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    Positioning the Firm: Cost

    Waste elimination relentlessly pursuing the removal of all waste Examination of cost structure

    looking at the entire cost structure for reduction potential

    Lean production providing low costs through disciplined operations

    1-1

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    Positioning the Firm: Speed

    Fast moves, Fast adaptations, Tight linkages Internet Customers expect immediate responses

    Service organizations always competed on speed (McDonalds, LensCrafters,

    and Federal Express)

    Manufacturers time-based competition: build-to-order production and

    efficient supply chains

    Fashion industry two-week design-to-rack lead time of Spanish retailer, Zara

    1-2

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    Positioning the Firm: Quality

    Minimizing defect rates or conforming to designspecifications

    Ritz-Carlton - one customer at a time

    Service system designed to move heaven and earth

    to satisfy customer

    Employees empowered to satisfy a guests wish

    Teams set objectives and devise quality action plans

    Each hotel has a quality leader

    1-3

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    Positioning the Firm: Flexibility

    Ability to adjust to changes in product mix,production volume, or design

    Mass customization: the mass production ofcustomized parts

    National Bicycle Industrial Company offers 11,231,862 variations

    delivers within two weeks at costs only 10% abovestandard models

    1-4

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    Policy Deployment

    Policy deployment translates corporate strategy into measurable

    objectives

    Hoshins

    action plans generated from the policydeployment process

    1-5

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    Policy Deployment

    Derivation of an Action Plan Using Policy Deployment

    1-6

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    Balanced Scorecard

    Balanced scorecard measuring more than financial performance

    1. finances

    2. customers

    3. processes

    4. learning and growing

    Key performance indicators

    set of measures to help managers evaluateperformance in critical areas

    1-7

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    Balanced Scorecard Worksheet

    1-8

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    Balanced Scorecard

    1-9

    Radar Chart Dashboard

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    Copyright 2011 John Wiley & Sons, Inc.Copyright 2011 John Wiley & Sons, Inc.

    Operations Strategy

    Products

    1-10

    Services Process

    and

    Technology

    Capacity

    Human

    Resources Quality

    Facilities Sourcing Operating

    Systems

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    Copyright 2011 John Wiley & Sons, Inc.Copyright 2011 John Wiley & Sons, Inc.

    Decision Analysis

    Quantitative methods a set of tools for operations manager

    Decision analysis

    a set of quantitative decision-makingtechniques for decision problems in whichuncertainty exists

    Example of an uncertain situation

    demand for a product may vary between 0 and200 units, depending on the state of market

    Supplement 1-11

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    Problem Formulation

    A decision problem is characterized by decision

    alternatives, states of nature, and resultingpayoffs.

    The decision alternatives are the differentpossible strategies the decision maker canemploy.

    The states of nature refer to future events, notunder the control of the decision maker, whichmay occur in the future. States of nature shouldbe defined so that they are mutually exclusiveand collectively exhaustive.Examples,

    high or low demand for a product

    good or bad economic conditions

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    Payoff Tables

    The consequence resulting from a specificcombination of a decision alternative and astate of nature is a payoff.

    A table showing payoffs for all combinations

    of decision alternatives and states of nature isa payoff table.

    Payoffs can be expressed in terms of profit,cost, time, distance or any other appropriate

    measure.

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    Payoff Table

    Payoff table method for organizing and illustrating payoffs from

    different decisions given various states of nature

    Payoff

    outcome of a decision

    Supplement 1-14

    States Of Nature

    Decision a b

    1 Payoff 1a Payoff 1b2 Payoff 2a Payoff 2b

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    Copyright 2011 John Wiley & Sons, Inc.Copyright 2011 John Wiley & Sons, Inc.

    Decision Making

    Decision making under risk probabilities can be assigned to the

    occurrence of states of nature in the

    future Decision making under uncertainty

    probabilities can NOT be assigned to the

    occurrence of states of nature in thefuture

    Supplement 1-15

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    Decision Trees

    A decision tree is a chronologicalrepresentation

    of the decision problem. Each decision tree has two types of nodes;

    round nodes correspond to the states of naturewhile square nodes correspond to the decision

    alternatives. The branches leaving each round node

    represent the different states of nature while thebranches leaving each square node representthe different decision alternatives.

    At the end of each limb of a tree are the payoffsattained from the series of branches making upthat limb.

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    Decision Making Criteria UnderUncertainty

    Maximax choose decision with the maximum of the

    maximum payoffs

    Maximin choose decision with the maximum of the

    minimum payoffs

    Minimax regret choose decision with the minimum of the

    maximum regrets for each alternative

    Supplement 1-17

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    Decision Making Criteria UnderUncertainty

    Hurwicz

    choose decision in which decision payoffs areweighted by a coefficient of optimism, alpha

    coefficient of optimism is a measure of adecision makers optimism, from 0(completelypessimistic) to 1(completely optimistic)

    Equal likelihood (La Place)

    choose decision in which each state of nature isweighted equally

    Supplement 1-18

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    Southern Textile Company

    Supplement 1-19

    STATES OF NATURE

    Good Foreign Poor Foreign

    DECISION Competitive Conditions Competitive Conditions

    Expand $ 800,000 $ 500,000

    Maintain status quo 1,300,000 -150,000

    Sell now 320,000 320,000

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    Maximax Solution

    Supplement 1-20

    STATES OF NATURE

    Good Foreign Poor Foreign

    DECISION Competitive Conditions Competitive ConditionsExpand $ 800,000 $ 500,000

    Maintain status quo 1,300,000 -150,000

    Sell now 320,000 320,000

    Expand: $800,000

    Status quo: 1,300,000 Maximum

    Sell: 320,000

    Decision: Maintain status quo

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    Maximin Solution

    Supplement 1-21

    STATES OF NATURE

    Good Foreign Poor Foreign

    DECISION Competitive Conditions Competitive ConditionsExpand $ 800,000 $ 500,000

    Maintain status quo 1,300,000 -150,000

    Sell now 320,000 320,000

    Expand: $500,000 Maximum

    Status quo: -150,000

    Sell: 320,000

    Decision: Expand

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    Minimax Regret Solution

    Supplement 1-22

    States of NatureGood Foreign Poor Foreign

    Competitive Conditions Competitive Conditions

    $1,300,000 - 800,000 = 500,000 $500,000 - 500,000 = 0

    1,300,000 - 1,300,000 = 0 500,000 - (-150,000)= 650,0001,300,000 - 320,000 = 980,000 500,000 - 320,000= 180,000

    Expand: $500,000 Minimum

    Status quo: 650,000

    Sell: 980,000Decision: Expand

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    Hurwicz Criteria

    Supplement 1-23

    STATES OF NATURE

    Good Foreign Poor Foreign

    DECISION Competitive Conditions Competitive ConditionsExpand $ 800,000 $ 500,000

    Maintain status quo 1,300,000 -150,000

    Sell now 320,000 320,000

    = 0.3 1 - = 0.7

    Expand: $800,000(0.3) + 500,000(0.7) = $590,000 Maximum

    Status quo: 1,300,000(0.3) -150,000(0.7) = 285,000Sell: 320,000(0.3) + 320,000(0.7) = 320,000

    Decision: Expand

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    Equal Likelihood Criteria

    Supplement 1-24

    STATES OF NATURE

    Good Foreign Poor Foreign

    DECISION Competitive Conditions Competitive ConditionsExpand $ 800,000 $ 500,000

    Maintain status quo 1,300,000 -150,000

    Sell now 320,000 320,000

    Two states of nature each weighted 0.50

    Expand: $800,000(0.5) + 500,000(0.5) = $650,000 Maximum

    Status quo: 1,300,000(0.5) -150,000(0.5) = 575,000

    Sell: 320,000(0.5) + 320,000(0.5) = 320,000

    Decision: Expand

    Decision Making under Risk

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    Copyright 2011 John Wiley & Sons, Inc.Copyright 2011 John Wiley & Sons, Inc.

    Decision Making under Risk(with Probabilities)

    Risk involves assigning probabilities tostates of nature

    Expected value

    a weighted average of decision outcomesin which each future state of nature isassigned a probability of occurrence

    Supplement 1-25

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    Copyright 2011 John Wiley & Sons, Inc.Copyright 2011 John Wiley & Sons, Inc.

    Expected Value

    Supplement 1-26

    EV (x) = p(xi)xin

    i =1

    xi = outcome i

    p(xi) = probability of outcome i

    where

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    Decision Making with Probabilities

    Supplement 1-27

    STATES OF NATURE

    Good Foreign Poor Foreign

    DECISION Competitive Conditions Competitive ConditionsExpand $ 800,000 $ 500,000

    Maintain status quo 1,300,000 -150,000

    Sell now 320,000 320,000

    p(good) = 0.70 p(poor) = 0.30EV(expand): $800,000(0.7) + 500,000(0.3) = $710,000

    EV(status quo): 1,300,000(0.7) -150,000(0.3) = 865,000 Maximum

    EV(sell): 320,000(0.7) + 320,000(0.3) = 320,000

    Decision: Status quo

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    Expected Value of Perfect Information

    Frequently, information is available which can

    improve the probability estimates for the states ofnature.

    The expected value of perfect information (EVPI) isthe increase in the expected profit that would result

    if the decision maker knew with certainty whichstate of nature would actually occur.

    The EVPI provides an upper bound on the

    expected value of any sample or surveyinformation. Thus, it is the maximum amount thatmight be paid to gain information that would resultin a decision better than the one made without

    perfect information.

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    EVPI

    Good conditions will exist 70%of the time choose maintain status quo with payoff of$1,300,000

    Poor conditions will exist 30%of the time choose expand with payoff of $500,000

    Expected value given perfect information=$1,300,000 (0.70) + 500,000 (0.30)= $1,060,000

    Recall that expected value without perfectinformation was $865,000 (maintain status quo)

    EVPI=$1,060,000 - 865,000 = $195,000

    Supplement 1-29

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    Sequential Decision Trees

    A graphical method for analyzing decisionsituations that require a sequence of decisionsover time

    Decision tree consists of

    Square nodes - indicating decision points Circles nodes - indicating states of nature Arcs - connecting nodes

    Supplement 1-30

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    Risk Analysis

    Risk analysis helps the decision maker

    recognize the difference between:

    the expected value of a decisionalternative, and

    the payoff that might actually occur The risk profile for a decision alternative

    shows the possible payoffs for the decisionalternative along with their associatedprobabilities.

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    Sensitivity Analysis

    Sensitivity analysis can be used to determine

    how changes to the following inputs affectthe recommended decision alternative:

    probabilities for the states of nature

    values of the payoffs If a small change in the value of one of the

    inputs causes a change in the recommendeddecision alternative, extra effort and careshould be taken in estimating the input value.