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Supplement to Chapter 6 Forecasting 175 Figure S6.5 Regression line showing the relationship between the previous week’s average temperature and demand it figures that the main influence on demand at the factory is the average temperature of the previous week. To understand this relationship, the company plots demand against the previous week’s temperatures. This is shown in Figure S6.5. Using this graph, the company can make a reasonable prediction of demand, once the average temperature is known, provided that the other conditions prevailing in the market are reasonably stable. If they are not, then these other factors which have an influence on demand will need to be included in the regression model, which becomes increasingly complex. These more complex networks comprise many variables and relationships each with their own set of assumptions and limitations. While developing such models and assessing the importance of each of the factors and understanding the network of interrelationships is beyond the scope of this text, many techniques are available to help managers undertake this more complex modelling and also feed back data into the model to further refine and develop it, in particular structural equation modelling. The performance of forecasting models Forecasting models are widely used in management decision-making, and indeed most decisions require a forecast of some kind, yet the performance of this type of model is far from impressive. Hogarth and Makridakis, 2 in a comprehensive review of the applied management and finance literature, show that the record of forecasters using both judgement and sophisticated mathematical methods is not good. What they do suggest, however, is that certain forecasting techniques perform better under certain circumstances. In short-term forecasting there is: considerable inertia in most economic and natural phenomena. Thus the present states of any variables are predictive of the short-term future (i.e. three months or less). Rather simple mechanistic methods, such as those used in time series forecasts, can often make accurate short-term forecasts and even out-perform more theoretically elegant and elaborate approaches used in econometric forecasting. 3 Long-term forecasting methods, although difficult to judge because of the time lapse between the forecast and the event, do seem to be more amenable to an objective causal approach. In
176 Part Two Design a comparative study of long-term market forecasting methods, Armstrong and Grohman 4 conclude that econometric methods offer more accurate long-range forecasts than do expert opinion or time series analysis, and that the superiority of objective causal methods improves as the time horizon increases. Selected further reading Hoyle R.H. (ed.) (1995) Structural Equation Modeling, Sage, Thousand Oaks, California. For the specialist. Maruyama G.M. (1997) Basics of Structural Equation Modeling, Sage, Thousand Oaks, California. For the specialist.
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sixth edition OperatiOns ManageMent
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Further reading in Operations Manag
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Pearson Education Limited Edinburgh
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Contents Guide to ‘operations in
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Contents ix Chapter 12 Inventory pl
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Guide to ‘operations in practice
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Guide to ‘operations in practice
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Making the most of this book and My
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Making the most of this book and My
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Preface xix Illustrations-based Ope
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To the Student . . . Making the mos
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About the authors Nigel Slack is th
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Publisher’s acknowledgements We a
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Part One INTRODUCTION This part of
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Chapter 1 Operations management 3 O
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Key operations questions Chapter 4
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Chapter 4 Process design Key questi
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88 Part Two Design Figure 4.2 The d
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90 Part Two Design Environmentally
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92 Part Two Design Figure 4.3 Diffe
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94 Part Two Design Professional ser
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96 Part Two Design Figure 4.4 Devia
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98 Part Two Design Figure 4.6 Proce
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100 Part Two Design Figure 4.8 Flow
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102 Part Two Design Given that an i
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104 Part Two Design From Little’s
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106 Part Two Design The relationshi
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108 Part Two Design Summary answers
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110 Part Two Design the grant appli
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Chapter 5 The design of products an
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114 Part Two Design hopeless lack o
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116 Part Two Design Core products a
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118 Part Two Design Figure 5.3 The
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120 Part Two Design Short case Squa
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122 Part Two Design Critical commen
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226 Part Two Design Table 8.5 Prese
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228 Part Two Design Anticipating im
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230 Part Two Design Case study Roch
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232 Part Two Design Problems and ap
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234 Part Two Design Operations in p
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236 Part Two Design Human resource
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238 Part Two Design Table 9.2 Cause
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240 Part Two Design ● Group resou
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242 Part Two Design Job design Job
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244 Part Two Design ● ● ● ●
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246 Part Two Design Figure 9.7 A ty
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248 Part Two Design asked to contri
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250 Part Two Design ● ● ● Eff
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252 Part Two Design Table 9.3 Examp
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254 Part Two Design Qualified worke
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256 Part Two Design Case study Serv
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258 Part Two Design 3 4 5 Step 1 -
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260 Part Two Design them. This mean
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262 Part Two Design Work measuremen
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264 Part Two Design Figure S9.1 Tim
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Key operations questions Chapter 10
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Chapter 10 The nature of planning a
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270 Part Three Planning and control
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276 Part Three Make-to-stock Planni
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282 Part Three Last-in first-out se
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Chapter 12 Inventory planning and c
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Chapter 14 Enterprise resource plan
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Part Four IMPROVEMENT Even the best
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Chapter 18 Operations improvement 5
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in both scientific management and m
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Chapter 19 Risk management Key ques
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Risk mitigation actions Chapter 19
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Chapter 20 Organizing for improveme
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Part Five CORPORATE SOCIAL RESPONSI
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● ● ● ● ● Chapter 21 Oper
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Notes on chapters 653 3 Sources inc
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Notes on chapters 655 ground data:
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Notes on chapters 657 national Jour
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Glossary 659 Breakthrough improveme
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Glossary 667 Reliability: when appl
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Glossary 669 Transformation process
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‘blue sky’ research projects 45
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cumulative representation 317-22 cu
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failures (continued) estimates of o
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inventories (continued) de-coupling
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mitigation of ethical practice brea
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precedence diagrams 197-9, 481 prec
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esources 7-8 business continuity 59
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supply chains (continued) meaning 3
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