OPSM 301 Operations Management Class 11: New Product Development Decision Analysis Koç University Zeynep Aksin [email protected].

OPSM 301 Operations Management

Class 11:

New Product Development

Decision Analysis

Koç University

Zeynep [email protected]

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 CycleIntroduction

Fine tuning– research– product development– process modification and enhancement– supplier development

Product Life CycleGrowth

Product design begins to stabilize Effective forecasting of capacity becomes

necessary Adding or enhancing capacity may be

necessary

Product Life CycleMaturity

Competitors now established High volume, innovative production may

be needed Improved cost control, reduction in

options, paring down of product line

Product Life CycleDecline

Unless product makes a special contribution, must plan to terminate offering

Product Life Cycle, Sales, Cost, and ProfitSa

les,

Cos

t & P

rofit

.

Introduction Maturity DeclineGrowth

Cost ofDevelopment

& ManufactureSales Revenue

Time

Cash flowLoss

Profit

Process Life Cycle

Start-UpStart-UpRapid GrowthRapid GrowthMaturityMaturity StabilityStability

Job ShopJob Shop

LowLow

LowLow

LowLow

BatchBatchProductionProduction

IncreasingIncreasing

MediumMedium

MediumMedium

MassMassProductionProduction

HighHigh

HighHigh

HighHigh

MassMassProductionProduction

HighHigh

MediumMedium

HighHighAutomationAutomation

ProcessProcessInnovationInnovation

ThroughputThroughputVolumeVolume

ManufacturingManufacturingSystemSystem

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

0%5%

10%15%20%25%30%35%40%45%50%

Position of Firm in Its Industry

Indu

stry

Lea

der

Top Third Middle

ThirdBottomThird

Percent of Sales From New Product

Few SuccessesFew Successes

0

500

1000

1500

2000

Development Stage

Number

1000

Market requirement

Design review,Testing, Introduction

25

Ideas1750

Product specification

100

Functional specifications

One success!

500

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

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 OfConcept

Product Development

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:

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

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 $0 $0 $0

Maximax Maximin Equally likely

Do nothing

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

Example - Decision Making Under Risk

States of NatureAlternatives Favorable

MarketP(0.5)

UnfavorableMarket P(0.5)

Expectedvalue

Construct $200,000 -$180,000 $10,000

Constructsmall plant

$100,000 -$20,000 $40,000

Do nothing $0 $0 $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

State of NatureAlternative

Probabilities

Construct alarge plantConstruct a small plant

Do nothing

200,000 -$180,000

$0

Favorable Market ($)

Unfavorable Market ($)

0.50 0.50

EMV

$40,000$100,000 -$20,000

$0 $0

$20,000

Expected Value of Perfect Information

EVPIEVPI = 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

– 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

Format of a Decision Tree

Payoff 1State of nature 1

State of nature 2

Payoff 6State of nature 2

State of nature 1

Choos

e A

Choose B

1

Decision Point

Chance Event, state of nature

Payoff 2

Payoff 3

2

Choose A1

Choose A2

2

Payoff 4

Payoff 5

Choose B1

Choose B2

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 engineersB) 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

0.1 0.5 0.4Low Medium High

A 10 50 90B -120 25 200C 20 40 60

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:

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

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

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

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)

AB

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

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 0.4Low Medium High

A 60 100B 20 165 340C 20 40 60

20

Low Medium HighHire -

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

Demand

Payoffs for each alternative:

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.

AB

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

80

40

$134k

$301k

Do nothing

Arrange hiring 150

30110190

-90100650

$ 104k