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414 The analytic hierarchy process the possible options to three: (i) the Aztec, (ii) the Barton and (iii) the Congress. However, the decision is still proving to be difficult because of the variety of attributes associated with the machines, such as the purchase price, reputation for reliability and the quality of after-sales support provided by the different manufacturers. An overview of the AHP Once the decision maker and the alternative courses of action have been identified, the main stages of the method are shown below: Stage 1: Set up the decision hierarchy. This is similar to a value tree in SMART but, as we will see, the main difference is that the alternative courses of action also appear on the hierarchy at its lowest level. Stage 2: Make pairwise comparisons of attributes and alternatives. This is used to determine the relative importance of attributes and also to compare how well the options perform on the different attributes. For example, how much more important is the initial purchase price than the cost of upgrading the machine at a later date? Is the Aztec strongly preferred to the Barton for the quality of after-sales support? Stage 3: Transform the comparisons into weights and check the consistency of the decision maker’s comparisons. Stage 4: Use the weights to obtain scores for the different options and make a provisional decision. Stage 5: Perform sensitivity analysis. This will enable the decision maker to examine how robust the provisional decision is to changes in the ratings of importance and preference. Note that stages 3, 4 and 5 require a computer package, like EXPERT CHOICE, because of the complexity of the calculations that are involved. We next consider each of the stages in more detail. Setting up the decision hierarchy Figure 16.1 shows the decision hierarchy for the packaging machine problem. At the top of the tree is a statement of the general objective
Making pairwise comparisons of attributes and alternatives 415 Costs Choose a Machine Purchase Upgrade Reliability After-Sales Support Aztec Barton Congress Aztec Barton Congress Aztec Barton Congress Aztec Barton Congress Quality Figure 16.1 – A hierarchy for the packaging machine problem Speed of Delivery Customization Aztec Barton Congress Aztec Barton Congress of the decision, in our case: ‘Choose a Machine’. The ‘general’ attributes associated with the decision problem (‘Costs’ and ‘Quality’) are then set out below this. As shown, these attributes can be broken down into more detail at the next level. For example, within ‘Quality’ the manager wishes to consider the attributes ‘Reliability’, ‘After-Sales Support’, ‘Speed of Delivery’ and ‘Customization’ (i.e. the extent to which the manufacturer is able to adapt the machine for the specific requirements of the food company). If necessary, this process of breaking down attributes continues until all the essential criteria for making the decision have been specified. Finally, the alternative courses of action are added to the hierarchy, below each of the lowest-level attributes. Making pairwise comparisons of attributes and alternatives As we saw in Chapter 3, SMART uses swing weights to compare the ‘importance’ of attributes, while the performance of options is measured on a 0–100 scale. The AHP uses a fundamentally different approach. Following each ‘split’ in the hierarchy the importance of each attribute is compared, in turn, with every other attribute immediately below that ‘split’. Thus the importance of ‘Costs’ and ‘Quality’ are first compared. Then the four ‘Quality’ attributes are compared with each other for importance and so on. Note that the comparisons are pairwise so that
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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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The representativeness heuristic 25
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The anchoring and adjustment heuris
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The anchoring and adjustment heuris
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Other judgmental biases 263 Other j
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Is human probability judgment reall
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Exercises 271 Did you receive timel
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References 273 have been taken from
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References 275 18. Hamilton, D. L.
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10 Methods for eliciting probabilit
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Preparing for probability assessmen
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Consistency and coherence checks 28
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Assessment of the validity of proba
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Assessment of the validity of proba
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Assessing probabilities for very ra
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Exercises 293 Odds in favor of even
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References 295 13. Wright, G. and W
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298 Risk and uncertainty management
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310 Decisions involving groups of i
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330 Resource allocation and negotia
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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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Page 445 and 446: Expert systems 429 generally agreed
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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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