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PORTFOLIO MANAGEMENT

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PORTFOLIO MANAGEMENT

  1. 1. PHARMACEUTICAL PORTFOLIO MANAGEMENT INNOVATION AND MARKETING BISHWJIT GHOSHAL 09 MBA (2ND SEM) PRESENTED TO: DR. ANAND SHARMA
  2. 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. 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. 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. 5. GOALS OF PORTFOLIO MANAGEMENT PORTFOLIO MANAGEMENT VALUE MAXIMIZATION RIGHT NUMBER OF PROJECTS STRATEGIC DIRECTION BALANCE
  6. 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. 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. 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. 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. 10. EXPECTED COMMERCIAL VALUE ECV=[(PV* Pcs- C) * Pts] – D
  11. 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. 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. 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. 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. 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. 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. 17. Techniques of balancing portfolios VISUAL CHARTS MONTE CARLO SIMULATION BCG MATRIX RISK REWARD BUBBLE DIAGRAM
  18. 18. RISK-REWARD BUBBLE DIAGRAM
  19. 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. 20. BCG MATRIX
  21. 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. 22. BCG MATRIX QUADRANT STRATEGY STAR HOLD / INVEST CASH COW HOLD QUESTION MARK HOLD / DISINVEST DOGS HOLD / QUIT
  23. 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. 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. 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. 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. 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. 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. 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. 30. PUTTING THE PORTFOLIO MODEL INTO WORK PORTFOLIO MANAGEMENT INTEGRATING APPROACHES GATES APPROACH PORTFOLIO REVIEW APPROACH
  31. 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. 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. 33. PORTFOLIO EXECUTION ISSUES PORTFOLIO EXECUTION ISSUES Organization Design Acquisition and Licensing Incentive Design Frequency of change in organization
  34. 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. 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. 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. 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. 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. 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. 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

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