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OPSM 301 Operations Management

Ko ç Un iversity. 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)

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OPSM 301 Operations Management

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  1. Koç University OPSM 301 Operations Management Class 11: New Product Development Decision Analysis 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 CycleIntroduction • Fine tuning • research • product development • process modification and enhancement • supplier development

  5. Product Life CycleGrowth • Product design begins to stabilize • Effective forecasting of capacity becomes necessary • Adding or enhancing capacity may be necessary

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

  7. Product Life CycleDecline • Unless product makes a special contribution, must plan to terminate offering

  8. 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

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

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

  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 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

  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 Decision a b 1 payoff 1a payoff 1b 2 payoff 2a payoff 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. States of Nature Alternatives Favorable Unfavorable Maximum Minimum Row Market Market in Row in Row Average Construct $200,000 - $180,000 $200,000 - $180,000 $10,000 large plant Construct $100,000 - $20,000 $100,000 - $20,000 $40,000 small plant $0 $ 0 $0 $0 $0 Do nothing Maximax Maximin Equally likely Example - Decision Making Under Uncertainty

  21. 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

  22. States of Nature Alternatives Favorable Unfavorable Expected Market Market P(0.5) value P(0.5) Construct $200,000 -$180,000 $10,000 large plant Construct $100,000 -$20,000 $40,000 Best choice small plant Do nothing $0 $0 $0 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 • EVPIis the expected value with certainty minus the maximum EMV

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

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

  26. 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

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

  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. 0.1 0.5 0.4 High Low Medium A 10 50 90 B -120 25 200 C 20 40 60 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:

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

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

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

  33. $90k High demand (.4) $50k Medium demand (.5) $10k Low demand (.1) A $200k B High demand (.4) $25k -$120k Medium demand (.5) C Low demand (.1) $60k $40k High demand (.4) $20k Medium demand (.5) Low demand (.1) Example of Decision Tree Problem:Step 4: Make the decision $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: 0.1 0.5 0.4 High Low Medium 20 A 60 100 B 20 165 340 C 20 40 60

  35. Payoffs for each alternative: Demand

  36. High demand (.4) Medium demand (.5) Low demand (.1) High demand (.4) Medium demand (.5) Low demand (.1) 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. 190 110 30 $134k 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

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