Tool considerations to minimize supply chain waste Predictive modelling Supply overage modelling Practitioner case studies: Demand planning scenarios

1. Risk-Based Predictive Modelling Tools to Best
Assist with Clinical Demand Planning Scenarios
V. Mark Kothapalli, R.Ph., Ph.D.
GlaxoSmithKline
Pharm. Dev. - Global Supply Operations
13-Apr-2010

2. Acknowledgements
Research Statistics Unit:
– Vladimir Anisimov; Darryl Downing; Valerii Fedorov; Frank Mannino;
Sourish Saha; Professor Richard Heiberger (Temple University)
Integrated Supply Chain:
– Chemical Development - Pharmaceutical Development
– Clinical Manufacturing - Clinical Packaging
– Demand Logistics - Supply Logistics
Project Teams
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3. Overview: Predictive Modelling
Tool considerations to minimize supply chain waste
Predictive modelling
Supply overage modelling
Practitioner case studies:
– Demand planning scenarios
3

4. What Does Our R&D Supply Chain Look Like?
4

5. Demand Planning Behaviors
Moving from a vicious circle to a virtuous circle
Supply chain intervenes to lower demand overages
High
Overages
Low
Capacity
Long
Lead
Times
Low
Certainty
Low
Forecast
Accuracy
Vicious Circle
Low
Overages
High
Capacity
Short
Lead
Times
High
Certainty
High
Forecast
Accuracy
Virtuous Circle
➢ Tool considerations to
minimize waste?
➢ How can we reduce
uncertainty?
Right Kit for Right Patient
@ Right Time & Place !
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6. Demand Forecast Modelling
Drug supply prediction through the use of risk-based statistical algorithms
developed by Research Statistics Unit, GSK:
– Uses patient recruitment modelling technique
– Evaluates probabilities of various critical events (e.g., patient arrivals at
same site in short time interval)
– Predicts the number of patients in different regions over time
– Predicts critical supply levels needed to satisfy demand in different
regions
Anisimov, V. and Fedorov, V. Statistics in Medicine, 26, 27, 4958 – 4975 (2007).
What is Risk?
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7. Risk Concept
V. Anisimov, Proceedings of the Joint Statistical Meeting, Washington, USA, August, 2009, pp 1248-1259
Risk: Probability that a study experiences a stock-out*
Default, agreed-upon risk level:  = 5%
5% risk means that on average, 1 in 20 similar studies will experience a stock-
out
If we run many similar studies, then on average one patient in _?_ may not
have the intended kit at site.
Study Design: 500 patients; 5 treatment arms; 4 active arms
Risk = 5%: 1 in 8,000 or 1.25%
* Stock-out: Intended patient kit is not available at site; requires alternative
measures (e.g. additional site visit, etc.)
Right Kit for Right Patient
@ Right Time & Place !
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8. Risk Level Dependence upon Supply Overage
and Randomization Scheme
Risk = 0.05
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
0 50 100 150 200 250 300
RiskLevel(%)
Overage (%)
Site-Based Randomization Central Randomization
( = 0.2): 0.1% probability of stock-out
( = 0.8): 0.4% probability of stock-out
Study Design: 250 patients; 25 sites; 5 treatment arms; 10 depots
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9. Risk-based Tool Specifications
Tool covers majority of study-supply scenarios:
– Central and site-based randomization
– Equal and different treatment proportions
– Single and multiple dispense studies
– Allows preloading of sites
Basic Features:
– Evaluates upper boundaries for drug supply at an agreed-upon risk-level
– Uses closed-form expressions; no Monte Carlo simulations
– Fast calculations
– Estimates demand profile over time
Anisimov, V., Pharmaceutical Statistics, 2010.9

10. Risk-based Tool Inputs & Outputs
Inputs:
– # of pts
– # of treatments
– # of depots
– # of sites
– Randomization scheme
– # of dispenses
– No preloading or preloading
– Study duration
– Delivery times
– Re-supply frequency
Outputs:
– Overage levels
– Demand profile over time
– User-friendly Excel-based interface
V. Anisimov, Drug Supply Modelling in Clinical Trials (Statistical Methodology),
Pharmaceutical Outsourcing, Mar-Apr, 2010.
R-Excel Interface
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11. Which Variables are Most Influential?
# of patients
# of sites
Recruitment duration
Re-supply intervals
Randomization scheme
# of treatments
# of depots
Delivery times
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12. Optimal Balance Between Risk and Overage?
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Overage
Risk

13. Impact of Study & Supply Logistics Assumptions
Assumption Risk Overage
Study Design:
Open-label
Double-blind
# of Treatment Arms
Randomization Scheme:
Central
Site
Regional Allocation
Pre-Loading
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14. Supply Overage Modelling/Forecast Considerations
Estimate clinical supply requirements based upon risk-based tool and study-
supply logistical parameters
– Models provide initial basis for discussion w/ Supply Chain Matrix teams
– Need buy-in on risk and overage levels; apply adjustments for operational or
logistics factors
Phasing of overages:
– Previously, a single fixed overage level was applied for life of study.
– Now, recruitment overages are applied either at:
• Enrollment
• First Subject, First Visit (FSFV)
– Maintenance overages are applied once enrollment is complete.
Regional allocation:
• US studies can typically be supported with lower overages than global studies due to
simpler logistical considerations.
Impact of study conduct and supply logistics assumptions:
– Complex distribution channels
– Language pools
14 V. Anisimov, Drug Supply Modelling in Clinical Trials (Statistical Methodology),
Pharmaceutical Outsourcing, Mar-Apr, 2010.

15. Phasing of Overage & Regional Allocation
Initial Forecast:
Statistical Modelling
Overage - Global
Time
Subjects
Previously, overage levels
were not modelled.
Fixed levels were applied
for life of study.
High overage levels applied
for complex global studies
due to high uncertainty
(i.e., One Size Fits All).
Now, risk-based models
used to estimate overage
levels.
2nd Overage Reduction:
Regional Allocation
Overage levels adjusted due to
logistical complexity.
Overage - US
Time
Subjects
Savings
Revised
Forecast
Overage - EU / ROW / Latina
Time
Subjects
Savings
Revised
Forecast
Lower uncertainty once
enrollment is completed.
Overage levels reduced for
maintenance phase.
Overage - Global
Time
Subjects
Savings
Revised
Forecast
1st Overage Reduction:
Phased Overage
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16. Demand & Operational Planning
Developed a Demand & Operations Planning (D&OP) process
– Disciplined demand and supply review with all stakeholders
– Approved demand & supply plan with articulated risks
– All key stakeholders ‘at the right table’
Lean Thinking and DMAIC approach:
– Identify root cause of process failures
– Identify opportunities for improving performance within and between
supply chain nodes
• Clinical Plan
• Project plan
• Supply Chain
and constraints
Understand
Program
Requirements
• Develop Base
Case
• Dev Demand
Scenarios
• Agree Risk
Develop Demand
Forecast
• Receive Forecast
• Design Supply
Chain
Architecture
• Supply Plan
Develop Supply
Forecast
• Plan Packaging
• Plan Bulk
• Plan API
Delivery Plan
• Monitor
Demand, Dev
Req’s & Supply
• Re-plan
• Change Control
Supply Chain
Management
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17. “What If” Scenario Planning
What if?
➢ Recruitment rate increases three-fold?
➢ Recruitment is slower than expected?
➢ Patient sample size changes?
➢ Patient withdrawal is higher than expected etc . . .
Continuous challenge of study design assumptions and supply scenarios yields lean supply chain
with minimal waste.
Ongoing lifecycle management of demand fluctuations and supply operations.
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0
250,000
500,000
750,000
1,000,000
1,250,000
0 5 10 15 20 25 30 35 40 45 50
#Containers
Month
Demand Supply
0
250,000
500,000
750,000
1,000,000
1,250,000
0 5 10 15 20 25 30 35 40 45 50
#Containers
Month
Demand Supply
Pre-Study Mid-Study
Pinch Points
Potential Stock-Out

18. No One Size Fits All ?
1,700 K vials
48% Reduction
1,150 K vials
875 K vials
31% Net Overage
Study Design: 360 patients, 100 sites, 8 treatments, 3 depots
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0
50,000
100,000
150,000
200,000
0 5 10 15
#Containers
Month
Demand - No Overage Supply - Pre-Loading (25% Overage) Supply - Risk-Based Modelling

19. Take Away Points
Risk-based predictive modelling is a powerful tool to minimize supply chain
waste.
Cultural shift and change management required to gain acceptance and
implement new ways of working.
Continuous challenge of forward study design assumptions and supply
scenarios yields lean supply chain and optimal study execution.
On-going lifecycle management of demand and supply operations is key to
sustained performance.
Integrated supply chain planning in R&D can deliver significant value to the
business.
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20. References
1. V. Anisimov, Drug Supply Modelling in Clinical Trials (Statistical Methodology), Pharmaceutical Outsourcing,
Mar-Apr, 2010.
2. V. Anisimov, Predictive modelling of recruitment and drug supply in multicenter clinical trials, Proceedings
of the Joint Statistical Meeting, Washington, USA, August, 2009, pp 1248-1259
3. Anisimov V. Modelling recruitment and randomization effects in multicenter clinical trials. Proceedings of
the International Workshop on Applied Probability, Compiegne, France (2008).
4. Anisimov, V. and Fedorov, V. Modelling, prediction and adaptive adjustment of recruitment in multicenter
trials. Statistics in Medicine, 26, 27, 4958 – 4975 (2007).
5. V. Anisimov, Effect of imbalance in using stratified block randomization in clinical trials, Bulletin of the
International Statistical Institute - LXII, Proc. of the 56 Annual Session, pp. 5938-5941. Lisbon, 2007.
6. Anisimov, V., Effects of unstratified and center-stratified randomization in clinical trials, Pharmaceutical
Statistics, 2010 (published in early view)
7. V. Anisimov, V. Fedorov, Design of multicenter clinical trials with random enrollment, in book ”Advances in
Statistical Methods for the Health Sciences”, Birkhauser, 2006, Ch. 25, 387–400.
8. V. Anisimov, D. Downing, V. Fedorov, Recruitment in multicenter trials: prediction and adjustment, In:
mODa 8 – Advances in Model-Oriented Design and Analysis, Physica-Verlag, 2007, 1–8.
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