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

2. Announcements Change in syllabus plan as follows: Today: NPD & DA
Chapter 5 ( ; )
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
Cost of
Development
& Manufacture
Sales Revenue
Sales, Cost & Profit .
Profit
Cash flow
Loss
Time
Introduction
Growth
Maturity
Decline

9. 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

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 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

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
Compounds Evaluated
2.5 – 3.5 yrs.
Compound Focus followed by indication Focus
1 – 3 compounds
Negation
1 – 3 Compounds Evaluated
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 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

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
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

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. 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

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
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

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

26. 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

27. 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

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. 20

29. 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

30. Example of a Decision Tree Problem: Step 1: We start by drawing the three decisions22

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

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

33. 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

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
Low
Medium
High 20 A 60
100 B 20
165
340 C 20 40 60

35. 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+( )=150

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.
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