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304 Risk and uncertainty management as a marketing issue may enable non-financial incentives to be packaged with the product one is trying to sell. Brainstorming has four basic rules: (1) Do not criticize ideas – the solution to the problem may turn out to lie in an idea that, initially, may seem to be crazy. (2) Encourage participants to put forward any idea that they can think of – particularly unconventional or outlandish ideas. (3) Aim to generate large quantities of ideas – in that way there is a greater chance that one or more of the ideas will lead to a solution to the problem. (4) Encourage people to combine or modify ideas that have already been put forward. A brainstorming session can be run in several ways. However, ideally, in addition to the group members, it will involve a facilitator, whose role is to create a relaxed atmosphere, to encourage new ideas and to control the group’s discussion, and a person to record the ideas put forward. At the start of the session the specific problem is defined (for example, how can we increase the probability of the major customer signing the contact?) and displayed on a board or screen. Then ideas are suggested one at a time and recorded. When the group appear to be unable to generate further ideas the list is evaluated and the best ideas are chosen. Table 11.3 shows some typical ideas that might be generated through brainstorming for the Two Valleys Company. The best ideas will then need to be investigated to establish whether they are feasible and realistic and they will also need to be costed. Recall that our analysis has revealed that answers to the first two questions are most likely to be of value. The simulation model can then be re-run with the revised inputs and the results assessed. In this case, the following package of measures was investigated: (i) run administrative tasks at company HQ, rather than at local factory, (ii) contract out research and development, (iii) buy equipment that is easily upgraded if technology advances, and (iv) put the product instructions on the Internet, rather than supplying hard copy. All of the measures appeared to be feasible. The probability distributions for costs were re-estimated in the light of these changes and the new model simulated. Figure 11.5 shows the new and old cumulative probability distributions of annual profit at Littleton. It can be seen that the effect of these measures is to push the bulk of the distribution to the right. This means that any given profit up to $7 million has a higher probability of
Summary 305 Table 11.3 – Ideas for risk management at Two Valleys Company Question Ideas How can we reduce fixed costs? How can we increase open-market demand? How can we reduce variable costs per unit? How can we increase the chances of the contract being signed? Try to renegotiate rent Run administrative tasks at company HQ, rather than at local factory Contract out research and development Buy equipment that is easily upgraded if technology advances Contract out transport and delivery Lower price Advertise in trade journals Increase number of sales representatives Attend more trade fairs Reduce percentage of defective components Reduce waste Increase frequency of service of manufacturing plant Reduce packaging Optimize inventory levels for finished goods Choose alternative supplier for materials Put product instructions on the Internet, rather than supplying hard copy Offer a larger price discount Offer free delivery Offer extended warranty period Involve customer in ongoing design improvements Offer buy-back of old components in event of upgrade being achieved while the probability of a loss has been more than halved to 3%. Summary This chapter has shown that risks represented in decision models need not be regarded as immutable. The proactive manager, who is unwilling
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Decision Analysis for Management Ju
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Decision Analysis for Management Ju
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To Mary and Josephine, Jamie, Jerom
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viii Contents Chapter 16 The analyt
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x Foreword real problems, and it is
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xii Preface accountancy, where rece
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1 Introduction Complex decisions Im
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The role of decision analysis 3 you
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Applications of decision analysis 5
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Overview of the book 11 and profit.
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References 13 8. Walls, M. R., Mora
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16 How people make decisions involv
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28 Decisions involving multiple obj
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72 Introduction to probability Beca
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74 Introduction to probability prac
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76 Introduction to probability we w
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78 Introduction to probability whet
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80 Introduction to probability give
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82 Introduction to probability prob
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84 Introduction to probability deci
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86 Introduction to probability that
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88 Introduction to probability inte
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90 Introduction to probability subj
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92 Introduction to probability (5)
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94 Introduction to probability (a)
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96 Decision making under uncertaint
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98 Decision making under uncertaint
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100 Decision making under uncertain
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6 Decision trees and influence diag
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Constructing a decision tree 145 be
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Determining the optimal policy 147
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Decision trees and utility 149 succ
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Decision trees involving continuous
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Practical applications of decision
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Assessment of decision structure 15
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Assessment of decision structure 15
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Eliciting decision tree representat
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Eliciting decision tree representat
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Summary 163 where the resulting dec
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Exercises 165 other elements of the
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Exercises 167 (b) Assuming that Wes
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Exercises 169 is not hired, given t
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Exercises 171 failing to meet the p
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Exercises 173 why it was rational f
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Exercises 175 (11) In the Eagle Mou
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References 177 ABC Chemicals are pl
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7 Applying simulation to decision p
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Monte Carlo simulation 181 Of cours
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Monte Carlo simulation 183 Table 7.
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Applying simulation to a decision p
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Applying simulation to investment d
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Summary 205 Hull 10 ), some of them
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Exercises 207 (c) Use your simulati
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Exercises 209 For each factor the f
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Exercises 211 (b) Suppose that your
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References 213 6 million Therefore
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216 Revising judgments in the light
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9 Biases in probability assessment
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Introduction 249 of people watching
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The availability heuristic 251 make
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14 Decision framing and cognitive i
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How people frame decisions 357 Figu
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Imposing imaginary constraints and
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Inertia in strategic decision makin
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Studies in the psychological labora
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Overcoming inertia? 369 it is diffi
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Discussion questions 373 frame anal
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References 375 References 1. Luchin
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15 Scenario planning: an alternativ
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Introduction 379 documented in Chap
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Scenario construction: the extreme-
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Using scenarios in decision making
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Using scenarios in decision making
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Scenario construction: the driving
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Case study of a scenario interventi
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Table 15.1 - The components of the
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Illustrative case study 403 (b) Att
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Illustrative case study 405 Stage 4
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Illustrative case study 407 importa
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Conclusion 409 combinations. Often
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References 411 2. Van der Heijden,
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414 The analytic hierarchy process
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17 Alternative decision-support sys
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Expert systems 429 generally agreed
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Expert systems 431 that knowledge e
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Expert systems 433 (1) That the sub
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Expert systems 435 information syst
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Expert systems 437 to focus on ‘t
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Expert systems 439 On the basis of
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Is there any intention/expectation
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Expert systems 443 described above
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Expert systems 445 body of knowledg
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Statistical models of judgment 447
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Comparisons 453 ‘broken leg’ cu
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Some final words of advice 455 Ques
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Some final words of advice 457 of t
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References 459 Table 17.1 - Summary
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References 461 20. Eisenhart, J. (1
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Chapter 3 Suggested answers to sele
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Suggested answers to selected quest
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472 Index Brown, C.E. 446 Brown, S.
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474 Index HIVIEW 58 Hodgkinson, G.P
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476 Index scenario intervention cas
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