Manufacturing - How to maximize the use of data

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Manufacturing - How to maximize the use of data
Prasad Shyam, General Manager & Global Business Head
MFG, HLS & ENU Analytics and Information Management(A&IM)
Wipro Technologies
Table of Contents
1. Capitalizing Data to Transform Manufacturing Operations ............................. 03
2. Freedom from spread sheets .................................................................................. 04
3. What you should do right now .............................................................................. 04
4. How did analytics get to be center stage? ........................................................... 04
5. High impact tool ........................................................................................................ 05
6. The 8 key success factors ........................................................................................ 06
Capitalizing Data to Transform Manufacturing Operations
Manufacturing is no stranger to data. However, over the last decade or so,
new sources of data are leaving manufacturing buried under piles of
numbers. This is because manufacturing continues to wield traditional
tools such as spreadsheets. These tools are incapable of handling the new
volumes, velocity and variety of data. Is it time for manufacturing to
ponder over how to maximize the use of data? Recent research
commissioned by Wipro showed that five out of six manufacturers do not
have appropriate data management strategies to help improve the quality
of their decision-making. The problem demands urgent attention.
An Economist Intelligence Unit survey, 2013, commissioned by Wipro
Technologies called `The Data Directive' showed that manufacturing
(16%) and retail (13%) are the least prepared with data management
strategies (See Figure 1 for a comparison between manufacturing and other
industries from the research). Other industries are getting ahead of
manufacturing in the use of modern data management and analytics. The
study indicates that manufacturing has much ground to cover before it can
begin to leverage data and analytics. The challenge before manufacturing is
to integrate vast volumes of core ERP data with market intelligence,
Data-driven industries
(% respondents)
56.3
60
Manufacturing
Financial Services
45.7
50
Professional Services
35.1
37.8
40
We have a welldefined data
management strategy
that focuses resources
on collecting and
analysing the most
valuable data
We understand the
value of our data and
are marshalling
resources to take
better advantage of
them
We collect a large
amount of data but do
not consistently
maximize their value
We collect data but
they are severely
underutilized
2.2
3.3
3.1
2.2
0
3.5
6.5
6.7
6.7
6.3
8.8
10
15.2
20
12.5
13.3
15.8
20
21.9
30
Technology, Media and Telecom
30
30.4
40
36.8
40
Retail and Consumer Goods
We do not prioritize
data collection
Figure 1
Source: Economist Intelligence Unit Survey
03
customer behavior, social data, , device data, logistics, partners, channel
performance, service opportunities, etc. In real terms it means overhauling
the tools used for data management. Manufacturing needs to switch to
sophisticated cutting edge tools to improve the efficiency of its operations
and the accuracy of its decision-making.
The Economist Intelligence Unit study showed that amidst the data
stockpiling now under way in manufacturing, clarity on which data matters
most is the biggest barrier to move forward (40%). As many as 34% of the
executives in the study worried “that the quality of their decisions are
actually being impaired by data overload.”
Over the last decade analytics has grown in importance. It has become the
central tool to improving efficiencies, reducing costs, adhering to
environmental norms, improving compliance monitoring/reporting and
addressing security concerns. Today it is indispensable. Manufacturers are
indicating their acute need for predictive analytics through questions
such as:
 How can I reduce down time of equipment without the expense of
routine preventive maintenance?
 How can I discover a potential product defect even before the
customer knows it?
 How can I improve traceability of defective components?
Freedom from spread sheets
Fortunately, for some manufacturers, the historical dependence on spread
sheets is on the wane. They are the ones that are building a strategic
response to fluctuating economic conditions, rise in raw material costs,
volatility in demand, higher customer expectations with regard to quality,
shrinking product cycles, and shorter forecast horizons. These select
manufacturers are capitalizing on data, maximizing its value, leading to
significant transformation in their manufacturing operations.
The Economist Intelligence Unit Report clearly points to analytics being a
differentiator in many businesses. The report shows that the key to
success of high growth firms is “the fact that they have done more to
reorganize their structures and leadership around data and to introduce
data management strategies.” As an example, the study showed that
10.7% of high-growth firms collect machine generated data (sensors,
RFID, network logs, telematics, etc). Ironically, 14.3% no-growth firms – a
shade more than high-growth firms - collect the same data. The obvious
question that begs an answer is: if no-growth firms collect more data than
high-growth firms, why do they lag in growth? This is because 46.6% of
high-growth firms collect and analyze the data while only 33.3% of nogrowth firms do the same. The difference lies in analyzing the data, not
collecting it.
Other independent research has begun to show that those investing in
analytics were also the most likely to be innovative and experience an
increase in operational efficiencies. Interestingly, one study showed that
only 10% were using predictive analytics. This means that manufacturers
who use predictive analytics and go from being reactive to being proactive can gain substantial advantage over those who don't. These are the
manufacturers who are extending their focus from traditional market
surveys and pilot products to leveraging social media, telemetry data from
products to discover new services that customers want, and reduce after
sales costs. And this is just the beginning of what data and analytics can
enable.
What you should do right now
Analytics until recently uncovered trends, sentiments, root cause for
failure, etc. by digging into data from the past. It produced reports that
helped answer modest questions: how much did that delay in delivery
cost us? How much did we add to bottom line by shaving off part of a
process?
 How can I avoid shipping defective components and reduce returns?
 How can I contain warranty costs?
Manufacturers are saying, “I don't want reporting on yesterday or alerts on
today; just tell me what my business should be doing this morning.” In fact
analytics is providing insights into what manufacturers should be doing at
this very moment.
This is a sophisticated capability, unthinkable until now. It is possible to
achieve this because conventional enterprise data sources (SCM, CRM,
HCM, etc.) are now being supplemented by data from social media
(Twitter, Facebook, YouTube, etc.), device and machine data (sensors,
meters, RFID tags, CDR, mobile devices, computer logs, cameras, GPS,
etc.) and interaction data (credit cards, clickstream, etc.).
'Listening' to the enormous data stream being generated by men and
machines can show up patterns that help predict the future: How much
time is left between a vehicle component failure and an accident? Can we
call the vehicle in for a check before the accident? When is a turbine likely to
go down? Can early intervention reduce plant downtime when the turbine
fails? How can I overcome my current manufacturing constraints when
demand bumps up? Or better still, can I predict the surge in demand and
prepare my plant for it? Sophisticated models and algorithms can forecast
the future and provide decision support to manage those emerging
scenarios in real time.
How did analytics get to be
center stage?
There are radical changes sweeping the industry and the way it consumes
information. Data has become a key driver of manufacturing and is the
fundamental building block of smart systems. It is fueling new waves of
efficiency and productivity.
Manufacturing is moving away from descriptive reporting (What
happened? When? How? How often? What was that outlier trying to say?)
and query for detail (What is the excitement about? Why did it happen?
What is the problem?). It has also begun to evolve beyond alerts (What is
the required action now? What is likely to happen?) towards predictive
forecasting (What could happen in the near future? What is this trend trying
to tell us? How should we respond?). In many ways we are at the most
complex level of generating and consuming business intelligence - the
prescriptive level (What is the best outcome if we don't omit all the
variables?).
04
Evolution: How we consume data
There are radical changes sweeping the industry and the way it consumes information. Data has become a key driver of
manufacturing and is the fundamental building block of smart systems and is fuelling new waves of efficiency and productivity.
t
pac
im
ess
in
Volume and complexity of data
Bus
Reporting
Descriptive
reporting: What
happened? When?
How? How often?
What was that
outlier trying to say?
Query
Alerts
What is the
excitement about?
Why did it happen?
What is the required
action now? What is
likely to happen?
Predictive
What could happen
in the near future?
What is this trend
trying to tell us?
How should we
respond?
Prescriptive
What is the best
outcome if we don't
omit all the
variables? Based on
sensors, CEP, Social
network analysis etc.
Enterprise manufacturing intelligence
High impact tool
Analytics has become a high impact tool across manufacturing:
Enhancing quality: Analytics can surface patterns from product problems.
These patterns can be used to improve product quality, thereby increasing
customer satisfaction, product safety and reducing potentially expensive
product returns. Reducing returns through identification of defects before
shipment can save millions of dollars in recalled products. Toyota alone,
for example, recalled 2.7 million vehicles late last year for steering and
water pump related problems at an estimated cost of half a billion dollars1.
Superior warranty management/product returns/fraud detection:
Adequately armed with predictive analytics, manufacturers can
understand when products or parts are likely to fail. This can help
organizations create better service, repair and warranty policies. Quality
departments can also drill down to the level of batch or unit that is likely to
fail, predicting product returns. This gives manufacturers an opportunity to
pro-actively recall products, send replacements and ensure customer
dissatisfaction is minimized. Increasingly, predictive analytics is being used to
detect suspicious or fraudulent warranty claims, thus reducing financial loss.
Improved maintenance capabilities: One of the major reasons that can be
attributed to maintenance costs of equipment is that manufacturers often
fall back on periodic maintenance schedules to prevent downtime.
Downtime can generate negative customer sentiment, impact SLAs, loss of
reputation, inability to meet compliance norms (in cases such as medical
equipment), and can add to costs through standby equipment, high spare
parts inventory or result in financial impact due to disruption in production.
Manufacturers have also invested in service teams to provide on-site
maintenance. Servicing can become expensive if the service team does not
have the right spares before the service request is made. In cases involving
medical equipment, valuable time can be lost in patient care. Device data
combined with regression models that are at the core of predictive
analytics could help ensure just-in-time preventive maintenance.
Regression models create relationships between interacting elements and
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variables to forecast breakdowns, thereby helping optimize field
personnel, availability of spares, etc. saving millions in unnecessary routine
maintenance costs.
Enabling service discovery: Service discovery can be the key to creating
fresh revenue streams. Services typically result in between 7 to 12% of
revenues through the lifecycle of a product. Service discovery can be
based on data from remote monitoring of the asset, analyzing equipment
usage, service schedules, customer feedback (CRM + social), contracts
(AMC), etc. As an example, telemetry data from a vehicle may suggest
that a battery failure is eminent. The manufacturer can call in the vehicle
for a replacement before the next scheduled maintenance.
Driving regulatory compliance: With increased regulatory pressure and
safety standards, traceability of products and their components is
becoming extremely important. Manufacturers, especially in industries
such as automotive, medical devices, high tech and defense need to be
able to capture, maintain and recall mountains of very granular data such
as component number, model, manufacturer/supplier/place of origin,
lot/ serial number, plant floor for assembly, production date and time,
dispatch, distribution etc. When required, analytics can sift through this
data to generate compliance reports. But more importantly analytics can
predict when compliance norms will be violated and how these violations
can be prevented.
Impacting demand planning and inventory management: Analytics is
turning manufacturers from being product centric to being customer
centric. Insights into sales and markets are helping manufacturers adjust
their inventory levels, production schedules, order fulfillment rates,
shipment, etc. Even a 5% improvement in demand planning – based on
data for holidays, seasonal changes, etc. -- can lead to significant bottom
line gains.
The 8 key success factors
The decision to leverage analytics begins with two simple questions, “Is it
worth analyzing the data? What is the return I can expect from investments
in analytics?” The analytics journey begins by identifying business areas
where it can create impact. Prioritizing these opportunities becomes the
next step. We believe that success is dependent on eight factors:
1. Identifying key business processes that can create impact
2. Creating a business case and obtaining executive sponsorship
3. Availability of the right data
4. Strategy and plan + supporting infrastructure for analytics
5. Implementation partner capability, domain expertise and solution
assets
6. Effective pilot for demonstrating potential business impact and value
7. Change management
8. Measuring impact/ value created to improve on the analytics journey
Organizations today need to move towards an analytics culture and
deploying their people, processes and technologies towards fact-based
decision-making. Mixing data from within (production, sourcing, resource
availability, CRM, etc.) and outside the enterprise (weather, demographic
profiles, insurance claims, etc.), can help them anticipate and develop a
faster response to volatile markets. They can solve complex business
problems with greater accuracy and confidence. Above all, using analytics,
manufacturing can maximize the use of data to stay on top of the key
drivers of financial performance in a world where change is completely
unpredictable.
References / Citations
1. http://www.ibtimes.com/auto-industry-churning-out-more-lemons-or-more-recalls-880154
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About the Author
Prasad Shyam is the General Manager & Global Business Head for Analytics and Information Management (A & IM) focusing on Manufacturing, Automotive,
HiTech & Consumer Electronics, Pharma and Healthcare, Energy, Utilities industries. He carries P&L responsibility, strategy and operations of this unit globally.
A&IM helps customers derive valuable insights out of integrated information by bringing together the combined expertise of Analytics, Business Intelligence,
Performance Management and Information Management. The group provides consulting, business centric and technology specific analytical solutions and data
management frameworks developed through a complete ecosystem of partners, focusing on industry specific analytics, optimization and operations analytics,
Enterprise Data Warehouse, MDM, Data quality and data life cycle management.
Prasad has 18+ years of experience in IT industry, and is strategic advisor to many Fortune 500 organizations focusing on analytics and information
management. He is one of the founding members of Business Intelligence and Data warehouse practice in Wipro.
Prasad holds a bachelor of engineering in Electronics and Communications.
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