Risk-based Portfolio Management
A pharmaceutical application
Palisade 2013 Risk Conference
London, June 11th
EpiX Analytics
www.epixanalytics.com
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
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Model structure and MC
simulation modeling
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Model structure
 First step: identification of key risks/uncertainties and
their drivers along the development and
manufacturing processes:
Type of
production
process
Observed
delays?
Type of
molecule
Development
phase
Expected
timing?
Chance of
approval?
Development & Launch
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Market
demand
Production
site
capacity
…
Production
costs?
Production
volumes?
Manufacturing
…
Product characteristics
(risk drivers)
Portfolio risks (uncertain
variables)
Overall
Portfolio value
Risk driver 1
Risk A
Risk driver 2
Risk driver 3
Risk driver 4
Risk B
Risk driver 5
Risk driver 6
Risk C
Risk driver 7
Risk driver 8
Risk D
Revenue and
Margin by
Quarter
Risk driver 9
Risk driver 10
Risk E
Risk driver 11
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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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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:
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%
© EpiX Analytics LLC
Development phase
Development phase
Development
phase
Molecule
Risksize
driver 1
Molecule size
Molecule
Initial
timing size
Risk
driver 2
Initial
timing
Delays
observed?
Initial
timing
Delays
observed?
Risk
driver 3
Delays
observed?
Production forecast
Risk forecast
driver 4
Production
Production
forecast
Company
capacity
Risk
driver 5
Company capacity
Company
capacity
Contract
expiry
Risk
driver 6
Contract expiry
Contract expiry
driver 7
InitialRisk
price
Initial price
Initial price
Annual
RiskPPI
driver 8
Annual PPI
Annual PPI
Initial
cost
Risk
driver 9
Initial cost
Initial cost
Cost uncertainty
Risk driver 10
Cost uncertainty
Cost uncertainty
Risk driver 11
Product A
Product B
Product C
Product …
Product approval
Product approval
Product approval
Risk A
Product launch date
Product launch date
Product launch date
Risk B
Table. 1. Effect of risk driver 1 and 2 on:
Risk A
hfils
ffdfff
cxv
V gr
iku
0
12%
35%
67%
80%
90%
1
34%
56%
61%
96%
100%
2
44%
55%
66%
77%
88%
3
52%
61%
69%
89%
100%
Table 2. Effect of risk driver 3:
Risk 2
1
a
b
2
3
Volume by quarter
Volume by quarter
Volume by quarter
Risk C
0.5
1.0
1.5
0.7
1.0
1.3
Table 2. Effect of risk driver 3:
Risk 2
1
2
3
a
0.5
1.0
1.5
b
0.7
1.0
1.3
Unit Price
Unit Price
Unit Price
Risk D
Overall
Portfolio
Value
Table. 1. Effect of risk driver 1 and 2 on:
Risk A
hfils
ffdfff
Unit cost
Unit cost
Unit cost
Risk E
0
12%
35%
cxv
V gr
67%
iku
80%
90%
1
34%
56%
61%
96%
100%
2
44%
55%
66%
77%
88%
3
52%
61%
69%
89%
100%
Table 3. Effect of risk driver 10 on:
Risk E
hg
ex
loi
1
0.8
0.5
0.3
2
0.7
0.6
0.5
3
1.2
1.0
0.5
© 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
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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
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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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Key outputs
Revenue & Margin forecast by portfolio (/quarter, calendar year, 12mth)
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Key outputs
Revenue forecast by project
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Key outputs
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Key outputs
Scatter plot of projects revenue/margin against risks
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Key outputs
Sensitivity charts
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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
Thank you
Dr. Solenne Costard
Senior Consultant
EpiX Analytics LLC
scostard@EpiXAnalytics.com
Ph: +1 970 372 1212