1. Group 8 Zaid Azmi01 Pratik Malde20 Nisha Pancholi31 Sneha Sahani39 Dhaval Shah41

2. Why product portfolio management? Allocate Resources amongst various businesses/products Maximizing product portfolio value Project Prioritization Aligning product portfolio to overall business strategy

3. Classification of Product Portfolio Models Product Portfolio Models Product based Models Standardized Models Univariate Dimensions Composite Dimensions Customized Models Product Performance Matrix Conjoint Analysis Analytic Hierarchy Process Financial Oriented Models Risk Return Model Stochastic Dominance

4. Standardized Models Standardized Models assume that the value of market position or market share depends on: Structure of Competition Stage in PLC

5. Arthur D Little's method is based on PLC Uses dimensions of Environmental Assessment and Business Strength Assessment Environmental measure is the Industry’s life cycle. A.D.Littles Business Profile Matrix

6. ADL Matrix

7. Calculating Competitive Position Illustration

8. Competitive position: Dominant: Rare. Results from near monopoly, protected leadership. Strong: Not too many rivals Favorable: Fragmented, No clear leader. Tenable: Business has a niche Weak: Business too small to be profitable or survive over long term. Limitations: Difficult to identify the current phase of industry life cycle. There is no standard life cycle ADL Matrix

9. Similar to GE Matrix X- Axis is Sector Prospects Y-Axis is Company’s Competitive Capability Shell’s Directional Policy Matrix

10. Sample Calculation Competitive Advantage

11. Sample Calculation Sector Prospects

12. Placing the SBU on the matrix

13. Leader - major resources to be focused upon the SBU. Try harder - could be vulnerable over a longer period of time, but fine for now. Double or quit - gamble on potential major SBU's for the future. Growth - grow the market by focusing just enough resources here. Custodial – Maximize Cash Flow, do not commit any more resources. Almost like Cash Cow Cash Generator – Exactly like a cash cow, milk here for expansion elsewhere. Phased withdrawal - move cash to SBU's with greater potential. Divest - liquidate or move these assets on as fast as you can. The 9 Cells Explained

14. Limitations No fixed factors Subjective

15. Customized Models Product – Performance Matrix Conjoint Analysis Analytic Hierarchy Process

16. Product – Performance Matrix Allows management flexibility to choose customized dimensions E.g. in the below matrix, 4 dimensions – Industry Sales, Product Sales, Market Share & Profitability are chosen Company Sales->DeclineStableGrowth Profitability -> Below Target On Target Above Target Below Target On Target Above Target Below Target On Target Above Target Industry SalesMarket Share Growth Leading Average Marginal Stable Leading Average Marginal Decline Leading Average Marginal

17. Conjoint Analysis  Overall utility for a product can be decomposed into the utilities of the individual attributes of the product.  Rankings or ratings of the product profiles in terms of preference, purchase probability, etc.  Pairwise comparisons of product profiles in terms of preference, purchase probability, etc.  Choice of a product from a set of product profiles

18. Example: Laptop Profiles Brand Hard Drive RAMScreenPriceAB Dell320 GB2 GB15.4 in$1,20096 Apple320 GB4 GB15.4 in$1,200612 Dell160 GB4 GB15.4 in$900125 Apple320 GB2 GB15.4 in$90011 Dell320 GB4 GB12.1 in$1,50043 Apple320 GB2 GB12.1 in$1,50019 Apple160 GB4 GB15.4 in$1,500310 Apple160 GB2 GB12.1 in$90087 Apple160 GB4 GB12.1 in$1,20058 Dell160 GB2 GB12.1 in$1,20071 Dell320 GB4 GB12.1 in$900104 Dell160 GB2 GB15.4 in$1,50022

19. Consumer AConsumer B Attribute level Mean for level across all profiles Mean as deviation from zero Range on attribute Percentage importance Mean for level across all profiles Mean as deviation from zero Range on attribute Percentage importance Apple5.67-.831.6614%9.50+3.006.0050% Dell7.33+.833.50-3.00 160HD6.17-.33.676%5.502.0017% 320H D 6.83+.337.50+1.00 2RAM6.33-.17.333%6.00-.501.008% 4RAM6.67+.177.00+.50 12.1in5.83-.671.3311%5.33-1.172.3319% 15.4in7.17+.677.67+1.17 $90010.25+3.757.7566%6.75+.25.756% $12006.75+.256.75+.25 $15002.5-4.006.00-.50

20. Uses of conjoint analysis Market segmentation New product design Trade-off analysis (esp. in pricing decisions)

21. Financial Models Risk – Return Model

22. Expected Return The expected rate of return on a SBU represents the mean of a probability distribution of possible future returns on the SBU. Given a probability distribution of returns, the expected return can be calculated using the following equation: N E[R] =  (p i R i ) i=1 Where: E[R] = the expected return on the stock N = the number of states p i = the probability of state i R i = the return on the SBU in state i. 22

23. Expected Return The table below provides a probability distribution for the returns on SBU A and SBU B Scenario Probability Return On Return On SBU A SBU B 1 20% 5% 50% 2 30% 10% 30% 3 30% 15% 10% 4 20% 20% -10% The probability reflects how likely it is that the state will occur. This is management assumption. The last two columns present the returns or outcomes for SBU A and SBU B that will occur in each of the four states. Again this is management assumption. 23

24. In this example, the expected return for SBU A & B would be calculated as follows: E[R] A =.2(5%) +.3(10%) +.3(15%) +.2(20%) = 12.5% E[R] B =.2(50%) +.3(30%) +.3(10%) +.2(-10%) = 20% SBU B offers a higher expected return than SBU A. However, we haven't considered risk. 24 Expected Return

25. Measures of Risk Risk reflects the chance that the actual return on an investment may be different than the expected return. Way to measure risk is to calculate the variance and standard deviation of the distribution of returns. Variance is calculated as N Var(R) =  2 =  p i (R i – E[R]) 2 i=1 Where: N = the number of states p i = the probability of state i R i = the return on the stock in state i E[R] = the expected return on the stock SD is root of Variance 25

26. The variance and standard deviation for SBU A is  2 A =.2(.05 -.125) 2 +.3(.1 -.125) 2 +.3(.15 -.125) 2 +.2(.2 -.125) 2 =.002625      Similarly for SBU B  2 B = 0.042 and   = 20.49% Although SBU B offers a higher expected return than SBU A, it also is riskier since its variance and standard deviation are greater than SBU A's. 26 Measures of Risk

27. Portfolio Risk and Return Most companies do not hold SBUs in isolation. Instead, they choose to hold a portfolio of several SBUs. A portion of an individual SBU’s risk can be eliminated, i.e., diversified away. From our previous calculations: the expected return on SBU A is 12.5% the expected return on SBU B is 20% the variance on SBU A is.00263 the variance on SBU B is.04200 the standard deviation on SBU A is 5.12% the standard deviation on SBU B is 20.49% 27

28. The Expected Return on a Portfolio is computed as the weighted average of the expected returns on the SBUs which comprise the portfolio. The weights reflect the proportion of the portfolio invested in the SBU. This can be expressed as follows: N E[R p ] =  w i E[R i ] i=1 Where: E[R p ] = the expected return on the portfolio N = the number of SBUs in the portfolio w i = the proportion of the portfolio invested in SBU i E[R i ] = the expected return on SBU i 28 Portfolio Risk and Return

29. If we have an equally weighted portfolio of SBUA and SBU B then the expected return of the portfolio is: E[R p ] =.50(.125) +.50(.20) = 16.25% The risk on the entire portfolio can also be calculated using Variance and Standard Deviation for the entire portfolio The purpose of diversification is that by forming portfolios, some of the risk inherent in the individual SBU’s can be minimized. 29 Portfolio Risk and Return

30. THANK YOU