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OPSM 301 Operations Management Class 11: New Product Development Decision Analysis Koç University Zeynep Aksin

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Presentation on theme: "OPSM 301 Operations Management Class 11: New Product Development Decision Analysis Koç University Zeynep Aksin"— Presentation transcript:

1 OPSM 301 Operations Management Class 11: New Product Development Decision Analysis Koç University Zeynep Aksin zaksin@ku.edu.tr

2 Announcements  Change in syllabus plan as follows: –Today: NPD & DA Chapter 5 (156-165; 181-184) Quant. Module A (entire module) Study questions: A1,A3,A4,A9,A18,A19,A20 –Last session of project management will be after the bayram on 8/11 Class will be held in the lab (SOS Z14) Campus Wedding assignment due in class We will have quiz 2 on Project Management –Decision Trees Quiz 3 on 10/11 Thursday

3 Product Life Cycle  Introduction  Growth  Maturity  Decline

4 Product Life Cycle Introduction  Fine tuning –research –product development –process modification and enhancement –supplier development

5 Product Life Cycle Growth  Product design begins to stabilize  Effective forecasting of capacity becomes necessary  Adding or enhancing capacity may be necessary

6 Product Life Cycle Maturity  Competitors now established  High volume, innovative production may be needed  Improved cost control, reduction in options, paring down of product line

7 Product Life Cycle Decline  Unless product makes a special contribution, must plan to terminate offering

8 Product Life Cycle, Sales, Cost, and Profit Sales, Cost & Profit. IntroductionMaturityDeclineGrowth Cost of Development & Manufacture Sales Revenue Time Cash flow Loss Profit

9 Process Life Cycle Start-Up Rapid Growth Rapid Growth Maturity Stability Job Shop Low Low Low BatchProduction Increasing Medium Medium MassProduction High High High MassProduction High Medium HighAutomation ProcessInnovation ThroughputVolume ManufacturingSystem

10 Quality Function Deployment  Identify customer wants  Identify how the good/service will satisfy customer wants  Relate customer wants to product hows  Identify relationships between the firm’s hows  Develop importance ratings  Evaluate competing products

11 QFD House of Quality

12 Percent of Sales From New Product

13 Few Successes 0 500 1000 1500 2000 Development Stage Number 1000 Market requirement Design review, Testing, Introduction 25 Ideas 1750 Product specification 100 Functional specifications One success! 500

14 Pharmaceutical Industry – Macro Trends  Axiom: the more drugs from NPD the better  Periods of therapeutic exclusivity are decreasing –Fast followers are the norm; markets get crowded quickly.  Social Pressures, Price Pressures increasing globally  Development becoming more complex  Technological discontinuities are certain, timing is not  Research and Development is the main source of competitive advantage (extremely high spending on R&D relative to sales)  Demand is growing –Unmet medical needs abound –Population is aging

15 Pharmaceutical Development Process Discovery 5,000 – 10,000 Compounds Evaluated 6.5 yrs. Target Focus followed by Lead Focus. 5 – 10 compounds Throughput 5 - 10 Compounds Evaluated 2.5 – 3.5 yrs. Compound Focus followed by indication Focus 1 – 3 compounds Negation 1 – 3 Compounds Evaluated 2.5 - 3.5 yrs. Indication Focus followed by Extension Focus. 0 – 2 compounds Run Fast Size of Opportunity Funnel Cycle Time Project Definition Output ~$1 Billion to Develop and Commercialize Important new compounds Dominant Theme Target ID & Validation Screening & Optimization Pre-Clinical Testing Phase I Clinical Phase II Clinical Phase III Clinical WMA & Post Filing Proof Of Concept Product Development

16 Decision Environments  Certainty - environment in which relevant parameters have known values  Risk - environment in which certain future events have probable outcomes  Uncertainty - environment in which it is impossible to assess the likelihood of various future events

17 Examples  Profit is $ 5 per unit. We have an order for 200 units. How much profit will we make?  Profit is $ 5 per unit. Based on previous experience there is a 50 percent chance for an order for 100 units and a 50 percent chance for an order for 200 units. What is the expected profit?  Profit is $ 5 per unit. The probability distribution of potential demand is unknown

18 Payoff Tables  A method of organizing and illustrating the payoffs from different decisions given various states of nature  A payoff is the outcome of the decision: States of Nature Decisiona b 1payoff 1apayoff 1b 2payoff 2apayoff 2b

19 Decision Making Under Uncertainty  Maximax - Choose the alternative that maximizes the maximum outcome for every alternative (Optimistic criterion)  Maximin - Choose the alternative that maximizes the minimum outcome for every alternative (Pessimistic criterion)  Equally likely - chose the alternative with the highest average outcome.

20 Example - Decision Making Under Uncertainty States of Nature Alternatives Favorable Market Unfavorable Market Maximum in Row Minimum in Row Row Average Construct large plant $200,000 -$180,000 $200,000 -$180,000 $10,000 Construct small plant $100,000 -$20,000 $100,000 -$20,000 $40,000 $0 $0 MaximaxMaximin Equally likely Do nothing

21  Probabilistic decision situation  States of nature have probabilities of occurrence  Select alternative with largest expected monetary value (EMV) –EMV = Average return for alternative if decision were repeated many times Decision Making Under Risk

22 Example - Decision Making Under Risk

23 Expected Value of Perfect Information (EVPI)  EVPI places an upper bound on what one would pay for additional information  EVPI is the expected value with certainty minus the maximum EMV

24 Expected Value of Perfect Information Construct a large plant Construct a small plant Do nothing 200,000 -$180,000 $0 Favorable Market ($) Unfavorable Market ($) 0.50 EMV $40,000 $100,000-$20,000 $0 $20,000

25 Expected Value of Perfect Information EVPI EVPI = expected value with perfect information - max(EMV) = $200,000*0.50 + 0*0.50 - $40,000 = $60,000

26  Graphical display of decision process  Used for solving problems –With one set of alternatives and states of nature, decision tables can be used also –With several sets of alternatives and states of nature (sequential decisions), decision tables cannot be used  EMV is criterion most often used Decision Trees

27 Format of a Decision Tree Payoff 1 State of nature 1 State of nature 2 Payoff 6 State of nature 2 State of nature 1 Choose A Choose B 1 Decision Point Chance Event, state of nature Payoff 2 Payoff 3 2 Choose A 1 Choose A 2 2 Payoff 4 Payoff 5 Choose B 1 Choose B 2

28 Example of a Decision Tree Problem An electronics company is considering a new product alternative, and the firm's management is considering three courses of action: A) Hire additional engineers B) Invest in CAD. C) Do nothing (do not develop) The correct choice depends largely upon demand which eventually realizes fro the developed product, which may be low, medium, or high. By consensus, management estimates the respective demand probabilities as.10,.50, and.40.

29 Example of a Decision Tree Problem: The Payoff Table 0.10.50.4 LowMedium High A105090 B-12025200 C204060 The management also estimates the profits when choosing from the three alternatives (A, B, and C) under the differing probable levels of demand. These profits, in thousands of dollars are presented in the table below:

30 Example of a Decision Tree Problem: Step 1: We start by drawing the three decisions A B C

31 Example of Decision Tree Problem: Step 2: Add our possible states of nature, probabilities, and payoffs A B C High demand (.4) Medium demand (.5) Low demand (.1) $90k $50k $10k High demand (.4) Medium demand (.5) Low demand (.1) $200k $25k -$120k High demand (.4) Medium demand (.5) Low demand (.1) $60k $40k $20k

32 Example of Decision Tree Problem: Step 3: Determine the expected value of each decision High demand (.4) Medium demand (.5) Low demand (.1) A $90k $50k $10k EV A =.4(90)+.5(50)+.1(10)=$62k $62k

33 Example of Decision Tree Problem: Step 4: Make the decision High demand (.4) Medium demand (.5) Low demand (.1) High demand (.4) Medium demand (.5) Low demand (.1) A B C High demand (.4) Medium demand (.5) Low demand (.1) $90k $50k $10k $200k $25k -$120k $60k $40k $20k $62k $80.5k $46k Alternative B generates the greatest expected profit, so our choice is B or to invest in CAD

34 Thinking of a longer horizon (sequential decisions)  Assume we have a 2 year horizon: If nothing is done now and demand is high, hiring decision could be reconsidered next year. Fixed cost of hiring is $ 10, and CAD is $130. (The cost structure will be the same next year)  Net revenues for one year for each demand case are as follows: 0.10.50.4 LowMedium High A60100 B20165340 C204060 20

35 LowMediumHigh Hire-10+(20x2)=30-10+(60x2)=110-10+(100x2)=190 CAD -130+(20x2)=-90-130+(165x2)=100-130+(340x2)=650 Do nothing20x2=4040x2=8060x2=120 Do nothing now, hire next year if demand is high 60+(- 10+100)=150 Demand Payoffs for each alternative:

36 Example of Decision Tree Problem: We can take actions sequentially: Wait until next year and if the demand is high, arrange hiring for the year after. Assume no discounting. A B C High demand (.4) Medium demand (.5) Low demand (.1) High demand (.4) Medium demand (.5) Low demand (.1) High demand (.4) Medium demand (.5) Low demand (.1) 120 8080 4040 $134k $301k Do nothing Arrange hiring 150 30 110 190 -90 100 650 $ 104k


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