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

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

Product Life Cycle Introduction Growth Maturity Decline .

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

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

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

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

Product Life Cycle, Sales, Cost, and Profit Cost of Development & Manufacture Sales Revenue Sales, Cost & Profit . Profit Cash flow Loss Time Introduction Growth Maturity Decline

Process Life Cycle Start-Up Rapid Growth Maturity Stability Automation Innovation Throughput Volume Manufacturing System Job Shop Low Batch Production Increasing Medium Mass Production High Mass Production High Medium

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

QFD House of Quality

Percent of Sales From New Product

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

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

Pharmaceutical Development Process Target ID & Validation Screening & Optimization Pre-Clinical Testing Phase I Clinical Phase II Clinical Phase III Clinical WMA & Post Filing Discovery Proof Of Concept Product Development Size of Opportunity Funnel 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 Cycle Time Project Definition Output Dominant Theme ~$1 Billion to Develop and Commercialize Important new compounds

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

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

Payoff Tables States of Nature Decision a b 1 payoff 1a payoff 1b 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 Decision a b 1 payoff 1a payoff 1b 2 payoff 2a payoff 2b

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.

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

Decision Making Under Risk 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

Example - Decision Making Under Risk States of Nature Alternatives Favorable Market P(0.5) Unfavorable Market P(0.5) Expected value Construct $200,000 -$180,000 $10,000 small plant $100,000 -$20,000 $40,000 Do nothing $0 Best choice large plant

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

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

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

Graphical display of decision process Used for solving problems Decision Trees 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

Format of a Decision Tree Payoff 1 State of nature 1 State of nature 2 Payoff 2 Payoff 3 2 Choose A1 Choose A2 Choose A 1 2 Payoff 4 Payoff 5 Choose B1 Choose B2 State of nature 2 State of nature 1 Choose B Decision Point Payoff 6 Chance Event, state of nature

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. 20

Example of a Decision Tree Problem: The Payoff Table 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: 0.1 0.5 0.4 Low Medium High A 10 50 90 B -120 25 200 C 20 40 60 21

Example of a Decision Tree Problem: Step 1: We start by drawing the three decisions 22

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 $200k $25k -$120k $60k $40k $20k 23

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 EVA=.4(90)+.5(50)+.1(10)=$62k $62k 24

Example of Decision Tree Problem: Step 4: Make the decision High demand (.4) Medium demand (.5) Low demand (.1) A B C $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 25

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: 0.1 0.5 0.4 Low Medium High 20 A 60 100 B 20 165 340 C 20 40 60

Payoffs for each alternative: Demand Low Medium High Hire -10+(20x2)=30 -10+(60x2)=110 -10+(100x2)=190 CAD -130+(20x2)=-90 -130+(165x2)=100 -130+(340x2)=650 Do nothing 20x2=40 40x2=80 60x2=120 Do nothing now, hire next year if demand is high 60+(-10+100)=150

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. High demand (.4) Medium demand (.5) Low demand (.1) 190 110 $134k 30 High demand (.4) Medium demand (.5) Low demand (.1) 650 A 100 B -90 $301k Arrange hiring 150 C High demand (.4) Do nothing 120 $ 104k Medium demand (.5) 80 Low demand (.1) 40 25