
would have used the Queuing model to analyze scenario 1A, we would have assumed that
in average a 3.7 containers during the whole day without considering the implications of
the length of the queue in the peak hours. This proves the importance to analyze a waiting
line system through different approaches like the Simulation model in order to assess the
impacts of the queues in a system.
Similar analysis can be made to the other scenarios. As we observe in Table 6.24 the
figures of both models are similar in the case of the small port in all scenarios because the
average service rate is underneath the fluctuations of the container arrivals. In the case of
the Big port the differences in queue time and time in system are greater for the
Simulation model because the fluctuations are assessed by the model when the arrivals
are grater then the capacity of service. We can conclude that both methods represent the
operating characteristics of the system, but when the container arrivals are steady the
queuing model is more suitable for a system analysis, and when there are disproportionate
arrivals the simulation model is necessary to assess properly the operating characteristics
of the system.
100
Continuing with our time analysis, in Figure 6.22 shows the average time spent by
container in scenario 2A when more scanners are added in the system.
Figure 6.22
Average Time in system per number of NII scanners, Scenario 2A
1:00:00
Time in System
0:50:00
0:40:00
Big Port
0:30:00
Small Port
0:20:00
0:10:00
0:00:00
1 NII Scanner
2 NII Scanners
3 NII Scanners
4 NII Scanners
Number of Scanners in System
In Figure 6.22 we observe when we increase scanners in the system the time spent in the
system drops. In the case of the Big port one scanner is not optimal to provide the system,
because the arrivals are greater than the service capacity of the equipment. When two NII
scanners are utilized the average time is around the 50 minutes of service time. The
increase of a third scanner will significantly improve the service time dropping around 15
minutes. The utilization of a forth scanner is unnecessary because no major improvement
is made to the service time of the system. In the case of the small port follows the same
negative relation on service time when NII scanners are increased in the system.
Considering the average time service, the optimal number of NII scanners in the system
for a small port is two. Figure 6.23 shows the relation of one, two and three scanners per
service time for a Small and Big port by each scenario.
Average Time in system per number of NII scanners, All scenarios
1:00:00
2
1
1
0:50:00
Time in System
Figure 6.23
0:40:00
1
0:30:00
0:20:00
2
1
2
2
3 3
3
3
1
0:10:00
2 2 3 3
2
3
1 1 2 2 3 3
0:00:00
Scenario 1A
Scenario 2A
Scenario 2B
Scenario 2C
Scenario 2D
Number of NII scanners required for Small Port (Blue) and Big Port (Purple)
101
From Figure 6.23 we can identify which of the five scenarios represent greater service
time by container resulting into higher levels of congestion within the terminals. In this
sense, the worse scenario that the shipping community could expect is scenario 2A which
requires at least 2 NII scanners to provide a 50 minutes service time for each container.
On the other hand, the less painfully situation that the shipping community could expect
is if the NII analysis is made in United States as scenario 2B considers.
6.6
Conclusions
Remarkable findings have been found derived from our waiting line analysis in our five
proposed scenarios. First, the forecasted saturation of the Alarm Inspection and second
inspection systems due the increase of the container flows derived from the
implementation of the legislation. In this respect, the only feasible alternative to solve this
problem is the use of New Technology through ASP inspection systems or the
considerably increase of personnel to perform the manual inspections. The second finding
is the importance the assessment of the transshipments flows. The scanning of transship
containers is the most challenging issue in order to implement the legislation in 2012. If
no major improvements in the technology and logistical integration is made in the
upcoming years the implementation of the 100% Container Scanning Legislation will be
on jeopardy. The third finding is the impact of the NII image analysis by officers or
authorities at origin port. If this analysis is avoided will represents an enormous increase
in service capacity alleviating container congestion at the terminals. The fourth finding is
important roll that the development of the technology would play for the upcoming years.
If substantial achievements are reached in any stage of the Integrated Security System
(ISS) process the implementation of the legislation would be more likely to be
implemented. And finally, in the case of the Hub and Spoke effect major findings have
been found. It is important for some countries reevaluate the possibility to attract
container flows derived from the new legislation. While for some countries the increase
of market would be minimal and competed by other counties of the region, for others
countries especially in the Africa and Oceania will really represents a good opportunity to
increase the market share.
102
7 Measuring the Economic Impact: Analysis and
Results of Waiting Lines
7.1
Introduction
The efficiency of the operation of the Integrated Scanning System is a tradeoff between
costs and congestion. In the previous chapter we concluded that adding more NII
scanners to the system will improve the service capacity which at the same time will
improve the operating characteristics of the system reducing waiting costs. In chapter 7
we will conduct an economic analysis of the waiting lines in order to provide a better
approach of the impact of the legislation. In order to estimate a good economic analysis is
required to determine the direct and indirect costs of the new policy. As direct costs are
included all the costs related with the operation of the ISS system itself. As indirect costs
we will consider only the congestion cost of the waiting lines. Even though the other
indirect costs can be included in the analysis of the legislation, we only will focus in the
waiting lines costs as our principal scope of study. The costs used in this analysis were
obtained form written sources in the topic, especially from the U.S. Government. The
object of this study is not to assess the exact costs of the operation of the ISS which will
vary between ports, but to provide an economic analysis of the waiting lines derived form
the implementation of the 100% Container Scanning Legislation.
7.2
7.2.1
Direct and Indirect costs of the Legislation
Direct Costs
As direct costs we can assume all the traceable costs incurred in order to provide the
service of the Integrated Security System (ISS) within a terminal. These costs include the
equipment described in chapter 4 (RPM, NII, RIID, etc), labor costs of the personnel
performing the service, installation costs, maintenance costs, communication systems,
software development, personnel training, among other costs related with the operation of
the ISS system. These costs will vary between ports depending in the type of equipment
utilized, the labor costs of each country, communication costs, and other costs incurred.
In order to conduct our analysis we will consider a set of equipment called “ISS Unit”
which includes the required equipment necessary to perform the Integrated Security
System service. Table 7.1 describes the equipment included in the ISS unit. Thus,
according to this table we assume that by every ISS unit of current technology we are
including all the equipment, personnel and all the resources needed to provide the
scanning service. Now we need to address a cost to the ISS unit in order to perform our
waiting cost analysis.
103
Table 7.1
Integrated Security System Unit (ISS unit)
Current Technology
New Technology
Quantity
Quantity
RPM
3
3
NII Scanner
1
1
RIID
2
0
ASP
0
1
Description of Equipment
Recalling Table 4.7 the Department of Homeland Security (DHS) and the Department of
Energy (DOE) presented the report to the congress on Integrated Scanning Systems
(2008) of the expenses of the pilot programs. Both dependencies spent together around 60
million Dollars in order to launch the SFI pilot programs in 7 different ports, three in a
full scale and the rest on a partially scale. Due the fact that we do not have access to the
detailed structure of these expenditures by port and dividing the total costs by 7 ports will
give a wrong assess of the costs because only in three ports were fully equipped, we
calculate the percentages incurred by each concept spent in Figure 7.1.
Figure 7.1
SFI pilot program Costs by percentage
SFI Pilot Program Costs (%)
DHS Analytical Study
DHS Comunications
Comunications
DHS
5%
Communications
DOE
10%
Equipment
DHS
17%
DHS Equipment
DHS Hardware
DHS Hardware (Server
License)
DHS Support
3%0%
0%
1%
DHS Program Office Support
DHS Software Development
0%
1%
3%
DHS Software License
DHS Software Support
2%
0%
0%
DHS Training
Installation
DOE
26%
Equipment
DOE
8%
Software
Development
DHS
17%
DHS Travel
DOE Equipment
DOE Installation
Source: Report to Congress ISS (2008).
DOE Testing
After assessing the percentages of each concept incurred in the pilot programs of the
Security Freight Initiative program, we observe a 17% from the total cost spent in
equipment by DHS which is the NII scanner itself. Then with the cost of one NII scanner
and with the 17% of participation from Figure 1 we can estimate the rest of the costs
incurred. Table 7.2 summarizes the costs assessed in our analysis by current technology
and projected new technology taking onto account ASP system.
104
Table 7.2
Estimated total direct costs of Integrated Scanning System unit
Dependency
Description
Percentage
Projected Cost
New Tech Costs
DHS
Analytical Study
0.33%
$ 49,236.83
$ 68,931.56
DHS
Communications
4.53%
$ 667,129.25
$ 933,980.95
DHS
Equipment
16.97%
$ 2,500,000.00
$ 3,500,000.00
DHS
Hardware
5.01%
$ 737,615.46
$ 1,032,661.64
DHS
Hardware (Server License)
0.14%
$ 20,219.60
$ 28,307.43
DHS
Support
0.78%
$ 114,210.49
$ 159,894.68
DHS
Program Office Support
2.77%
$ 408,050.22
$ 571,270.31
DHS
Software Development
16.85%
$ 2,481,753.82
$ 3,474,455.34
DHS
Software License
1.05%
$ 154,723.29
$ 216,612.60
DHS
Software Support
0.23%
$ 34,597.49
$ 48,436.48
DHS
Training
0.39%
$ 56,992.12
$ 79,788.97
DHS
Travel
1.84%
$ 270,579.27
$ 378,810.98
DOE
Equipment
8.44%
$ 1,242,431.56
$ 1,739,404.19
DOE
Installation
25.68%
$ 3,782,762.43
$ 5,295,867.41
DOE
Testing
0.78%
$ 114,475.63
$ 160,265.88
DOE
Maintenance
0.92%
$ 135,401.28
$ 189,561.79
DOE
Communications
9.92%
$ 1,461,246.18
$ 2,045,744.66
DOE
Training
3.20%
$ 470,950.27
$ 659,330.38
DOE
Travel
0.18%
$ 26,264.65
$ 36,770.51
Total Investment Cost
100.00%
$ 14,728,639.83
$ 20,620,095.77
Based on the percentages obtained from the SFI cost and with a base price of US$ 2.5
million for the current ISS unit and US$3.5 for the new technology ISS unit, we
estimated a total cost of US$ 14.7 million and US$ 20.6 million respectively. In Table 7.3
describes the total costs of ISS unit by time period accounting a 7 year depreciation. The
figures obtained in table 7.3are similar to the figures calculated by Carluer (2008) and
mentioned in chapter 4. Thus, we assume that we have a good estimation of the total cost
of the operation of the ISS unit. It is important to mention that no Economies of Scale are
attributed per increase of ISS unit, we are assuming constant costs. Table 7.3 summarizes
the estimated costs of the ISS unit per time period, which will be the base of our cost
analysis.
Table 7.3
Estimated total direct costs of Integrated Scanning System unit per period
Description
Projected Cost
New Tech Costs
Total Investment Cost
$ 14,728,639.83
$ 20,620,095.77
Cost per Year (7 year depreciation)
$ 2,104,091.40
$ 2,945,727.97
Cost per Day
$ 5,764.63
$ 8,070.49
Cost Per Hour
$ 240.19
$ 336.27
105
7.2.2
Indirect Costs
The indirect costs are all the expenses not identifiable with the operation of a product or
service (Webster [2008]). As indirect costs of the 100% Container Legislation we will
assume only two concepts: Waiting Lines Costs and Transfer Costs. As waiting lines
costs we are assuming all the Traffic Congestion Costs defined by the Victoria Transport
Policy Institute (2006) but not limited to “The incremental delay, driver stress, vehicle
costs, crash risk and pollution resulting from interference between vehicles in the traffic
stream”. All the queues in a system incur in traffic congestion costs derived from the
delay to obtain a service. The second concept that we will include as indirect cost are the
Transfer costs. We are assuming as transfer cost as the extra expenses to move one
transshipment container from the terminal container yard to the Integrated Scanning
System (ISS) and return back to the terminal container yard.
7.2.2.1 Waiting Lines Costs
In order to conduct a representative economic analysis of the waiting lines is important to
assess realistic and approachable estimations of the queues. From all the costs associated
with the 100% Container Scanning Law, the waiting cost is the most difficult variable
concept to determine because this cost is not directly attributed to the ISS system.
However it is essential to include this cost in the economic burden of the scanning
legislation because if is ignored this situation will lead into high congestion at terminals
reducing the efficiency and disrupting the welfare of the global supply chain. The
importance to assign a cost to the waiting lines is because if the queues are not taking into
account the decision management of any service would not tackle the problem
encouraging their customers to look for other service providers or find other alternatives
to avoid the system. This situation might lead to a redistribution of the container flows
bounding to the United States. According to Swizstick (2007), in the last Transpacific
Stabilization Agreement (TSA) roundtable meeting on June 7, 2007, the Port/Terminal
congestion costs account US 200 billion per year. The TSA is conformed by carriers such
as APL, CMA-CGM, Cosco, Evergreen, Hanjin, Hapag-Lloyd, Hyunday, K Line, MOL,
NYK, OOCL, and Yang Ming.
Even though Swizstick mentioned a study presented in TSA, unfortunately we have not
find a specific investigation assessing the congestion costs at terminal ports. However,
many studies have been made about the measurement of congestion addressing these
costs from US$ 20 up to US$160. Due the assessment of the congestion costs within a
terminal is out of scope of this study, we will base our calculations in the investigation
created by the American Highway Users Alliance (2000). In their study named “Saving
Time, Saving Money: the Economics of Unclogging America’s worst bottlenecks”, the
American Highway Users Alliance estimated the congestion costs for commercial
vehicles at US$ 48 per hour. Since this study was made in 2000 we projected these costs
considering the inflation growth of the United States to the year 2012 when the legislation
will be implemented. From this assumption we generate Table 7.4 which shows the
assumed costs of congestion in this study.
Table 7.4
106
Forecasted Congestion Costs by hour
Description
Projected Cost 2012
Congestion Cost by hour
US$ 66.30
Thus, in our calculations we will apply a US$ 66.30 per hour a container spends in queue
line. Like mentioned before, we are basing our study in this figure in order to estimate the
economic impact of the legislation. In order to apply this methodology into a specific case
this congestion cost must be tailored according the characteristics of each individual port.
7.2.2.1 Transfer Costs
The second indirect cost that must be necessarily to account is the transfer costs. This
costs are only associated with the movement of transshipped containers from a container
yard to the Integrated Scanning System (ISS). The transfer procedure consists in the
inspection in the ISS system by movement of a transshipped container from the terminal
container yard by a specialized terminal truck merging with the gate flows at the RPM
system and returning to the container yard. The importance to assess this cost into our
analysis is because is directly associated with the cost of the scanning itself. Most of the
studies and publications have failed to include this cost associated with the legislation.
The costs associated with movements within terminals vary from one port to another. In
the study made by Carluer (2008 p.160) the author assumes a US$ 20 per transfer
movement. In our interview with the project Manager Security of the port of Rotterdam,
Mr. Jurgen Duintjer (2008) mentioned a 100 Euro charge for container transfer
movements within the port. In the port of Valencia (2008) in Spain an average cost an
estimate cost per hour of the housekeeping operations by terminal truck is around 400
Euro per 6 hour shift. In order to estimate the transfer cost for our study we decide to
assume a balanced cost from these figures. We assumed a US$60 per transfer cost and we
forecasted this figure to the year 2012 according to the inflation growth of the United
States. Table 7.5 shows the estimated projected transfer costs for 2012.
Table 7.5
Forecasted Transfer Costs by container
Description
Projected Cost 2012
Transfer Cost by Container
US$ 75.60
Thus, we will assume a transfer cost of US$ 75 per container moved to the ISS system for
our cost analysis. After calculate the direct costs and the indirect costs assumed in this
study, in the next section we will conduct our waiting line cost analysis base in our cost
estimations.
7.3
Economic Analysis of Waiting Lines
From the last section we assessed a cost base in order to measure the economic impact of
the waiting lines on each scenario. Recalling form scenario 2A based in the Queue model
in Figure 7.2 we can observe the similar behavior of the waiting lines like Figure 6.9 but
now measured in US$ Dollars. In the graph are represented the NII costs, which is the
scanner itself, the Total Cost of the service, which includes all the incurred costs per hour
of service, the Peak Waiting Cost, resulting from the M factor derived from a blocked
system, the average waiting cost, which is calculated by the Queuing formula (5.20) in
107
chapter 5, the Total Average Cost, obtained from the Total Cost of service, and finally the
Transfer costs from the transshipment containers. In the graph we observe the costs of the
waiting lines which increase the Total Cost of the service. This graph shows the impact
cost per hour within a three day of service at terminal. In the highest peak the total cost of
the service reaches more than US$ 3,000 per hour. The average cost of the service per
hour is almost US$ 2,000.00.
Figure 7.2
Costs of Waiting Lines Big port Scenario 2A two NII
4,500.00
4,000.00
NII Cost
US$ Dollars
3,500.00
Total Cost
3,000.00
Peak Waiting Cost
2,500.00
2,000.00
Average Waiting
Cost
Total Average Cost
1,500.00
1,000.00
Transfer Cost
500.00
22
:0
0
2:
00
6:
00
10
:0
0
14
:0
0
18
:0
0
22
:0
0
2:
00
6:
00
10
:0
0
14
:0
0
18
:0
0
22
:0
0
2:
00
6:
00
10
:0
0
14
:0
0
18
:0
0
-
Hours (3 Days)
If we consider the accumulative costs from each hour of service the impact looks even
greater. In Figure 7.3 we have the same waiting lines cost analysis under scenario 2A, but
we are adding a new line to the graph which assesses the accumulative costs of the
service throughout the day. As we can see by the end of the day the total cost
accumulated only from the peak waiting costs at the end of the day overpasses US$
50,000. This represents an indirect cost increase of 931% of the cost of the scanner.
According with the Queue model the two NII systems capacity of service can cover the
container arrivals of the Big port in scenario 2b with a reasonable waiting time spent on
queue. However, if take into account the waiting line costs the system does not look so
efficient due its higher waiting line costs. In this scenario the acquisition of a third
scanner should be considered.
108
Costs of Waiting Lines Big port Scenario 2A two NII
US$ Dollars
Figure 7.3
60,000.00
NII Cost
50,000.00
Total Cost
40,000.00
Peak Waiting Cost
30,000.00
Average Waiting Cost
20,000.00
Peak Total Cost Acc
10,000.00
Transfer Cost
22
:
00
1:
00
4:
00
7:
00
10
:0
0
13
:0
0
16
:0
0
19
:0
0
22
:0
0
1:
00
4:
00
7:
00
10
:0
0
13
:0
0
16
:0
0
19
:0
0
22
:0
0
1:
00
4:
00
7:
00
10
:0
0
13
:0
0
16
:0
0
19
:0
0
-
Hours
As mentioned in the previous chapter the Queuing model is based on average container
arrivals, but when the system experiences extreme fluctuations over the period, the
Simulation model could result more useful to our waiting line analysis. Also for the same
scenario 2A Big port with two NII, in Figure 7.4 we have calculated the Total Cost
incurred by day assessed with the Waiting Line Simulation model, also tested by 7
consecutive days.
Container Costs Simulation Model Big port scenario 2a two NII (Tested Model)
$350.00
$300.00
Total Cost per Container
NII Cost
Waiting Cost
$250.00
US$ Dollars
Average Cost per Container
Transfer Cost
$200.00
$150.00
$100.00
$50.00
20
9
20
1
19
3
18
5
17
7
16
9
16
1
15
3
14
5
13
7
12
9
12
1
11
3
97
10
5
89
81
73
65
57
49
41
33
25
17
9
$-
1
Figure 7.4
Containers per Day
If we compare Figure7.3 and Figure 7.4 the growth tendency is similar at the end of the
day. The average cost per container assessed by the simulation model reaches more than
US$150.00 per day. If we would have accounted only the cost of the NII scanner we
would have wrongly assumed only US$ 52.32 per service cost, without including the cost
of the container transshipment transfers. However assuming the waiting lines cost and the
transfer costs we have assessed the real cost of the Integrated Scanning System (ISS) for
the Big port under scenario 2A. An average Total Cost of $157.41 per container was
incurred including US$ 54.32 of NII cost, US$ 79.94 of waiting line cost, plus US$ 23.15
109
from transfer costs ( 65 transshipping containers x US$ 75.60 prorated by the total
container flow). If we analyze Figure 7.4 we can observe that at the moment the waiting
line cost overpass the cost of the NII scanner, the system becomes inefficient. This is
because the cost of the waiting lines turns more expensive than the cost of the scanner
system. Now if we observe Figure 7.5 which describes the same scenario 2A for the
Small port, we have a different structure of the costs.
Figure 7.5
180.00
160.00
US$ Dollars
140.00
NII Cost
120.00
100.00
Waiting Cost
80.00
60.00
Average Cost per
Container
40.00
20.00
Transfer Cost
77
73
69
65
61
57
53
49
45
41
37
33
29
25
21
17
9
13
5
1
-
Containers per Day
In the case of the small port the total costs per container are around US$ 140.00 less than
the Big port. However, the direct costs of the operation of the NII system are grater for
the Small port. Even though, in this scenario the small port presents waiting costs, these
do not overpass the cost of the NII system. In this scenario for the Small port it seems
unnecessary to add a second NII scanner in order to reduce the waiting costs because will
increase the NII costs. In table 7.6 are summarized the costs incurred by each port under
scenario 2A.
Table 7.6
Cost comparison Small port, Big Port (Scenario 2A)
Cost per Container
Scenario 2A
Small Port
Cost per Day
Big Port
Small Port
Big Port
Number of NII scanners
1
2
1
2
Containers per Day
78
212
78
212
NII Cost
$ 73.91
$ 54.32
$ 5,764.63
$ 11,529.27
$ 29.98
$ 79.94
$ 2,338.44
$ 16,947.28
Transfer costs
$ 36.83*
$ 23.15*
$ 2,872.80
$ 4,914.00
Total Cost
$ 140.72
$ 157.41
$ 10,975.87
$ 33,390.55
*Total transfer costs prorated by total container flow per day.
Analyzing Table 7.6 we observe that the Big ports incurs in higher operational costs than
the small port per container and per day. In the case of the Average waiting costs the
small port has lower costs than the Big port. In this sense for the big port has a higher
potential of savings due waiting costs. In the case of the transfer costs which are fixed to
the number of container transhipments the small port presents a higher cost than the Big
port. Figure 7.6 shows the cost structure of both ports per container.
110
Figure 7.6
Cost comparisons per container Small port, Big Port Histogram (Scenario 2A)
$180.00
Transfer Cost
$160.00
$140.00
NII Cost
US$ Dollars
$120.00
$100.00
$80.00
$60.00
$40.00
$20.00
$0.00
Dubai
Hong Kong
Port
From Figure 7.6 we observe the high burden of the transfer costs represent for the overall
cost of the legislation. This is because the port of Dubai has a higher Transshipment ratio
then Hong Kong described in chapter 6. From previous table 7.5 we forecasted a US$
75.60 per transshipment transfers per container higher than the direct costs of the NII
scanners for both ports. In Figure 7.7 we compare the costs from a container who needs to
be transshipped than other arriving by the gate.
Transshipment Vs Gate Container Cost Comparison Small port, Big Port (Scenario 2A)
250
Transfer Cost
Waiting Cost
200
US$ Dollars
Figure 7.7
NII Cost
150
100
50
0
Small Port
Big Port
Small Port (Gate)
(Transshipment) (Transshipment)
Big Port (Gate)
Small Port - Big Port
The burden of the transfer costs for both ports is huge. A lot has been said about the
possible extra costs that the NII scanning procedure might impact the freight costs but not
much has been said about the burden of the transfer costs for the transship containers.
Thus recalling what we conclude form scenario 2D, if the small ports can not afford or is
inefficient to acquire a NII system, their US-bound container flows will reallocate to
other ports. This reallocation will impact more the containers from the small countries
because besides the extra cost to reallocate the containers to other ports (not analyzed in
this study) they also will have to pay the extra transfer costs. Also mentioned in chapter 6,
the higher impact of the Hub and Spoke effect would take place in Africa and Oceania,
111
which U.S.-bound containers represent higher market share of their operations. In this
context we can conclude that even the Hub and Spoke effect will impact higher in the
Africa and Oceania ports this might represent an attractive container flows for the ports
which can afford the technology, however this would represent a high burden for the
shippers who will have to support the extra costs of reallocation of their U.S.-bound
containers and the extra transfer costs of their transshipments. In this sense the legislation
will encourage to widen the gap between developed countries and developing countries
due the Hub and Spoke effect.
Continuing with our analysis, if we conclude that is cheaper to scan in a small port
because has fewer costs than the Big port under scenario 2A, we could make a wrong
assumption. We can see in Figure 7.6 the higher waiting costs that the Big port has
derived from long waiting lines compared from the Small port. In this sense the Big port
could increase their NII scanners in order to reduce their Waiting line costs. In Figure 7.8
we observe the cost structure for each port when more NII scanners are added in the
system.
Figure 7.8
NII costs per container under scenario 2A Small port, Big Port when more NII Scanners are added into the
system
350
300
Small port
US$ Dollars
250
Big port
200
150
100
50
0
1 NII scanner
2 NII scanners
3 NII scanners
4 NII scanners
We observe from Figure 7.8 the higher cost per container for the small port then the Big
port derived from their lower U.S. Bound flows. In both cases the increase of NII
scanners into the system will increase their direct costs. However the for the small port
the steep is more pronounced for the small port. In this sense, the Big port has a good
opportunity to reduce their costs tackling the waiting costs increasing more NII scanners
into the system. In order to compare both ports under each scenario, first is necessary to
calculate the right number of NII scanners considering service time and costs. In the next
section we will estimate the optimal number of NII scanners in order to evaluate the
performance of each port under each scenario.
112
7.4
Optimal Selection of Systems
After analyzing the waiting times and the waiting costs of each scenario, it is necessary to
assess the right amount of NII scanners in order to minimize the total costs of the
legislation. From chapter 6 we observed a negative relation between the service waiting
time and the number of NII scanners in the system. The more scanners we include in the
system the less service time was expected for each container. Contrary to the waiting
times, the waiting costs have a positive relationship between costs and the addition of NII
scanners into the system. As more scanners we include in the system the higher NII costs
will be incurred per container. In order to find the optimal number of scanners we need to
evaluate both concepts the waiting time and the total costs of the service. The total costs
include service costs (NII scanner) and the waiting costs (waiting lines). We exclude the
transfer costs in order to analyze only the relation cost-time of the NII system. Derived
from figures 6.22 and 7.8, Figure 7.9 describes the relation between Total Cost and
Waiting Time of the waiting lines for both ports.
Figure 7.9
Total Service costs and Waiting Time Small and Big port, Scenario 2A
1:00:00
0:50:00
$350.00
Waiting time Big port
$300.00
Waiting Time Small port
Total Costs Small port
$250.00
0:40:00
Total costs Big port
$200.00
0:30:00
$150.00
0:20:00
$100.00
0:10:00
$50.00
-0:00:00
$0.00
1 NII Scanner
2 NII Scanners
3 NII Scanners
4 NII Scanners
From Figure 7.9 we can conclude that the optimization of the NII scanners in the system
is a tradeoff relationship between the waiting time of service and total costs. The
maximization of the benefits is produced when both lines cross each other. In this sense,
we can assume that the waiting time and the total cost of the NII system are in
equilibrium. Because in the case of the NII systems we can not have fractions of
equipments, we have to choose the closest one to the equilibrium. In Figure 7.9 we can
easily identify the optimal number of NII scanners for the Big port, while for the small
port is between one and two scanners. Based in this assumption, Table 7.7 estimates the
optimal number of scanners and total costs for each scenario based in the Queuing model.
From this table we can made good assumptions about the 100% Container Scanning
Legislation. In the next section the optimal structure of cost and service assessed by the
Queuing model will be analyzed and validated with the Simulation Model for each
scenario.
113
Also in Table 7.7 we have include two new concepts, the optimal percentage cost of
waiting time and the optimal percentage cost of NII service. Due this calculations are
based in two examples, these figures are introduced in order to serve as a basis to apply in
other ports. Thus, if we would like to calculate a quick estimation of the waiting lines we
could apply these indexes to any to any port based on the scenario which fits better to
their situation.
114
Table 7.7
Optimal number of NII scanners for each scenario Small port and Big port
Scenario 1A
Scenario 2A
Scenario 2B
Scenario 2C
Scenario 2D
Small Port
Big Port
Small Port
Big Port
Small Port
Big Port
Small Port
Big Port
Big Port
1
2
1
3
1
1
1
2
2
1.62
6.14
3.25
8.8
3.25
8.8
3.25
8.8
8.84
5
10
5
15
120
120
10
20
20
32.40%
61.40%
65.00%
58.67%
2.71%
7.33%
32.50%
44.00%
44.20%
0:05:45
0:07:16
0:22:17
0:03:16
0:00:01
0:00:02
0:02:53
0:01:26
0:01:27
0:17:45
0:19:16
0:34:17
0:15:16
0:00:31
0:00:32
0:08:53
0:07:26
0:07:27
Total Average Cost per hour
$ 272.00
$ 611.01
$ 363.33
$ 869.09
$ 242.04
$ 245.44
$ 368.20
$ 744.91
$ 745.39
Total Average Cost per Container
$ 167.68
$ 99.59
$ 111.77
$ 98.73
$ 74.46
$ 27.88
$ 113.27
$ 84.62
$ 84.30
Average Waiting Cost per Hour
$ 31.81
$ 130.62
$ 123.14
$ 148.51
$ 1.85
$ 5.25
$ 31.93
$ 72.37
$ 72.85
Average Service Cost (NII) per Hour
$ 240.19
$ 480.39
$ 240.19
$ 720.58
$ 240.19
$ 240.19
$ 336.27
$ 672.54
$ 672.54
Optimal Percentage cost of Waiting time
12%
21%
34%
17%
1%
2%
9%
10%
10%
Optimal Percentage cost of NII service
88%
79%
66%
83%
99%
98%
91%
90%
90%
Mean of Services per hour per total scanners µk
115
7.5
Cost Analysis of Waiting Lines
From section 7.4 with the application of the Queuing model we have estimated the
optimal number of NII systems of each scenario for the Small and Big port. However as
we have mentioned before, an extra analysis is required to prove the calculations made
from our model due the queuing model only bases its estimates in average amounts. In
this sense the Simulation model could be a good alternative to validate our calculations.
Two concepts are use in the simulation study: Verification and Validation. The
verification is the process to determine if the Simulation model created is making the
right calculations correctly, while the Verification is the process to determine if the
calculations made from the model represents the real system (Anderson, Sweeney,
Williams, Martin [2008 p.619]). Due the limitations of our study and the forecasted
analysis employed to measure the possible impacts of the legislation the validation
concept is out of scope. However due the objective of the validation id to ensure that the
model created represents the real system, we are assuming our Simulation model as the
validation tool to assess the optimal calculations from the Queuing model. According to
this assumption in this section we are analyzing each scenario with a 7 day trail
Simulation model base in the optimal estimations. In this section are included in the
transfer cost prorated by total containers for each port.
7.5.1
Cost Analysis Scenario 1A
From Table 7.7 we have determined the optimal numbers of scanners for each port based
in the Queuing model. According to this table, in the case of the port of Dubai one NII
scanner is sufficient to provide the service. The percentage of utilization of the Scanner is
32.40%. The estimated waiting time of service is around 17 minutes while the waiting
time in queue is expected to be around 5 minutes. The average total cost per hour is US$
272.00. The total average cost per container for a small port is estimated in US$ 167.68.
The average cost of the NII scanner per hour is calculated in US$ 240.19 while the
average waiting cost per hour is assumed in US$ 31.81. For the Big port the waiting time
in the system and the waiting time in queue are similar than for the Small port 19 minutes
and 7 minutes respectively. In all the scenarios, including 1A the Small port incurred in
higher costs per container than the Big port. However, the Big port reported higher
operation costs per hour in all the scenarios. Under this scenario only is considered the
gate flows of U.S.-bound containers, thus no transfer costs are included. Figure 7.10
shows the results of the 7 day trial Simulation model for the optimal number of scanners
for the Big port.
116
US$ Dollars
Figure 7.10
200.00
180.00
NII Cost
160.00
Waiting Cost
140.00
120.00
100.00
80.00
60.00
40.00
20.00
14
5
13
9
13
3
12
7
12
1
11
5
10
9
10
3
97
91
85
79
73
67
61
55
49
43
37
31
25
19
7
13
1
0.00
Containers per Day
In the graph shows an accumulative increase in waiting cost. However this cost only
crosses the NII cost line only by a few containers at the end of the day, not enough to
justify the acquisition of an extra NII scanner.
7.5.2
Cost Analysis Scenario 2A
In scenario 2A the transshipment flows are included for both ports. In the case of the
small port there is no need to acquire an extra NII scanner. However for the case of the
Big port, after analyzing the waiting lines and the cost structure, we assess the necessity
to add an extra NII scanner to the system. Recalling Table 7.4 we observed that the
waiting cost over passed by far the cost of the NII scanner. In this sense the third scanner
was necessary to improve the overall of the system. Figure 7.11 shows the results of the
Simulation model for the Big port with three scanners in the system.
Container Costs Simulation Model Big port scenario 2a three NII (Tested Model)
$160.00
$140.00
$120.00
US$ Dollars
$100.00
NII Cost
Total Cost per container
$80.00
Waiting Cost
Transfer cost
$60.00
$40.00
$20.00
20
5
19
3
18
1
16
9
15
7
14
5
13
3
12
1
10
9
97
85
73
61
49
37
25
1
$-
13
Figure 7.11
Containers per Day
117
If we compare Figure 7.4 and 7.11 we observe the improvement generated for the
addition of the third scanner to the system. Another important notation from Scenario 2A
is the waiting time and the time in system the containers spent in both ports. Besides the
Big port has a lower unitary cost per scanned container, also the service time is more
convenient in the Big port. The time spent in the system in a Big port is 15 minutes, while
in the small port doubles up to 34 minutes. This is an extra advantage for the Big port
which can offer a better service at less cost than the small port.
7.5.3
Cost Analysis Scenario 2B
In the case of scenario 2B the situation is merely similar between both ports. The cost
graphs for both ports look similar. The avoidance of the image inspection from the NII
system at foreign ports increases dramatically the capacity of the NII scanner. For both
ports the service is practically a free flow through the scanner, spending in average less
then one minute through the NII system. In the cost structure, for the small port this
scenario is the only one where they can offer the NII service under US$ 100. Even
though, the big port remains more efficient then the Small port offering the service for
less than US$ 30. An important notation must be made that under this scenario the image
inspection generate costs which are included in our service costs but now are transferred
to the National Target Center in the United States. Thus, he U.S. Government would
assume the image analysis costs derived from the NII scanned images from foreign ports.
In Figure 7.13 we observe a flat behavior of all the costs of the system. In this scenario is
important to notice the high impact of the transfer costs derived from the transshipment
flows.
Figure 7.12
Container Costs Simulation Model Big port scenario 2b one NII (Tested Model)
$60.00
US$ Dollars
$50.00
$40.00
NII Cost
$30.00
Waiting Cost
$20.00
Transfer Costs
$10.00
21
1
16
9
18
3
19
7
14
1
15
5
99
11
3
12
7
85
71
57
43
29
1
15
$-
Containers per Day
7.5.4
Cost Analysis Scenario 2C
In scenario 2C both ports present a relative steady fluctuation of containers throughout
the system. Both ports have an average service time under 10 minutes. Different from the
previous scenarios, scenario 2C assumes new technology with higher NII costs. In this
118
sense we observe that the Big port the costs incurred under this scenario are lower then
scenario 2B. This means that the increase in capacity derived from new technology has
helped the Big port to reduce their total average costs per container. Thus, for the Big port
is feasible invest in new technology which not only will lowered their costs also the
waiting time in system. Contrary to the small port the average cost per container remained
the same in scenario 2B than 2C. However the service time in system improved
significantly from 34 minutes under scenario 2A to 8 minutes in scenario 2C. This is
because the savings obtained from the decrease of the waiting lines was eclipsed with the
increase to obtain new technology. In this sense the port administration of Dubai, must
decide to invest in new technology to improve the service time in the system, or invest
that amount in other project maintaining the current technology to provide the service.
From this scenario we can assume that new technology will feasible for the Big port
rather then the small port. In Figure 7.13 we observe a similar behavior as in Figure 7.12
from scenario 2B. This behavior is derived form the optimal number of scanner which
maximizes the efficiency and minimizes the costs.
Figure 7.13
Container Costs Simulation Model Big port scenario 2C two NII (Tested Model)
$120.00
US$ Dollars
$100.00
Total Cost per
Container
$80.00
NII Cost
$60.00
Waiting Cost
$40.00
Average Cost
Per Container
$20.00
Transfer Costs
205
193
181
169
157
145
133
121
109
97
85
73
61
49
37
25
13
1
$-
Containers per Day
7.5.5
Cost Analysis Scenario 2D
In the case of the scenario 2D we already have mentioned the similarity with scenario 2C.
The optimal number of NII scanners is two. The waiting time in system is around 7
minutes on average. The total average cost per container is around the US$ 85.00. An
important observation is the transfer costs are higher than the waiting costs in all the
scenarios. This represents the high burden of costs attributed to the transshipment flows.
In Figure 7.14 we observe the same behavior as the scenario 2C based in new technology.
As we have mentioned before, for the Asiatic ports is not expected to have a huge
increase in container flows derived from the Hub and Spoke effect.
119
Figure 7.14
Container Costs Simulation Model Big port scenario 2D two NII (Tested Model)
$120.00
Total Cost per
Container
US$ Dollars
$100.00
$80.00
NII Cost
$60.00
Waiting Cost
$40.00
Average Cost
per Container
Transfer Costs
$20.00
15
7
17
0
18
3
19
6
20
9
92
10
5
11
8
13
1
14
4
79
66
53
40
27
1
14
$-
Containers per Day
In Figure 7.15 are summarized all the optimal costs for each scenario. As already
mentioned, in all scenarios the small port incurs in higher cost than the big port. In this
figure we observe in which scenarios will represent a higher economic impact. In
scenario 1A the small port has higher costs per container derived from higher investment
costs and lower containers flows. The scenario which has less impact from the legislation
is scenario 2B. This one is the most efficient of all with the less time spent at waiting line
and the less expensive costs among all the scenarios. In the scenarios 2C and 2D we have
practically the same situation with no substantial increase due the Hub and Spoke effect
in the Hong Kong port.
Figure 7.15
Container Costs Comparison by Scenario with optimal NII system
$180.00
$160.00
$140.00
$120.00
$100.00
$80.00
$60.00
$40.00
$20.00
$-
Small
Port
Big
Port
Scenario 1A
Small
Port
Big
Port
Scenario 2A
Small
Port
Big
Port
Scenario 2B
Small
Port
Big
Port
Scenario 2C
Big
Port
Scenario
2D
In Figure 7.15 we see a positive effect in costs from scenario 1A to scenario 2A for the
small port. In both scenarios assume the same technology and one scanner to provide the
service. However the increase in the number of containers due the transshipment flows
affects negatively the total costs of the service. This situation is generated by Economies
of Scale where the increase in the flow of containers reduces the unitary service cost. In
120
next section we will conduct a sensitive analysis in order to analyze the possible
behaviors of costs due fluctuations in containers through the NII system.
7.6
Sensitivity of results to the percentage of Containers Scanned
Since the 100% Container Scanning Legislation was enacted, many reactions have been
expressed by the shipping community. Most of these reactions have questioned the
feasibility of the new legislation adopted by the U.S. Congress. In order to find an
alternative to fulfill the legislation, some stakeholders have expressed the possibility to
ease the burden of the legislation reducing the percentage of container inspection. This
idea has been taken seriously by some members of the shipping community and we have
considered the feasibility to measure this alternative. In order to assess a lower level of
percentage container scanning, in this section we conducted a sensitive analysis of our
optimal results. The idea to perform a sensitive analysis is to observe the behaviors of the
waiting lines if the volume of container scans is modified. From this analysis is expected
to learn if the changes container scans required by the legislation will improve welfare of
the legislation itself. Also important to measure is if these changes in the volume
container scans will affect the optimal solution founded in section 7.4. In this section we
are analyzing two possible volumes: 50% and 25% of the total container flows bounding
to the United States. Assuming these container volumes we generated Tables 7.8 y 7.9 for
the Small and Big port of each scenario.
7.6.1
50% Container Scanning
The first idea to reduce the container scanning volumes was in order to reduce congestion
at terminals. The improvements in the traffic at the NII system decreased. However, these
flows were already reasonable ranging from less than one minute up to 35 minutes on
average. In the case of the Big port under scenario 1A, the waiting time increased due the
decrease of one NII scanner in the system.
In the case of the NII scanners required by the small port remained the same. Since the
100% scanning volume the small port always needed only one NII scanner to provide the
service, with the decrease in the scanning volumes the same amount of scanners is
required. For the case of the Big port, the change in scanning volumes decreased the
number of scanners in one unit for all the scenarios, except scenario 2B which already
required one NII scanner.
On the other hand, the impact on the average total costs per container increase
significantly for both ports. The cost impact to change the percentage volume form 100%
to 50% is extreme for the small port. For all the scenarios the costs practically doubled
with the changed scanning volume. For the case of the Big port, the increase in costs was
not as alarming as for the Small port. However a considerably 4% to almost 30% raise
was derived form the change in scanning volume.
121
Table 7.8
Sensitivity Analysis with 50% of container scanning for each scenario Small port and Big port
Scenario 1A
Operating Characteristics (50% Container Scanning)
Scenario 2A
Scenario 2B
Scenario 2C
Scenario 2D
Small
Big Port
Small
Big Port
Small
Big Port
Small
Big Port
Big Port
1
1
1
2
1
1
1
1
1
0.81
3.07
1.63
4.40
1.63
4.40
1.63
4.40
4.42
5
10
5
10
120
120
10
10
10
16.20%
30.70%
32.50%
44.00%
1.35%
3.67%
16.25%
44.00%
44.20%
0:02:19
0:19:05
0:05:48
0:02:53
0:00:00
0:00:01
0:01:10
0:04:43
0:04:45
0:14:19
0:31:05
0:17:48
0:14:53
0:00:30
0:00:31
0:07:10
0:10:43
0:10:45
$ 53.01
$ 45.65
$ 72.26
$ 52.74
$ 241.11
$ 242.72
$ 49.18
$ 388.36
$ 725.06
$ 12.36
$ 12.59
$ 167.03
$ 25.62
$ 147.92
$ 55.16
$ 214.22
$ 88.26
$ 87.96
$ 12.82
$ 105.46
$ 32.07
$ 72.35
$ 0.91
$ 2.52
$ 108.99
$ 52.09
$ 52.52
$ 240.19
$ 240.19
$ 240.19
$ 480.39
$ 240.19
$ 240.19
$ 240.19
$ 336.27
$ 336.27
Table 7.9
Sensitivity Analysis with 25% of container scanning for each scenario Small port and Big port
Scenario 1A
Operating Characteristics (25% Container Scanning)
Scenario 2B
Scenario 2C
Scenario 2D
Small
Big Port
Small
Big Port
Small
Big Port
Small
Big Port
Big Port
1
2
1
1
1
1
1
1
1
0.41
1.54
0.81
2.20
0.81
2.20
0.81
2.20
2.21
5
10
5
10
120
120
10
10
10
8.10%
15.35%
16.25%
22.00%
0.68%
1.83%
8.13%
22.00%
22.10%
0:01:04
0:05:20
0:02:19
0:09:26
0:00:00
0:00:01
0:00:32
0:01:42
0:01:42
0:13:04
0:17:20
0:14:19
0:21:26
0:00:30
0:00:31
0:06:32
0:07:42
0:07:42
$ 247.71
$ 269.70
$ 253.01
$ 292.29
$ 240.64
$ 241.43
$ 342.11
$ 354.97
$ 691.35
$ 604.16
$ 175.13
$ 312.36
$ 132.86
$ 297.09
$ 109.74
$ 422.36
$ 161.35
$ 160.67
$ 7.51
$ 29.51
$ 12.82
$ 52.09
$ 0.45
$ 1.24
$ 101.92
$ 18.70
$ 18.81
$ 240.19
$ 240.19
$ 240.19
$ 240.19
$ 240.19
$ 240.19
$ 240.19
$ 336.27
$ 336.27
122
Scenario 2A
7.6.2
For the 25% container scanning volume the decrease in the waiting time was not
significant. Almost the same figures were registered in both situations for the Small and
Big port. In the case of the costs the impact was extremely high for the small port. For the
scenario 2A the small port almost tripled their costs compared with the 100% scanning
legislation. Also the Big port presented a negative effect especially in the scenario 2B
where the increase compared with the 100% volume were from US$ 28 to almost US$
110. In order to have a clear idea about the impacts of the behaviors when container
scanning volumes are changed, Figure 7.16 shows the impact on average time spent in
system to 50% and 25% container scanning volumes.
Figure 7.16
Average time in system per container scanning volume
100% Scanning Time
0:40:00
50% Scanning Time
0:30:00
25% Scanning Time in
System
0:20:00
0:10:00
-0:00:00
Small
Big Port
Small
Scenario 1A
Big Port
Scenario 2A
Small
Big Port
Scenario 2B
Small
Big Port Big Port
Scenario 2C
Scenario
2D
In Figure 7.16 we observe almost the three lines overlapping each other. IN the case of
the big port the 50% scanning volume represent an increase in average time spent in
system. As well for scenarios 2C and 2D for the big port the time spent in system was
increased due the decrease of the NII scanners in the system. On the other hand, Figure
7.17 shows the behaviors of the costs derived of the three container scanning volumes.
123
Figure 7.17
Average container cost per scanning volume
$700.00
$600.00
$500.00
$400.00
$300.00
$200.00
$100.00
$Small
Big
Port
Scenario 1A
Small
Big
Port
Scenario 2A
Small
Big
Port
Scenario 2B
Small
Big
Port
Big
Port
Scenario 2C Scenario
2D
From Figure 7.17 we observe the behavior of costs of each container scanning volume.
After analyzing this graph we can conclude that according to our analysis the decrease in
container scanning volumes impact directly the service costs per container, while the
service time remained almost the same. This means that is not such a good idea to
decrease the scanning volumes as an alternative to solve congestion at ports, because the
benefit to decrease amount in waiting time is much less compared with the increase in
costs incurred by the container scanning reduction. The less the percentage of the
container scanning the higher will be the cost especially for the small ports.
Another important notation about the idea to reduce the container scanning volumes is the
possible impact that would represent for the Hub and Spoke effect. From Figure 7.17 we
have learned that the idea to change the container scanning volume will represent higher
costs. If the container scanning volumes are reduced and the costs increase, fewer ports
will be able to buy the NII technology. This situation will lead in the increase of the Hub
and Spoke effect because now fewer ports will have the U.S. Container volumes to justify
the investment of the technology. This new reallocation of container flows will result in
the implications of the Hub and Spoke effect discussed in chapter 6. For the 50%
container scanning volume, only for the top 38 ports will be feasible to obtain the NII
system representing 94% of the container flows to USA. If the 25% container scanning
volume is implemented only the top 25 ports will be able to buy the NII system,
representing the 90% of the U.S.-bound containers. The higher economic impact will be
will be reflected in all small ports.
7.7
Conclusion
In this chapter we conduct a waiting line cost analysis in order to measure the economic
impacts derived from the implementation of the 100% Container Scanning Law. Based in
124
the analysis of the waiting time spent in the system from chapter 6 and with the cost
analysis of the waiting lines in this chapter we could measured the optimal number of NII
scanners in the system. These estimations include the analysis of the waiting lines cost,
the transfer costs derived from transshipment containers, and the service costs attributed
to all the direct costs related with the operation of the NII scanner itself. We learned that
the optimization of the NII scanners required to provide the scanning service is a tradeoff
relationship between the waiting time of service and the total costs. Form the economic
and congestion impact analysis we estimate which scenarios will represent greater
impacts than others. From the cost analysis we conclude for scenario 2C and 2D that the
feasibility to invest in new technology was more feasible for the big port than the small
port, where improved the unitary costs and the service time. At the end of the chapter we
perform a sensitive analysis in order to observe the behaviors of the waiting lines due the
decrease in the container scanning volumes. After this analysis we can conclude that is
better to have a 100% Container Scanning Legislation, than a 50% or less Container
Scanning Legislation. We conclude that the lower container scanning volume the higher
cost will represent, especially for the small ports. This change in container scanning
volumes will impact directly the Hub and Spoke effect, because fewer ports will be able
to justify the investment of NII systems, increasing US-bound containers for reallocation.
125
8 Conclusions
8.1
Final thoughts
The uncertainty whether or not the 100% Container Scanning Legislation will be applied
is one of the major concerns for the shipping industry. In an interview with Mr. Jurjen
Duintjer, Project Manager Security of the port of Rotterdam, when he was questioned
about whether the 100% Container Scanning Law would be implemented in 2012, he
answered “To be honest, at this moment I would not know where to put my money. It is a
50-50 percent chance”. This quote explains the feeling of most of the members of the
shipping community. This legislation has been a hot issue nowadays, but so many things
have been told about this law, that at this moment it is hard to predict which direction the
security measure will take in the following years. Also the many discussions and stories
about the law have given rise to wrong ideas or interpretations of what is intended and
what may be the consequences. This study provides important answers to many important
questions regarding the main motivations behind the law and its main implications,
notably on waiting lines and (economic) costs.
Management and Logistic Operation
There exists a considerable possibility of the 100% Container Scanning Legislation to be
implemented by 2012. If this situation occurs as the government of the United States has
planned, major changes must been made to the current operations at container terminals.
These changes include reorganization of the operations especially to capture
transshipment flows. In our study we have demonstrated that congestion delays through
the arrival of containers by gate could be tackled by improved technology or by
increasing the number of NII scanners in the system. The real problem is identified in the
possibility to scan high volume transshipment containers. Many possible outcomes from
this legislation result in a possible reallocation of container flows from small U.S. bound
ports to big U.S. bound ports. In this study the possibility to include the transshipment
flows into the Integrated Scanning System is assumed, but in reality this operation can be
even more complex than it seems. In multimodal ports, like Antwerp or Rotterdam that
combine different modes of transport (barge, rail and short sea shipping) in their
transshipment operations, difficulties are present to separate the container flows in order
to establish an Integrated Scanning System to capture the US container flows. Thus, a
massive restructuring in the terminal operations around the world is needed to apply
before the implementation of the legislation can take place successfully.
Economic Costs
The feasibility to implement the 100% Container Scanning Legislation is not only a
matter of Queues and Delays. The economic factor plays a deterministic roll in the
possibility to implement the legislation successfully. As mentioned in this study a
127
security policy must be efficient, measured in terms of possible benefits derived from its
implementation against operational feasibility and incurred costs. The direct costs of the
policy are the total costs incurred by the Integrated Scanning System (ISS) and the costs
derived from the operation of the ISS itself. The Indirect costs must include the delays
and congestion associated with the implementation of the policy, and the transfer costs
derived from the transshipment containers. Based on our calculations, a rough estimate of
the total annual costs of the 100% Container Scanning Legislation is approximately US$
2 billion plus US$ 500 million derived from transfer costs per year. The importance to
address a reasonable estimate of congestion costs to the cost analysis equation is essential
in order to assess the real economic impact of the legislation. The account of an indirect
cost like waiting lines can be a determinant for decision makers to select the appropriate
number of NII scanners for each port. Until now, this cost has not been (sufficiently)
considered by the shipping community. At some point the burden of the waiting line costs
will be split by some shipping stakeholders but at the end this cost will be reflected in the
overall cost of the global supply chain, and therefore borne by the final consumer.
Technological
The importance of the upcoming developments and improvements to the equipment of
security will play a substantial role in the course of this controversial legislation. The
Development of new technologies will be crucial for the implementation of the 100%
Container Scanning Legislation. If the security developers reach major improvements
within the following years, the possibility to implement the legislation successfully will
be higher. As mentioned before, the principal challenge for security providers is to create
an effective and efficient system of massive scanning for transshipment container flows.
If an effective system for transshipments is created a great battle is won for the supporters
of the legislation. On the other hand, if no major technological achievements are reached
in the next years, the feasibility to implement the law would be minimal. Derived form
our study we estimate the effects of new technology into the Integrated Scanning System.
The acquisition of new technology will only benefit the big ports, because the container
volumes handled by these ports can support the increase in operation costs derived from
the new technology. On the other hand, if small ports acquire new technology, because of
their low container volumes the unitary costs per scan will impact more strongly than in
big ports. Also derived from this study, we assess the possible flow of containers that
might face second inspections and alarm inspections derived from the implementation of
the law. In this sense, we estimate that based on the current capacity volumes in the
second and alarm inspections, these systems will be blocked due to the new container
flows. According to this, the utilization of new technology like the ASP systems must be
included or the number of personnel to perform these inspections must be increased, in
order to avoid saturation and overcrowding of the system. Any of these alternatives will
represent an increase in operational cost of the Integrated Scanning System. As a result,
this represents a disadvantage for the small ports because the economic impact for the
small ports per unit will be higher than for big ports.
Hub and Spoke
In this study we estimate the possible allocations of container flows in the case a Hub and
Spoke effect is derived from the 100% Container Scanning Legislation. Many counties
are betting to capture transshipment flows if they provide the scanning service for the
U.S. bound containers. In our findings we conclude that only for the ports located in the
128
Africa and Oceania continents the legislation will represent a substantial advantage
derived the Hub and Spoke effect in terms of relative market share. For Europe there is a
substantial increase in absolute terms with respect to the number of containers, but not so
much in relative terms. On the contrary, for countries from the Asian continent the Hub
and Spoke system represents a minor increase in the U.S. bound container market. For
those countries that are expecting to capture big flows of container derived from this new
legislation, it is important to consider whether they will get as many flows of containers
as they are hoping for. It is important to mention that the hub and spoke effect implies the
scan of transshipment containers. Thus, also for the hub and spoke effect it is necessary to
develop new technologies in order to capture transshipment containers into the Integrated
Scanning System (ISS). Another problem of the Hub and Spoke effect is the necessity to
incur in transfer costs. These transfer costs almost represent the same cost as the ISS
service cost itself. Then if the small ports, most of which are located in developing
countries, need to reallocate their container flows because their ports are not be able to
acquire ISS equipment, the economic impact for these container flows will be higher and
may have poverty-increasing effects. These flows not only will have to pay the extra cost
to reallocate to other ports that offer the ISS service, but also they will have to pay for the
extra transfer costs. This situation may lead to in increase in the gap between the rich and
poor countries.
Political
The political factor, a factor beyond the scope of this research, but nonetheless vital for
the final process outcome regarding the 100% Container Scanning Law, represents a
determinant for the future of the legislation. Many shipping stakeholders are expecting
reconsideration from the U.S. government in order to avoid the implementation 100%
Container Scanning Law when the new administration enters into the White House. The
intentions of the new US federal administration are unpredictable, but it is important to
note that the Congressmen who pushed most for the approval of the legislation were from
the Democratic Party. Not sure at this point, but if the Democratic nominee would capture
the White House and if the Houses of Congress would be Democrat controlled, it looks
unlikely that a new Democratic president will go against the ideology of security
measures adopted by his own Congressmen party. Another important political issue is the
role that foreign governments play in lobbying with respect to this piece of legislation.
Whether the legislation is implemented or not, it is going to depend in big part on the
political pressure put by the main US trade partners, especially regarding the cost bearing
responsibilities of the law. However, lately no comments have been uttered regarding the
implementation of the 100% Container Scanning legislation. This is most likely due to
the fact that governments wait for the posture adopted by the new U.S. administration
after the November 2008 elections.
Reciprocity
The demand for reciprocity by foreign governments to the United States is one of the
possible outcomes derived from the implementation of the law. This study is designed to
provide a close estimate of the impacts derived from the 100% Container Scanning Law
to any port, including the U.S. ports. The cost and congestion impacts would be similar,
with the same implications for the U.S. ports if for example the EU, China or ASEAN
would insist on the same law applying to container imports into their countries. Many
members of the shipping community expect that – if reciprocity is demanded by foreign
129
governments to the U.S. - the U.S. will reconsider the implementation of the law.
However, the possibility also exists that the U.S. agree to implement 100% Container
Scanning of their exports to any country that requests this mechanism. The United States
like a few other countries have the resources to include this type of mechanism into their
supply chain nodes.
Lower Scanning Percentages
Derived form our sensitivity analysis we estimate the possibility to reduce the scanning
volumes in order to improve the application of the law. This alternative has been
mentioned by some members of the shipping community as an alternative solution to
relieve the expected impact of the legislation. However, our results show a different
picture. The effects of scanning lower percentages of containers do not look like what
some members of the shipping community have thought. If the container scanning
volumes are reduced, the unitary costs to provide the service will increase. This effect
largely offsets the reduction in containers. Therefore a 50% reduction in containers from
100% to 50% will only lead to 7% cost savings. The same cost reduction applies when
25% of the containers are scanned. Moreover, on top of the limited cost reduction due to
the unitary cost increase of scanning, 50% or 25% scanning is relatively more negative
for the small ports, where such measures have much higher per unit impact than for the
big ports. This is because the direct service costs remain the same and the service
volumes decrease significantly, pushing the cost factor towards the asymptotic limit. The
more we decrease the container scanning volume the higher the impact is on unitary
costs. Another effect, derived from the decrease in the container scanning volumes is a
possibility that relates to the Hub and Spoke effect. Derived from the diminution of
scanning levels, fewer ports would be able to acquire the Integrated Scanning System
(ISS) technology. This would represent higher flows of transshipments derived from the
shrinking of the ports that will be able to provide the service, carrying higher flow of
transshipments liked with the problems discussed previously.
Best Scenario
If the 100% Container Scanning Legislation is implemented in July 2012, among all the
scenarios that were suggested in this study derived from its implementation, the less
harmful for the shipping community is Scenario 2D. This scenario is the most efficient
according to the feasibility of operation and the costs incurred to provide the service.
Under scenario 2D we assume Gate as well as Transshipment container flows bound for
the United States. It is performed with the utilization of current Integrated Scanning
System (ISS) technology in order to provide the service. And it is assumed No NonIntrusive Image (NII) analysis by officers or operators at foreign port, but held at the U.S.
Targeting Service Center in Virginia. The idea to perform the NII analysis in the United
States remarkably improves the capacity of service of the Integrated Scanning System
(ISS). Also it decreases the total cost of service per container. However, performing this
analysis at the U.S. government installations in the United States will represent the
increase in personnel from a current 50 people to roughly 1,875 people working 24 hours
a day in order to analyze all the images arriving from around the world (Carluer [2008
p.162]).
After summarizing these key issues it is important to remark that whether or not the 100%
Container Scanning Legislation is applied or not in 2012, this study is an important
130
exercise to assess the behavior of the container waiting lines derived from a security
policy that might affect for good or bad the wellbeing of the shipping industry and
associated with the waiting line the potential effects on economic costs. Until 2012, the
evolution of the 100% Container Scanning legislation will take different forms, new
information will appear and other researches will complement this study in order to
measure this new security mechanism for the shipping industry. At the end, there will be
winners and losers derived from this law. Those who are expecting to have benefits and
others who expect to avoid high costs derived from the implementation of this new law.
Only time will decide the outcome of the legislation.
8.2
Limitations of the study
The outcomes derived from this study have been based in scientific models, data and
assumptions. This study has been based in figures from data analysis, articles,
questionnaires and interviews from members of the shipping community. All the figures
utilized in this study are based in authentic published figures unless some assumptions are
remarked. The model employed to assess the calculations of this study are feasible for
any particular port, but real input data must be obtained in order to produce accurate
output from the model. In the case of the transshipment flows, this study assumes a
constant arrival of containers. In order to assess a correct behavior of container arrival
from transshipments a study including port calls and other intermodal transshipments
flows arrival behaviors must be assessed.
8.3
Areas for future research
For following research is suggested to include a detailed transshipment container analysis
in order to asses the real impact of transshipments in the Integrated Scanning System.
This study must include real ship calls in order to assess the variations of flows through
the system in order to determine the real impact of transshipment flows. Also container
transshipments must be tackled from the diverse modes of connections: Barge, Rail and
Short-sea shipping. In order to have a more accurate estimate of the possible reallocation
of the transshipment flows derived from the Hub and Spoke effect, other factors and
methods must be accounted including gravity models to provide a closer estimation of the
container flows.
131
Annex I References
Ahern, Jayson (2008). Written Statement of the results of Secure Freight Initiative pilot
ports. Deputy Commissioner, U.S. Customs and Border Protection. Hearings by U.S.
Senate Committee on Commerce, Science and Transportation, focused on the SFI initial
pilot program results required by the SAFE Port Act 2006. Washington, D.C., June 2008.
American Automobile Association (2008). Daily Fuel Gauge Report. Obtained
September 1, 2008 from http://www.fuelgaugereport.com/index.asp.
American Highway Users Alliance (2000). Saving Time, Saving Money: the Economics of
Unclogging America’s worst bottlenecks. Obtained September 1, 2008 from
http://ntl.bts.gov/lib/7000/7400/7477/timemoney.pdf.
Anderson, David; Sweeney, Dennis; Williams, Thomas; Martin, Kipp (2008).An
Introduction to Management Science. 12th Edition. Ohio, USA: Thomson South-Western.
Autobloggreen (2008). Diesel becoming more expensive than gasoline in Europe.
Obtained September 1, 2008 from http://www.autobloggreen.com/2008/03/20/dieselbecoming-more-expensive-than-gasoline-in-europe/.
Caldwell, Stephen (2008). Written Statement of the results of Secure Freight Initiative
pilot ports. Director of Maritime Security Issues of U.S. Government Accountability
Office. Hearings by U.S. Senate Committee on Commerce, Science and Transportation,
focused on the SFI initial pilot program results required by the SAFE Port Act 2006.
Washington, D.C., June 2008.
Chopra, Sunil and Meindl, Peter (2007). Supply Chain Management, Strategy Planning &
Operation. New Jersey: Pearson Prentice Hall.
CNNMoney (2005). Another record at the pump. Obtained September 1, 2008 from
http://money.cnn.com/2005/08/16/news/economy/gasoline/index.htm.
Crauler, Frederic (2008). Global Logistic Chain Security, Economic Impacts of the US
100% Container Scanning Law. Paris, France: Editions EMS.
Department of Homeland Security (2005). National Plan to achieve Maritime Domain
Awareness. Obtained on July 30, 2008 from
http://www.dhs.gov/xlibrary/assets/HSPD_MDAPlan.pdf.
Department of Homeland Security (2006). Automated Targeting System. Obtained on
July 30, 2008 from http://www.dhs.gov/xlibrary/assets/privacy/privacy_pia_cbp_ats.pdf.
133
Department of Homeland Security (2007a). Strategy to Enhance International Supply
Chain Security. Obtained on July 30, 2008 from http://www.dhs.gov/xlibrary/assets/plcyinternationalsupplychainsecuritystrategy.pdf.
Department of Homeland Security.(2007b). DHS Health and Safety Report on NonIntrusive Imaging (NII) container scanning technology. Washington, D.C. Obtained from
http://aapa.files.cmsplus.com/PDFs/NII%20Health%20and%20Safety%20Report%20to%20Congress%207%
206%202007%20-%20Final.pdf.
Department of Homeland Security.(2008).Container Security Initiative Ports. DHS.
Obtained on July 30, 2008 from
http://www.dhs.gov/xprevprot/programs/gc_1165872287564.shtm.
Domestic Nuclear Detection Office. (2006). DNDO Overview. Obtained August 11, 2008
from http://www.aaas.org/spp/rd/Forum_2006/oxford.pdf.
Drewry (2004). Container Shipping: Overcapacity inevitable despite increasing demand.
Obtained August 1, 2008 from http://www.dbresearch.com/PROD/DBR_INTERNET_ENPROD/PROD0000000000198081.pdf.
Duintjer, Jurgen. (2008). Interview by Miguel Tovar: Mr. Jurgen Duintjer, Project
Manager Security of the Port of Rotterdam. September 2, 2008. World Port Centre,
Rotterdam, the Netherlands.
Implementing Recommendations of the 9/11 Commission Act of 2007. (2007). 110th
Congress of the United States H.R.1. Obtained on August 1, 2008 from
http://frwebgate.access.gpo.gov/cgibin/getdoc.cgi?dbname=110_cong_bills&docid=f:h1enr.txt.pdf.
International Maritime Organization.(2002). IMO adopts comprehensive maritime
security measures. Obtained July 30, 2008 from
http://www.imo.org/Newsroom/mainframe.asp?topic_id=583&doc_id=2689#code.
Homeland Security Act of 2002. (2002). Obtained on August 1, 2008 from
http://www.whitehouse.gov/deptofhomeland/bill/hsl-bill.pdf.
Huizenga, David. (2008). Written Statement of the results of Secure Freight Initiative
pilot ports. Assistant Deputy Administrator, Defense Nuclear Nonproliferation of the
National Nuclear Security Administration of the Department of Energy. Hearings by U.S.
Senate Committee on Commerce, Science and Transportation, focused on the SFI initial
pilot program results required by the SAFE Port Act 2006. Washington, D.C., June 2008.
Lee, Hau L. and Whang, Seungjin (2003). Higher supply chain security with lower cost:
Lessons from total quality management. Elsevier.
Martinosi, S.E, Ortiz, D.S. and Willis H.H. (2006). Chapter 12: Evaluating the viability
of 100 percent container inspection at America’s ports. From the book “The Economic
134
Impacts of Terrorist Attacks” by Harry Richardson, Peter Gordon and James Moore II.
U.S.A.: Edward Elgar Publishing.
Mayerson, Roger. (2005). Probability Models for Economic Decisions. U.S.A.: Thomson
Brooks/Cole.
Mechanical Solutions group. (2008). Straddle Carrier Portal. Obtained August 11, 2008
from http://mechanical.pnl.gov/projects/development/straddle.stm.
Merriam-Webster Dictionary. (2008). “Indirect cost definition”. Obtained August 1st,
2008 from: http://www.merriam-webster.com/dictionary/Indirect%20costs.
National Commission on Terrorists Attacks Upon the United States. (2004). The 9/11
Commission Report. Obtained on August 1, 2008 from
http://govinfo.library.unt.edu/911/report/index.htm.
National Nuclear Security Administration. (2008). Megaports Initiative. Obtained on July
30, 2008 from http://nnsa.energy.gov/nuclear_nonproliferation/1641.htm.
Ortec (2008). Detective ASP Series: Vehicle, Rail and Freight cargo Monitors. Obtained
August 11, 2008 from http://www.ortec-online.com/pdf/asp-truck.pdf.
Port Import Export Reporting Service (PIERS) (2008). Obtained August 1st, 2008 from:
http://www.marad.dot.gov/Marad_Statistics/index.html.
RAND New York (2005). Traffic Congestion Statistics Glossary. Obtained August 1st,
2008 from: http://ny.rand.org/stats/community/trafficcong.more.html.
Sannen, Yvo (2008).Terminal as link in the intercontinental supply chain. TBA,
Rotterdam, March, 2008.
SAIC (2008a). VACIS P7500 Imaging System. Obtained August 11, 2008 from
http://www.saic.com/products/security/pdf/VACIS-P-7500.PDF.
SAIC (2008b). “Gamma Image”. Obtained August 11, 2008 from
http://content.edgar-online.com/edgar_conv_img/2007/10/09/0001193125-07214818_G39955EX99_1S94GBGD.JPG.
SAIC (2008c). GR-135 Plus Identifier Radioactive Isotope Identification Device.
Obtained August 11, 2008 from
http://www.saic.com/products/security/gr-135/.
Security and Accountability for Every Port Act of 2006. (2006). 109th Congress of the
United States H.R.4954. Obtained on August 1, 2008 from
http://frwebgate.access.gpo.gov/cgibin/getdoc.cgi?dbname=109_cong_bills&docid=f:h4954enr.txt.pdf.
135
Shell Singapore. (2008). Shell station price board. Obtained August 1st, 2008 from:
http://www.shell.com/home/content/sgen/shell_for_motorists/fuels/discounts/price_board_0720.html.
SwizStick. Port/terminal congestion and BAF costs challenge carriers.Obtained August
1st, 2008 from
http://www.3plwire.com/2007/06/19/portterminal-congestion-and-baf-costs-challengecarriers/.
TSA Systems. (2008). Radiation Portal Monitors. Obtained August 11, 2008 from
http://www.tsasystems.com/products/portal.html.
Transport Security Administration. (2008). TWIC Program Information. Obtained on July
30, 2008 from http://www.tsa.gov/what_we_do/layers/twic/program_info.shtm.
US Customs and Border Protection (2003). Enforcement of 24 Hour Rule Begins
February 2. Obtained on 30 July, 2008 from
http://www.cbp.gov/xp/cgov/newsroom/news_releases/archives/cbp_press_releases/0120
03/01302003.xml.
US Customs and Border Protection (2004). Customs-Trade Partnership Against
Terrorism Strategic Plan. CBP. Obtained on July 30, 2008 from
http://www.cbp.gov/linkhandler/cgov/trade/cargo_security/ctpat/what_ctpat/ctpat_strate
gicplan.ctt/ctpat_strategicplan.pdf.
US Customs and Border Protection (2006). Container Security Initiative: Strategic Plan
2006-2011. CBP. Obtained on July 30, 2008 from
http://www.cbp.gov/linkhandler/cgov/trade/cargo_security/csi/csi_strategic_plan.ctt/csi_
strategic_plan.pdf.
US Customs and Border Protection (2007). SFI Scanning at a Glance. CBP. Obtained on
July 30, 2008 from
http://www.cbp.gov/linkhandler/cgov/newsroom/fact_sheets/trade_security/sfi/sfi_scanni
ng.ctt/sfi_scanning.pdf.
U.S. Customs and Border Protection (2008a).Secure Freight Scanning at a Glance.
http://www.cbp.gov/linkhandler/cgov/newsroom/fact_sheets/trade_security/sfi/sfi_scanni
ng.ctt/sfi_scanning.pdf.
U.S. Customs and Border Protection (2008b).Secure Freight Scanning at a Glance.
http://www.cbp.gov/linkhandler/cgov/newsroom/fact_sheets/trade_security/sfi/sfi_technol
ogy.ctt/sfi_technology.pdf.
US Customs and Border Protection (2008c). Report to Congress on Integrated Scanning
System Pilots (Security and Accountability for Every Port Act of 2006, Section 231).
Obtained on August 6, 2008 from
136
http://commerce.senate.gov/public/_files/SFIReport_PublicRelease_FINAL_Consolidated
.pdf.
US Coast Guard (2008a).Nationwide Automatic Identification System (NAIS).Obtained on
July 30, 2008 from http://www.uscg.mil/nais/.
US Coast Guard (2008b).International Port Security Program (ISP Code).Obtained on
July 30, 2008 from http://homeport.uscg.mil/mycg/portal/ep/browse.do?channelId=18389.
U.S. Government Accountability Office (2007). Combating Nuclear Smuggling. Obtained
on September 1, 2008 from www.gao.gov/new.items/d07581t.pdf.
U.S. Senate Committee on Commerce, Science and Transportation (2008). Hearings from
Supply Chain Security: Security Freight Initiative and the Implementation of 100 Percent
Scanning on June 12, 2008. Archive video webcast obtained on August 6, 2008 from
rtsp://video.webcastcenter.com/srs_g2/commerce061208.rm.
Veritainer-Amptek (2008). Radiation Detector for Intermodal Shipping Containers.
Obtained August 11, 2008 from http://www.amptek.com/pdf/gammarad.pdf.
Victoria Transport Policy Institute (2006). Transportation Cost and Benefit Analysis.
Obtained on August 1, 2008 from http://www.vtpi.org/tca/tca0505.pdf.
World Customs Organization (2007). SAFE Framework Standards. Obtained on July 30,
2008 from
http://www.wcoomd.org/files/1.%20Public%20files/PDFandDocuments/SAFE%20Frame
work_EN_2007_for_publication.pdf.
World Shipping Council (2007).Statement Regarding Legislation to Require 100%
Container Scanning. Obtained on August 1, 2008 from
http://www.worldshipping.org/wsc_legislation_statement.pdf.
137

The 100% Container Scanning Legislation

Transcription

Similar documents

1_innopas pi/iisc modular tunnel pasteurizer

Flatbed

instructions

Terrarium Design

Napoleon Container Terminal

BOX BULL

PDF 1mb - Marina Adelaide

Port Operations

CONTAINERS Cement Washout Container