Risk Mitigation and Assessment

Risk Mitigation and Assessment
Minimizing Risk / Exposure By Limiting Samples / Products that are in the field
Marc Chester – Account Executive, Howell Marketing Services
Linda Kaer – Director, Merck & Co.
Risk
Risk is the poten*al of losing something of value. Values can be gained or lost when taking risk resul*ng from a given ac*on, ac*vity and/or inac*on, foreseen or unforeseen. 1 1Wikipedia Considerations in sampling
•  When we think of sampling, we think ini5ally about compliance. •  Sampling also raises financial considera5ons regarding the cost to manufacture and distribute samples. 3 Industry Overview
•  In 2012, the pharmaceu5cal industry spent more than $27 billion on drug promo5on. –  $5.7 billion was spent on samples © 2013 The Pew Charitable Trusts Source: Cegedim Strategic Data, 2012 U.S. Pharmaceu5cal Company Promo5on Spending (2013). TYPES OF CONSIDERATIONS Financial Regulatory Business 5 Regulatory Considerations
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Mee5ng PDMA requirements Adhering to state repor5ng requirements An5 kickback regula5ons Ensuring that HCP specialty aligns to approved, “on label” use 6 Business Considerations
•  Nega5vely affec5ng product sales by sampling. •  Nega5ve publicity via social media. 7 Financial Considerations
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Cost to manufacture (produc5on and packaging) Distribu5on (Employee, CSO, shipping) Return-­‐destruc5on (product cost, transport, verifica5on, documenta5on – cert of destruc5on) Storage (warehousing, Rep storage loca5ons) – 
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What happens at end of day? Expense created by required infrastructure (internal or outsourced) Assess levels of loss across products 8 Considerations for Sampling
The decision to include samples into a brand’s overall promo5onal plan should be based on its impact to the brand’s overall strategy. What are some considera*ons in determining whether or not to sample? 9 2 Important Areas
1.  Sample Quan55es 2.  Sample Storage 10 Sample Quantities
•  Alloca5on –  Push –  Pull •  Data Analy5cs 11 How Do You Manage Rep Field Inventory Today?
•  Do you require the reps to place an order based on need/projected need? •  Do you suggest/allocate order quan55es? •  Do you automa5cally ship product to a rep without their input? Predictive Analytics Primer
•  Predictors Rank Your Risk? •  Combined Predictors Means Smarter Alloca5on •  The Computer Makes Your Model from Your Data 13 Bring Analysis Awareness to All Levels of a Company
•  The ability to use Big Data to drive decisions in daily work is becoming a must-­‐have. •  Business analysis solu5ons must have the same ease of use, as the enterprise wants answers to specific ques5ons and KPIs. •  It must be relevant, easy to use and understand. 14 Predictive Analytics – Using Disparate Data
•  High-­‐level data makes it hard to tell a story. •  If we drill down, it is easier to analyze the data and therefore, create data models. –  85% of my tracked 5me is spent in NJ. 15 Example – Exploring Data Analytics
DATA EVERYWHERE (MEANINGFUL USE)
3 YEARS OF WORKOUT DATA
Type Bike Ride Machine Workout Indoor Bike Ride Road Cycling Road Cycling Total # of Rides 393 15 91 4 37 540 Maps Distance 346 5,441.20 0 207.00 0 1,217.25 4 121.24 0 443.75 350 7,430.44 Dura?on 377.99 10.58 80.83 2.68 29.60 501.68 Calories 243,237 7,349 kCal 55,855 1,007 19,488 319,587 •  Using simple math, we can now predict (using the data) how far I will ride in the next 12 months. •  Once again, high level data with no logic / analy5cs applied offers no real insight. 16 Push Data Model
Row Labels
Jan
Rep A
Rep B
Rep C
Rep D
Jan Total
Feb
Rep A
Rep B
Rep C
Rep D
Feb Total
Mar
Rep A
Rep B
Rep C
Rep D
Mar Total
Apr
Rep A
Rep B
Rep C
Rep D
Apr Total
Grand Total
Sum of Shipments
Sum of Disbursements Sum of Returns
Sum of Transfers
Sum of On Hand
1,000
1,000
1,000
1,000
4,000
100
900
750
750
2,500
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200
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200
700
100
250
250
1,300
1,000
1,000
1,000
1,000
4,000
100
900
750
750
2,500
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200
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200
1,400
200
500
500
2,600
1,000
1,000
1,000
1,000
4,000
100
900
750
750
2,500
500
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500
200
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200
1,600
300
750
750
3,400
1,000
1,000
1,000
1,000
4,000
16,000
100
900
750
750
2,500
10,000
500
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500
1,000
200
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200
800
1,800
400
1,000
1,000
4,200
11,500
Push Data Analytics – Trend Analysis
Push Model 2,000 1,800 1,600 1,400 Sum of Shipments 1,200 Sum of Disbursements 1,000 Sum of Returns 800 Sum of Transfers 600 Sum of On Hand 400 Linear (Sum of On Hand) 200 -­‐ Rep A Rep B Rep C Rep D Rep A Rep B Rep C Rep D Rep A Rep B Rep C Rep D Rep A Rep B Rep C Rep D Jan Feb Mar Apr Inventory Management – Ideas From Outside Our Industry.
Inventory Management in the PC World – Just in Time Inventory
•  A great example of a company using
Just In Time Inventory (JIT) is Dell,
which revolutionized the computer world
in the 1990s by selling “custom-made”
computers to customers over the phone
and, later, online. Dell does not possess
the raw materials needed to build a
computer until a customer places an
order. The company’s processes and
systems enable it to order and receive
inventory and build computers to exact
customer specifications in about the
same time it takes competitors to ship
computers sitting on warehouse
shelves.
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Inventory Management in the PC World – Just in Time Inventory
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The philosophy behind Just in Time Inventory is simple: Excess inventory is wasteful and should be minimized or eliminated if possible. Therefore, JIT systems aim to increase profitability and return on investment by reducing ordering and inventory holding costs. In a best case scenario, finished goods and services are produced only when needed at the point of sale and never even put into what would tradi5onally be called inventory. Just In Time Inventory (JITA) (A=Allocation) Data Model
Row Labels
Jan
Rep A
Rep B
Rep C
Rep D
Jan Total
Feb
Rep A
Rep B
Rep C
Rep D
Feb Total
Mar
Rep A
Rep B
Rep C
Rep D
Mar Total
Apr
Rep A
Rep B
Rep C
Rep D
Apr Total
Grand Total
Sum of Shipments Sum of Disbursements Sum of Returns Sum of Transfers Sum of On Hand
1,000
1,000
1,000
1,000
4,000
100
900
750
750
2,500
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900
100
250
250
1,500
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750
750
2,700
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900
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750
2,500
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800
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2,500
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750
2,500
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200
200
250
250
900
100
900
750
750
2,500
11,700
100
900
750
750
2,500
10,000
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800
200
200
250
250
900
4,200
Just In Time Inventory / Allocation (JITA) Data Analytics – Trend
Analysis
Just in Time Inventory Model 1,200 1,000 Sum of Shipments 800 Sum of Disbursements 600 Sum of Returns 400 Sum of Transfers Sum of On Hand 200 Linear (Sum of On Hand) -­‐ Rep A Rep B Rep C Rep D Rep A Rep B Rep C Rep D Rep A Rep B Rep C Rep D Rep A Rep B Rep C Rep D Jan Feb Mar Apr Can We Apply This to Samples?
•  What would be the chief obstacles to implementa5on? •  What are some methods to overcome the obstacles? •  What would be the chief advantages to adop5on of this methodology? Sample Allocation and Offsite Storage
•  Managing Offsite Storage Loca5ons –  “Warehouse Sub-­‐Loca5ons” 24 Warehousing Product(s)
Brad Garreo, a former FBI agent and ABC News consultant, said it was difficult to “assess storage theq only because most of it, I think, does not get reported”. He said the biggest problem with temporary storage facili5es is that they aren't built to store luxury items, such as expensive china or Persian rugs. “The locking systems are extremely poor and the ability for people to go into them twenty-­‐four hours a day sort of make them ripe for people to steal items if they have a pass to get in,” he said. Warehousing Product(s)
Pharmaceu5cal Sample Storage •  Manufacturer Warehouse or Fulfillment Vendor Partner –  The warehouse by defini5on, should be “a planned space for the storage and handling of goods and material”. –  Security Strategy Minimizing Field Risk - Samples Storage
What Storage Requirements Does Your Company Mandate? 1. 
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Climate control is cri5cal. Pest control is not nego5able. Cleanliness counts. Security needs to be priority. Discussion of delivery policies is necessary. Contract with Storage Loca5on. 27 JIT Sampling
•  Alloca5on Based on U5liza5on – A New Formula No more push shipments + No more returns + No More Expiring / Expired product = Just In Time Inventory (JITA) (Minimized Risk and Dollar Savings!) Thank You!