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www.epixanalytics.com
Risk-based Portfolio Management A pharmaceutical application
Palisade 2013 Risk Conference London, June 11th
EpiX Analytics
Case study
Objective:
Illustrate the usefulness of MC simulation modeling to forecast a complex pharmaceutical portfolio
Based on real consulting project - portfolio management
© EpiX Analytics LLC
Contents
Background
Model structure
Identification of uncertainties
Definition of evaluation rules
Simulation modeling
Automation & data checks
User- defined evaluation using unified framework
Key outputs
Conclusions
© EpiX Analytics LLC
Background
Pharmaceutical development and manufacturing is “risky”:
Many uncertainties in processes involved
Need for decision-support tool: modeling revenues and margins of portfolios with uncertainties, comparisons of portfolios, identification of risks
Large number of products in portfolio:
Need to come up with unified metrics & framework
Need for data quality control and automation of evaluation procedure
© EpiX Analytics LLC
Model structure and MC simulation modeling
© EpiX Analytics LLC
Model structure
First step: identification of key risks/uncertainties and their drivers along the development and manufacturing processes:
Development & Launch Manufacturing
Expected timing?
Production volumes?
Production costs?
Chance of approval?
…
Type of molecule
Development phase
Observed delays?
Market demand
Production site
capacity
Type of production
process …
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Revenue and Margin by
Quarter
Product characteristics
(risk drivers) Portfolio risks (uncertain
variables)
Overall
Portfolio value
Risk driver 11
Risk D
Risk E
Risk driver 1
Risk driver 2
Risk driver 3
Risk driver 5
Risk driver 7
Risk driver 8
Risk driver 9
Risk driver 10
Risk A
Risk C
Risk B
Risk driver 6
Risk driver 4
© EpiX Analytics LLC
Model structure
Second step: characterization of uncertainties
Use of data / expert opinion to quantify impact of uncertainties
Establishment of systematic evaluation rules that consider product characteristics
Definition of probability distributions to represent uncertainties
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Pre-clinical
testing Phase I Phase II Phase III On Market
Large 0.5% 5% 45% 90% 100%
Medium 2% 15% 66% 70% 100%
Small 5% 25% 70% 85% 100%
Example: Product approval
Based on development phase and type of molecule under development
Use of historical data on approval
Product approval represented by a series of Binomial distributions with probabilities of approval defined by matrix:
© EpiX Analytics LLC
Unit Price
Unit cost
Development phase
Molecule size
Initial timing
Production forecast
Contract expiry
Initial price
Annual PPI
Initial cost
Product approval
Volume by quarter
Product launch date
Company capacity
Delays observed?
Cost uncertainty
Product A
Unit Price
Unit cost
Development phase
Molecule size
Initial timing
Production forecast
Contract expiry
Initial price
Annual PPI
Initial cost
Product approval
Volume by quarter
Product launch date
Company capacity
Delays observed?
Cost uncertainty
Product B
Unit Price
Unit cost
Development phase
Molecule size
Initial timing
Production forecast
Contract expiry
Initial price
Annual PPI
Initial cost
Product approval
Volume by quarter
Product launch date
Company capacity
Delays observed?
Cost uncertainty
Product C
Risk D
Risk E
Risk driver 1
Risk driver 2
Risk driver 3
Risk driver 5
Risk driver 7
Risk driver 8
Risk driver 9
Risk driver 10
Risk A
Risk C
Risk B
Risk driver 6
Risk driver 4
Risk driver 11
Product …
Overall
Portfolio
Value
© EpiX Analytics LLC
Simulation modeling
Model in @RISK 6:
MC simulation for portfolio evaluation under uncertainty
Excel interface for users - easy to navigate and understand
Due to large number of projects in portfolio, need for quality control and automation
- use of VBA to minimize user errors
© EpiX Analytics LLC
Simulation modeling
Portfolio evaluation
Unified framework for evaluation Pre-defined evaluation framework applied to all products
included in simulation
Automatic running and production of outputs (more to come…)
User-defined Simulation period: up to 10yrs, start date ≥ current date
Projects in portfolio: manual and/or pre-defined group selection (type of product, production site, etc.)
Demo
© EpiX Analytics LLC
Simulation modeling
Automation and Data checks
Importation of data directly from client database Limits errors of data entry
Automatic data check Highlights errors that would affect model results
Products with data errors “locked out”
Security Sheets of cells locked to prevent erroneous changes by users
Instructions guide users to a limited number of sheets where they can 1) configure the simulation 2) view results
Demo
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Key outputs
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Revenue & Margin forecast by portfolio (/quarter, calendar year, 12mth)
Key outputs
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Revenue forecast by project
Key outputs
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Key outputs
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Scatter plot of projects revenue/margin against risks
Key outputs
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Sensitivity charts
Key outputs
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Portfolio management
Model successful, used for short and medium to long term planning and budgeting
Four years of use
Model improved over time (criteria, data, automation, outputs)
Adopted by decision makers – required for planning
© EpiX Analytics LLC
Dr. Solenne Costard Senior Consultant EpiX Analytics LLC
[email protected] Ph: +1 970 372 1212
Thank you
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