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
Sales, Cost & Profit .
Cost of
Development
& Manufacture
Sales Revenue
Profit
Cash flow
Loss
Time
Introduction
Growth
Maturity
Decline
Process Life Cycle
Start-Up
Rapid Growth
Maturity
Stability
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
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
50%
45%
40%
35%
30%
25%
20%
15%
10%
5%
0%
Industry Leader
Percent of Sales From New Product
Top
Third
Middle
Third
Bottom
Third
Position of Firm in Its Industry
Few Successes
Number
2000
1500
1000
500
0
Ideas
1750
Design review,
Testing, Introduction
Market
requirement
1000
Functional
specifications
500
Product
specification
100
Development Stage
25
One
success!
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
Discovery
Size of Opportunity
Funnel
Cycle Time
Project Definition
Output
Dominant Theme
Phase I
Clinical
Phase II
Clinical
Proof Of
Concept
Phase III
Clinical
WMA
&
Post Filing
Product Development
• 5,000 – 10,000
Compounds
Evaluated
• 5 - 10
Compounds
Evaluated
•1–3
Compounds
Evaluated
• 6.5 yrs.
• 2.5 – 3.5 yrs.
• 2.5 - 3.5 yrs.
•Target Focus
followed by Lead
Focus.
• Compound
Focus followed by
indication Focus
• Indication Focus
followed by
Extension Focus.
• 5 – 10
compounds
• 1–3
compounds
• 0–2
compounds
• Throughput
• Negation
• Run Fast
~$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
 A method of organizing and illustrating the payoffs from
different decisions given various states of nature
 A payoff is the outcome of the decision:
Decision
1
2
States of Nature
a
b
payoff 1a
payoff 1b
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 Unfavorable Maximum
Construct
large plant
Construct
small plant
Do nothing
Market
$200,000
$100,000
$0
Minimum
Row
Market
in Row
in Row Average
-$180,000 $200,000 -$180,000 $10,000
-$20,000 $100,000
$0
Maximax
-$20,000 $40,000
$0
Maximin
$0
Equally
likely
$0
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
Construct
large plant
Construct
small plant
Do nothing
Favorable
Unfavorable
Market
Market P(0.5)
P(0.5)
$200,000
-$180,000
$100,000
-$20,000
$0
$0
Expected
value
$10,000
$40,000 Best choice
$0
Expected Value of Perfect Information
(EVPI)
 EVPI places an upper bound on what
one would pay for additional information
 EVPI is the expected value with
certainty minus the maximum EMV
Expected Value of Perfect Information
Alternative
Construct a
large plant
Construct a small
plant
Do nothing
Probabilities
State of Nature
Favorable Unfavorable
Market ($) Market ($)
EMV
200,000
-$180,000
$20,000
$100,000
-$20,000
$40,000
$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
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
Payoff 2
2
Payoff 3
1
Payoff 4
2
Payoff 5
Decision Point
Chance Event, state of nature
Payoff 6
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.
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:
A
B
C
0.1
Low
10
-120
20
0.5
Medium
50
25
40
0.4
High
90
200
60
Example of a Decision Tree Problem:
Step 1: We start by drawing the three decisions
A
B
C
Example of Decision Tree Problem:
Step 2: Add our possible states of nature, probabilities, and
payoffs
High demand (.4)
Medium demand (.5)
Low demand (.1)
A
High demand (.4)
B
Medium demand (.5)
Low demand (.1)
C
High demand (.4)
Medium demand (.5)
Low demand (.1)
$90k
$50k
$10k
$200k
$25k
-$120k
$60k
$40k
$20k
Example of Decision Tree Problem:
Step 3: Determine the expected value of each
decision
High demand (.4)
$62k
Medium demand (.5)
Low demand (.1)
$90k
$50k
$10k
A
EVA=.4(90)+.5(50)+.1(10)=$62k
Example of Decision Tree Problem:
Step 4: Make the decision
Medium demand (.5)
$90k
$50k
Low demand (.1)
$10k
High demand (.4)
$200k
$25k
High demand (.4)
$62k
A
B
$80.5k
C
$46k
Medium demand (.5)
Low demand (.1)
-$120k
Medium demand (.5)
$60k
$40k
Low demand (.1)
$20k
High demand (.4)
Alternative B generates the greatest expected profit,
so our choice is B or to invest in CAD
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
Low
A
B
C
20
20
20
0.5
Medium
60
165
40
0.4
High
100
340
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.
$134k
A
C
B
$301k
High demand
(.4)
$ 104k
High demand (.4)
Medium demand (.5)
Low demand (.1)
190
110
30
High demand (.4)
Medium demand (.5)
Low demand (.1)
650
100
-90
Arrange hiring
Do nothing
120
Medium demand (.5)
Low demand (.1)
150
80
40