The document provides an overview of pharmaceutical portfolio management. It discusses the importance of portfolio management, including maximizing value, maintaining the right number and balance of projects, and aligning with strategic direction. It outlines techniques for evaluating and optimizing portfolios, such as calculating net present value, expected commercial value, and using matrices like BCG and risk-reward to balance risk and reward across projects. Monte Carlo simulation and strategic "buckets" are also summarized as tools to incorporate risk and align projects with strategic goals respectively.

1. PHARMACEUTICAL
PORTFOLIO MANAGEMENT
INNOVATION AND MARKETING
BISHWJIT GHOSHAL
09 MBA (2ND SEM)
PRESENTED TO:
DR. ANAND SHARMA

2. FLOW OF PRESENTATION
• Definition
• Importance of portfolio management
• Goals of portfolio management
 Value Maximization
 Balance
 Strategic Direction
 Right Number of Projects
• Product portfolio value gap
• Putting the portfolio tools to work
• Portfolio execution issues
• Portfolio optimization
• Conclusion

3. DEFINITION
• Product portfolio is a dynamic decision making process,
whereby, business’s list of active new products and
(development projects) is constantly updated and
revised.
• In this process,
 New projects are evaluated, selected and prioritized
 Existing projects may be accelerated, killed or de-
prioritized
 Resources are allocated and re allocated to active
projects

4. IMPORTANCE OF
PORTFOLIO MANAGEMENT
To maximize return; to maximize R & D productivity; to achieve financial goals
To maintain the competitive position of the business- to increase sales and market share
To properly and efficiently allocate scarce resources
To forge the link between project selection and business strategy
To achieve focus- not doing too many projects for the limited resources available; and to
resource the great projects
To achieve balance between the long term and the short term projects and high risk & low risk
ones
To better communicate priorities within the organizations, both vertically and horizontally

5. GOALS OF
PORTFOLIO MANAGEMENT
PORTFOLIO
MANAGEMENT
VALUE
MAXIMIZATION
RIGHT NUMBER
OF PROJECTS
STRATEGIC
DIRECTION
BALANCE

6. GOAL 1
Maximizing the value of portfolio
NET PRESENT VALUE
METHOD
EXPECTED
COMMERCIAL VALUE
METHOD
PRODUCTIVITY INDEX SCORING MODELS
VALUE
MAXIMIZATION
TECHNIQUES OF VALUE MAXIMIZATION

7. NET PRESENT VALUE METHOD
• In this, we calculate the NPV of each project on a spreadsheet
and then arrange them based upon these values.
• GO projects are at the top of the list.
• In this method, we keep on adding projects until we run out of
resources.
 ADVANTAGES:
 It is the simplest method.
 DISADVANTAGES:
 Ignores probabilities and risks.
 Assumes only financial goals as important and no consideration
given to strategies
 Fails to deal with constrained resources
 Assumes an all or none investment decision, whereas in new
projects, the process of investment is an incremental one.

8. NET PRESENT VALUE METHOD
• Let’s explain this by way of a simple example. A company must decide
whether to approve a recently requested project. The project has the
following cash flow profile.
• Cash outflow of £100,000 which is an up-front investment in the project.
• Years 1 – 6: Cash outflow of £5,000 per year
• Years 1 – 6: Cash inflow of £30,000 per year due to new revenue streams
• No further inflows or outflows after year 6. (Discount Rate=10%)

9. EXPECTED COMMERCIAL VALUE
• Seeks to maximize the value or commercial worth of
portfolio subject to budget constraints
• Introduces the notion of risks and probabilities.
• Calculation of ECV based on decision tree analysis.
• Considerations:
o Future stream of earning from the project
o Probabilities of both commercial success and technical success
o Commercialization costs and development costs

10. EXPECTED COMMERCIAL VALUE
ECV=[(PV* Pcs- C) * Pts] – D

11. EXPECTED COMMERCIAL VALUE
Project
Name
PV Probability
of Technical
Success
Probability
of
Commercial
Success
Development
Cost
Commercial
Cost
ECV
Alpha 30 0.80 0.50 3 5 5.0
Beta 63.75 0.50 0.80 5 2 19.5
Gamma 8.62 0.75 0.75 2 1 2.1
Delta 3 1.00 1.00 1 0.5 1.5
Echo 50 0.60 0.75 5 3 15.7
Foxtrot 66.25 0.50 0.80 10 2 15.5
•PV income stream assumes commercial success which is not 100%. Thus PV needs to be multiplied
by probability of commercial success. (PV*Pcs)
•But, to get to market, firm needs to commercialize the project that would involve commercialization
cost. The firm must spend C dollars on the project. (PV*Pcs)-C
•Before commercialization can occur, project must be a technical success. Thus, the above value must
be multiplied with probability of technical success. [(PV*Pcs)-C]*Pts
•To get to a technical success, the firm must spend first on development of the project. Thus,
development cost must be deducted from the above eqn. {[(PV*Pcs)-C]*Pts}-D

12. EXPECTED COMMERCIAL VALUE
• ADVANTAGES:
 Go/Kill decision is an incremental one (the notion of purchasing
options)
 All monetary amounts are discounted to today (not just to
launch date)
 Deals with the issue of constrained resources
• DISADVANTAGES:
 Dependence on excessive financial and other quantitative data
 Accurate estimates for all the variables must be available which
is not possible in most of the cases
 Does not look at the balance of the portfolio i.e. balance
between high cost and low cost across various markets and
technologies
 Considers a single financial criterion for maximization

13. PRODUCTIVITY INDEX
• Productivity Index can be mathematically expressed as
the following ratio:
PI= ECV*Pts-R&D / R&D
• In productivity index, ECV is a probability adjusted NPV.
It is the probability weighted stream of cash flows from
the project, discounted to the present, and assuming
technical success, less remaining R & D costs.
• Projects are ranked according to this productivity index
in order to arrive at the preferred portfolio with
projects at the bottom of the list on hold.

14. SCORING MODELS
• Projects scored on number of criteria by the
management. Typical main criteria include:
 Strategic alignment
 Product advantage
 Market attractiveness
 Ability to leverage core competencies
 Technical feasibility
 Reward vs risk
• The weighted addition of item ratings becomes the
basis for developing a rank ordered list of projects.

15. SCORING MODELS
Project Leader Strat.
Fit
Prod.
Advrtsng
Market
Attract
Core
Comp
Tech
Feasib
Reward Project
Attract
Score
Status
Epsilon Peters 9 9 10 10 9 9 93.3 Active
Gamma Cooper 10 10 7 7 7 7 80 Active
Alpha Smith 8 7 7 8 8 9 75 Active
Delta Scott 7 7 9 9 8 5 74 Active
Beta Jones 7 7 6 6 8 6 66.7 Hold
Omicron Bailey 8 6 6 8 7 5 66.7 Hold
A RANK ORDERED LIST

16. GOAL 2
A balanced portfolio
• To achieve balance of projects in terms of many
parameters; for example:
 Long term projects vs short term projects
 High risk vs low risk projects
 Balance across various markets, technologies, product
categories and product types

17. Techniques of
balancing portfolios
VISUAL
CHARTS
MONTE
CARLO
SIMULATION
BCG MATRIX
RISK REWARD
BUBBLE
DIAGRAM

18. RISK-REWARD
BUBBLE DIAGRAM

19. RISK-REWARD
BUBBLE DIAGRAM
• There are 4 quadrants in risk reward bubble diagram.
Pearls (Upper left quadrant)
These are potential star products, projects with a high likelihood of
success, and expected to yield a high reward. The bigger circle is
provided more resources.
Oysters (Lower left)
These are the long shot projects, projects with high expected payoffs,
but low likelihoods of technical success.
Bread and Butter (Upper right)
These are small, simple projects, projects with high likelihood of
success, but low reward.
White Elephants (Lower right)
These are the low probability and low reward projects.

20. BCG MATRIX

21. BCG MATRIX
• There are 4 quadrants in the BCG matrix.
 Stars (Upper left quadrant)
These are units with a high market share in a fast growing industry.
 Cash cows (Lower left)
These units have a high market share in slow growing industry. These
generate cash in excess to the amount needed to run the business.
 Question mark (Upper right)
These are the products running in a high growth market but have a low share
in that market. They have potential to gain market share and become STARS
and when the market growth slows, can turn to CASH COWS.
 Dogs (Lower right)
These products have a low market share in a mature, slow growing market.
These products achieve only the break even point and do not generate any
profit for the company.

22. BCG MATRIX
QUADRANT STRATEGY
STAR HOLD / INVEST
CASH COW HOLD
QUESTION MARK HOLD / DISINVEST
DOGS HOLD / QUIT

23. MONTE CARLO SIMULATION
• Monte Carlo simulation is a computerized mathematical technique
that allows people to account for risk in quantitative analysis and
decision making.
• Monte Carlo simulation furnishes the decision-maker with a range of
possible outcomes and the probabilities that will occur for any choice
of action.
• It shows the extreme possibilities—the outcomes of going for broke
and for the most conservative decision—along with all possible
consequences for middle-of-the-road decisions.
 HOW MONTE CARLO SIMULATION WORKS?
• Monte Carlo simulation performs risk analysis by building models of
possible results by substituting a range of values—a probability
distribution—for any factor that has inherent uncertainty.
• It then calculates results over and over, each time using a different set
of random values from the probability functions.

24. MONTE CARLO SIMULATION
• TYPES OF CURVES:
 NORMAL DISTRIBUTION CURVE
Defines the mean or the expected
value and a standard deviation to describe the variation about
the mean.
 LOG-NORMAL DISTRIBUTION CURVE
Positively skewed, not symmetric.
Used to represent values that don’t go
below zero but have unlimited positive potential.

25. MONTE CARLO SIMULATION
 TRIANGULAR DISTRIBUTION CURVE
In this, the user defines the minimum, most
likely and maximum values.
 PERT CURVE
The user defines the maximum, most likely
and minimum value similar to triangular but
values between most likely and extremes
are more likely to occur than triangular.

26. GOAL 3
Strategic Direction
• The mission, vision and strategy of the business is
made operational through the decisions it makes about
where to spend the money.
STRATEGIC
DIRECTION
BOTTOM UP
TOP
DOWN

27. STRATEGIC DIRECTION
1. BOTTOM UP APPROACH
In this approach, strategic fit is attained simply by including
numerous strategic criteria into the GO / kill strategy or
prioritization. The scoring model is the most commonly used
technique in this approach.
2. TOP DOWN APPROACH
In this approach, strategic buckets method is mainly used for
allocation of resources. This begins with the business’s strategy
and then moves to setting aside funds- envelopes or buckets
of money- destined for different types of projects.

28. GOAL 4
Right number of projects
• Superimposed across all three goals, is resource
constraints. The management must try to complete
these goals but always wary of the fact that if too many
projects are approved for the limited resources, the
pipeline gridlock is the result.
• Two questions must be kept in mind when setting the
number of projects.
 Do you have enough of the right resources to handle projects
currently in your pipeline?
 Do you have enough resources to achieve your new product
goals?

29. PRODUCT PORTFOLIO
VALUE GAP
 Defining Potential value and executing the right product portfolio
 Many companies fail to realize the full potential available from
their portfolios
 Too frequently , Inadequately Defined portfolios and poor project
execution drain the value from products
 Can’t take much advantage for newer products because they have
difficulty in meeting their product development target.

30. PUTTING THE
PORTFOLIO MODEL INTO WORK
PORTFOLIO
MANAGEMENT
INTEGRATING
APPROACHES
GATES
APPROACH
PORTFOLIO
REVIEW
APPROACH

31. GATES APPROACH
•Best for larger firms in mature businesses.
•Resource allocation methods are integrated into the gates
•Focus is more on the in-depth reviews of individual projects
•In the process, the gates are modified by displaying portfolio lists and charts at gates
•Go/kill decisions are made on each gate.

32. PORTFOLIO
REVIEW APPROACH
•Best suited to fast
paced companies with
dynamic portfolios.
•In this method, all
projects are up for 4
times a year
•In this, all projects and
all resources are on the
table and then suitable
decision is taken.

33. PORTFOLIO
EXECUTION ISSUES
PORTFOLIO
EXECUTION
ISSUES
Organization
Design
Acquisition
and
Licensing
Incentive
Design
Frequency of
change in
organization

34. ORGANIZATION DESIGN
• In organization design the key question is to have either
a “Centralized” or “Decentralized” design.
CENTRALIZED DECENTRALIZED
Distant Proximal
Capabilities-broadening search Capabilities-deepening search
Better for radical innovations Better for incremental
innovations
Advantage: More synergy
across programs
Advantage: Reduced levels of
management hierarchy

35. FREQUENCY OF CHANGE
• The change includes personnel reshuffling from top to
middle management leading to frequent modifications to
projects within a portfolio and organization design.
• Firms should carefully consider the history of changes made
in the R & D organization and in the portfolio to assess if
the change will help or hinder overall performance.
• A balance is required between infrequent portfolio
rebalancing and overly frequent rebalancing.
• Changes that are too frequent can drain organizational
resources in simply managing the modifications as opposed
to accelerating progress to deliver on objectives.

36. ACQUISITION AND LICENCING
• Two lines of thoughts:
 Acquisitions tend to hurt innovations because they may:
o Distract managers from innovations
o Compete for funds with existing innovation projects
o Trigger the exodus of key employees
 Acquisitions could be a tonic for innovations because:
o Firms with better internal knowledge have higher ability to utilize
external knowledge from acquisitions
o Firms experiencing the greatest deterioration in R&D productivity are
most likely to undertake the acquisition of a research-intensive firm to
replenish their portfolio.

37. INCENTIVE DESIGN
• Incentives affect how organizational strategies are carried
out by the people tasked with execution: managers and
scientists.
• Substantial tolerance (or even reward) for early failure and
reward for long-term success is needed for agents (such as
managers or scientists) to explore riskier options.
• If short-term success is rewarded, then agents are more
inclined to choose safer options (i.e., those which can lead
to incremental innovations).
• Incentives should depend on interaction of project
complexity and desired type of innovation.
• An organization focused on incremental innovation should set higher
incentives for more complex projects.
• An organization focused on radical innovation should set lower
incentives for more complex projects.

38. INCENTIVE DESIGN
• There are two different school of thoughts regarding
incentives:
A. Complex problems are difficult to solve and incentives should be
provided to enable managers to invest adequate effort.
B. Incentives result in lower performance for complex tasks.
• Another aspect involves motivating managers to kill
right projects at the right time.
 Rewarding success may mean that an agent persists with a
project even if its prospects have dimmed since its inception.
 Rewarding failure, on the other hand, undermines motivation for
persisting to find solutions to challenging projects, as it could be
“argued” that the project should be discontinued.

39. PORTFOLIO OPTIMIZATION
• Portfolio optimization is the process of choosing the proportions of
various assets to be held in a portfolio, in such a way as to make the
portfolio better than any other according to some criterion.
Overall Level of
Investment
• Overall R & D
investment
Type of project
• Balance between
radical and
incremental
innovation
• Right mix of short,
medium and long
term
developments
Strategy for optimal
project selection
• Prioritization
using optimization
methods
• Prioritization
using decision
trees
• Interaction among
projects

40. CONCLUSION
• Establishing an effective portfolio management processes
and infrastructure is a critical success factor in meeting
following objectives:
Increasing Product Profitability
Reducing Product Failures
Getting The Most Out Of Limited Resources
Achieving High Revenues And Margins