Intermittent Demand Forecasting
Description
The first text to focus on the methods and approaches of intermittent, rather than fast, demand forecasting
Intermittent Demand Forecasting is for anyone who is interested in improving forecasts of intermittent demand products, and enhancing the management of inventories. Whether you are a practitioner, at the sharp end of demand planning, a software designer, a student, an academic teaching operational research or operations management courses, or a researcher in this field, we hope that the book will inspire you to rethink demand forecasting. If you do so, then you can contribute towards significant economic and environmental benefits.
No prior knowledge of intermittent demand forecasting or inventory management is assumed in this book. The key formulae are accompanied by worked examples to show how they can be implemented in practice. For those wishing to understand the theory in more depth, technical notes are provided at the end of each chapter, as well as an extensive and up-to-date collection of references for further study. Software developments are reviewed, to give an appreciation of the current state of the art in commercial and open source software.
"Intermittent demand forecasting may seem like a specialized area but actually is at the center of sustainability efforts to consume less and to waste less. Boylan and Syntetos have done a superb job in showing how improvements in inventory management are pivotal in achieving this. Their book covers both the theory and practice of intermittent demand forecasting and my prediction is that it will fast become the bible of the field."
--Spyros Makridakis, Professor, University of Nicosia, and Director, Institute for the Future and the Makridakis Open Forecasting Center (MOFC).
"We have been able to support our clients by adopting many of the ideas discussed in this excellent book, and implementing them in our software. I am sure that these ideas will be equally helpful for other supply chain software vendors and for companies wanting to update and upgrade their capabilities in forecasting and inventory management."
--Suresh Acharya, VP, Research and Development, Blue Yonder.
"As product variants proliferate and the pace of business quickens, more and more items have intermittent demand. Boylan and Syntetos have long been leaders in extending forecasting and inventory methods to accommodate this new reality. Their book gathers and clarifies decades of research in this area, and explains how practitioners can exploit this knowledge to make their operations more efficient and effective."
--Thomas R. Willemain, Professor Emeritus, Rensselaer Polytechnic Institute.
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Persons
John E. Boylan is Professor of Business Analytics at Lancaster University, an Editor-in-Chief of the Journal of the Operational Research Society, and President of the International Society for Inventory Research.
Aris A. Syntetos is Professor of Operational Research and Operations Management at Cardiff University, an Editor-in-Chief of the IMA Journal of Management Mathematics, and Director of the International Institute of Forecasters.
Content
Preface xix
Glossary xxi
About the Companion Website xxiii
1 Economic and Environmental Context 1
1.1 Introduction 1
1.2 Economic and Environmental Benefits 3
1.2.1 After-sales Industry 3
1.2.2 Defence Sector 4
1.2.3 Economic Benefits 5
1.2.4 Environmental Benefits 5
1.2.5 Summary 6
1.3 Intermittent Demand Forecasting Software 6
1.3.1 Early Forecasting Software 6
1.3.2 Developments in Forecasting Software 6
1.3.3 Open Source Software 7
1.3.4 Summary 7
1.4 About this Book 7
1.4.1 Optimality and Robustness 7
1.4.2 Business Context 8
1.4.3 Structure of the Book 9
1.4.4 Current and Future Applications 10
1.4.5 Summary 10
1.5 Chapter Summary 11
Technical Note 11
2 Inventory Management and Forecasting 13
2.1 Introduction 13
2.2 Scheduling and Forecasting 13
2.2.1 Material Requirements Planning (MRP) 13
2.2.2 Dependent and Independent Demand Items 14
2.2.3 Make to Stock 15
2.2.4 Summary 15
2.3 Should an Item Be Stocked at All? 15
2.3.1 Stock/Non-Stock Decision Rules 16
2.3.2 Historical or Forecasted Demand? 18
2.3.3 Summary 18
2.4 Inventory Control Requirements 19
2.4.1 How Should Stock Records be Maintained? 19
2.4.2 When are Forecasts Required for Stocking Decisions? 22
2.4.3 Summary 24
2.5 Overview of Stock Rules 25
2.5.1 Continuous Review Systems 25
2.5.2 Periodic Review Systems 26
2.5.3 Periodic Review Policies 28
2.5.4 Variations of the (R, S) Periodic Policy 29
2.5.5 Summary 30
2.6 Chapter Summary 30
Technical Notes 31
3 Service Level Measures 33
3.1 Introduction 33
3.2 Judgemental Ordering 34
3.2.1 Rules of Thumb for the Order-Up-To Level 34
3.2.2 Judgemental Adjustment of Orders 34
3.2.3 Summary 35
3.3 Aggregate Financial and Service Targets 35
3.3.1 Aggregate Financial Targets 36
3.3.2 Service Level Measures 36
3.3.3 Relationships Between Service Level Measures 38
3.3.4 Summary 39
3.4 Service Measures at SKU Level 39
3.4.1 Cost Factors 39
3.4.2 Understanding of Service Level Measures 40
3.4.3 Potential Service Level Measures 40
3.4.4 Choice of Service Level Measure 41
3.4.5 Summary 42
3.5 Calculating Cycle Service Levels 42
3.5.1 Distribution of Demand Over One Time Period 43
3.5.2 Cycle Service Levels Based on All Cycles 44
3.5.3 Cycle Service Levels Based on Cycles with Demand 45
3.5.4 Summary 47
3.6 Calculating Fill Rates 48
3.6.1 Unit Fill Rates 48
3.6.2 Fill Rates: Standard Formula 49
3.6.3 Fill Rates: Sobel's Formula 51
3.6.4 Summary 53
3.7 Setting Service Level Targets 53
3.7.1 Responsibility for Target Setting 53
3.7.2 Trade-off Between Service and Cost 54
3.7.3 Setting SKU Level Service Targets 55
3.7.4 Summary 56
3.8 Chapter Summary 56
Technical Note 57
4 Demand Distributions 59
4.1 Introduction 59
4.2 Estimation of Demand Distributions 60
4.2.1 Empirical Demand Distributions 60
4.2.2 Fitted Demand Distributions 62
4.2.3 Summary 64
4.3 Criteria for Demand Distributions 64
4.3.1 Empirical Evidence for Goodness of Fit 64
4.3.2 Further Criteria 64
4.3.3 Summary 65
4.4 Poisson Distribution 65
4.4.1 Shape of the Poisson Distribution 66
4.4.2 Summary 67
4.5 Poisson Demand Distribution 67
4.5.1 Poisson: A Priori Grounds 67
4.5.2 Poisson: Ease of Calculation 67
4.5.3 Poisson: Flexibility 68
4.5.4 Poisson: Goodness of Fit 69
4.5.5 Testing for Goodness of Fit 70
4.5.6 Summary 72
4.6 Incidence and Occurrence 72
4.6.1 Demand Incidence 72
4.6.2 Demand Occurrence 73
4.6.3 Summary 74
4.7 Poisson Demand Incidence Distribution 75
4.7.1 A Priori Grounds 75
4.7.2 Ease of Calculation 75
4.7.3 Flexibility 76
4.7.4 Goodness of Fit 76
4.7.5 Summary 79
4.8 Bernoulli Demand Occurrence Distribution 79
4.8.1 Bernoulli Distribution: A Priori Grounds 79
4.8.2 Bernoulli Distribution: Ease of Calculation 80
4.8.3 Bernoulli Distribution: Flexibility 81
4.8.4 Bernoulli Distribution: Goodness of Fit 81
4.8.5 Summary 82
4.9 Chapter Summary 82
Technical Notes 83
5 Compound Demand Distributions 87
5.1 Introduction 87
5.2 Compound Poisson Distributions 88
5.2.1 Compound Poisson: A Priori Grounds 89
5.2.2 Compound Poisson: Flexibility 89
5.2.3 Summary 89
5.3 Stuttering Poisson Distribution 90
5.3.1 Stuttering Poisson: A Priori Grounds 91
5.3.2 Stuttering Poisson: Ease of Calculation 91
5.3.3 Stuttering Poisson: Flexibility 93
5.3.4 Stuttering Poisson: Goodness of Fit for Demand Sizes 93
5.3.5 Summary 95
5.4 Negative Binomial Distribution 96
5.4.1 Negative Binomial: A Priori Grounds 96
5.4.2 Negative Binomial: Ease of Calculation 96
5.4.3 Negative Binomial: Flexibility 97
5.4.4 Negative Binomial: Goodness of Fit 98
5.4.5 Summary 99
5.5 Compound Bernoulli Distributions 100
5.5.1 Compound Bernoulli: A Priori Grounds 100
5.5.2 Compound Bernoulli: Ease of Calculation 100
5.5.3 Compound Bernoulli: Flexibility 100
5.5.4 Compound Bernoulli: Goodness of Fit 101
5.5.5 Summary 101
5.6 Compound Erlang Distributions 101
5.6.1 Compound Erlang Distributions: A Priori Grounds 103
5.6.2 Compound Erlang Distributions: Ease of Calculation 104
5.6.3 Compound Erlang-2: Flexibility 104
5.6.4 Compound Erlang-2: Goodness of Fit 104
5.6.5 Summary 105
5.7 Differing Time Units 105
5.7.1 Poisson Distribution 106
5.7.2 Compound Poisson Distribution 106
5.7.3 Compound Bernoulli and Compound Erlang Distributions 107
5.7.4 Normal Distribution 108
5.7.5 Summary 110
5.8 Chapter Summary 110
Technical Notes 111
6 Forecasting Mean Demand 117
6.1 Introduction 117
6.2 Demand Assumptions 118
6.2.1 Elements of Intermittent Demand 119
6.2.2 Demand Models 119
6.2.3 An Intermittent Demand Model 120
6.2.4 Summary 121
6.3 Single Exponential Smoothing (SES) 121
6.3.1 SES as an Error-correction Mechanism 122
6.3.2 SES as aWeighted Average of Previous Observations 122
6.3.3 Practical Considerations 125
6.3.4 Summary 126
6.4 Croston's Critique of SES 126
6.4.1 Bias After Demand Occurring Periods 126
6.4.2 Magnitude of Bias After Demand Occurring Periods 128
6.4.3 Bias After Review Intervals with Demands 128
6.4.4 Summary 129
6.5 Croston's Method 129
6.5.1 Method Specification 129
6.5.2 Method Application 130
6.5.3 Summary 131
6.6 Critique of Croston's Method 132
6.6.1 Bias of Size-interval Approaches 132
6.6.2 Inversion Bias 132
6.6.3 Quantification of Bias 133
6.6.4 Summary 134
6.7 Syntetos-Boylan Approximation 134
6.7.1 Practical Application 134
6.7.2 Framework for Correction Factors 135
6.7.3 Initialisation and Optimisation 135
6.7.4 Summary 138
6.8 Aggregation for Intermittent Demand 138
6.8.1 Temporal Aggregation 138
6.8.2 Cross-sectional Aggregation 141
6.8.3 Summary 142
6.9 Empirical Studies 143
6.9.1 Single Series, Single Period Approaches 143
6.9.2 Single Series, Multiple Period Approaches 144
6.9.3 Summary 145
6.10 Chapter Summary 145
Technical Notes 146
7 Forecasting the Variance of Demand and Forecast Error 151
7.1 Introduction 151
7.2 Mean Known, Variance Unknown 151
7.2.1 Mean Demand Unchanging Through Time 152
7.2.2 Relating Variance Over One Period to Variance Over the Protection Interval 152
7.2.3 Summary 153
7.3 Mean Unknown, Variance Unknown 153
7.3.1 Mean and Variance Unchanging Through Time 154
7.3.2 Mean or Variance Changing Through Time 155
7.3.3 Relating Variance Over One Period to Variance Over the Protection Interval 156
7.3.4 Direct Approach to Estimating Variance of Forecast Error Over the Protection Interval 158
7.3.5 Implementing the Direct Approach to Estimating Variance Over the Protection Interval 160
7.3.6 Summary 160
7.4 Lead Time Variability 161
7.4.1 Consequences of Recognising Lead Time Variance 161
7.4.2 Variance of Demand Over a Variable Lead Time (Known Mean Demand) 162
7.4.3 Variance of Demand Over a Variable Lead Time (Unknown Mean Demand) 163
7.4.4 Distribution of Demand Over a Variable Lead Time 164
7.4.5 Summary 165
7.5 Chapter Summary 165
Technical Notes 166
8 Inventory Settings 169
8.1 Introduction 169
8.2 Normal Demand 170
8.2.1 Order-up-to Levels for Four Scenarios 170
8.2.2 Scenario 1: Mean and Standard Deviation Known 170
8.2.3 Scenario 2: Mean Demand Unknown Standard Deviation Known 172
8.2.4 Scenario 3: Mean Demand Known Standard Deviation Unknown 175
8.2.5 Scenario 4: Mean and Standard Deviation Unknown 176
8.2.6 Summary 177
8.3 Poisson Demand 177
8.3.1 Cycle Service Level System when the Mean Demand is Known 177
8.3.2 Fill Rate System when the Mean Demand is Known 178
8.3.3 Poisson OUT Level when the Mean Demand is Unknown 179
8.3.4 Summary 181
8.4 Compound Poisson Demand 181
8.4.1 Stuttering Poisson OUT Level when the Parameters are Known 181
8.4.2 Negative Binomial OUT Levels when the Parameters are Known 183
8.4.3 Stuttering Poisson and Negative Binomial OUT Levels when the Parameters are Unknown 183
8.4.4 Summary 184
8.5 Variable Lead Times 184
8.5.1 Empirical Lead Time Distributions 184
8.5.2 Summary 185
8.6 Chapter Summary 185
Technical Notes 186
9 Accuracy and Its Implications 193
9.1 Introduction 193
9.2 Forecast Evaluation 194
9.2.1 Only One Step Ahead? 194
9.2.2 All Points in Time? 194
9.2.3 Summary 195
9.3 Error Measures in Common Usage 195
9.3.1 Popular Forecast Error Measures 195
9.3.2 Calculation of Forecast Errors 197
9.3.3 Mean Error 197
9.3.4 Mean Square Error 198
9.3.5 Mean Absolute Error 198
9.3.6 Mean Absolute Percentage Error (MAPE) 198
9.3.7 100% Minus MAPE 199
9.3.8 Forecast Value Added 199
9.3.9 Summary 200
9.4 Criteria for Error Measures 200
9.4.1 General Criteria 200
9.4.2 Additional Criteria for Intermittence 201
9.4.3 Summary 201
9.5 Mean Absolute Percentage Error and its Variants 201
9.5.1 Problems with the Mean Absolute Percentage Error 202
9.5.2 Mean Absolute Percentage Error from Forecast 202
9.5.3 Symmetric Mean Absolute Percentage Error 203
9.5.4 MAPEFF and sMAPE for Intermittent Demand 204
9.5.5 Summary 205
9.6 Measures Based on the Mean Absolute Error 205
9.6.1 MAE: Mean Ratio 205
9.6.2 Mean Absolute Scaled Error 206
9.6.3 Measures Based on Absolute Errors 207
9.6.4 Summary 208
9.7 Measures Based on the Mean Error 208
9.7.1 Desirability of Unbiased Forecasts 209
9.7.2 Mean Error 209
9.7.3 Mean Percentage Error 210
9.7.4 Scaled Bias Measures 210
9.7.5 Summary 211
9.8 Measures Based on the Mean Square Error 211
9.8.1 Scaled Mean Square Error 212
9.8.2 Relative Root Mean Square Error 212
9.8.3 Percentage Best 213
9.8.4 Summary 213
9.9 Accuracy of Predictive Distributions 214
9.9.1 Measuring Predictive Distribution Accuracy 214
9.9.2 Probability Integral Transform for Continuous Data 215
9.9.3 Probability Integral Transform for Discrete Data 215
9.9.4 Summary 217
9.10 Accuracy Implication Measures 218
9.10.1 Simulation Outline 218
9.10.2 Forecasting Details 218
9.10.3 Simulation Details 219
9.10.4 Comparison of Simulation Results 220
9.10.5 Summary 221
9.11 Chapter Summary 221
Technical Notes 221
10 Judgement, Bias, and Mean Square Error 225
10.1 Introduction 225
10.2 Judgemental Forecasting 225
10.2.1 Evidence on Prevalence of Judgemental Forecasting 226
10.2.2 Judgemental Biases 226
10.2.3 Effectiveness of Judgemental Forecasts: Evidence for Non-intermittent Items 229
10.2.4 Effectiveness of Judgemental Forecasts: Evidence for Intermittent Items 230
10.2.5 Summary 231
10.3 Forecast Bias 232
10.3.1 Monitoring and Detection of Bias 232
10.3.2 Bias as an Expectation of a Random Variable 234
10.3.3 Response to Different Causes of Bias 235
10.3.4 Summary 236
10.4 The Components of Mean Square Error 236
10.4.1 Calculation of Mean Square Error 236
10.4.2 Decomposition of Expected Squared Errors 236
10.4.3 Decomposition of Expected Squared Errors for Independent Demand 238
10.4.4 Summary 239
10.5 Chapter Summary 240
Technical Notes 240
11 Classification Methods 243
11.1 Introduction 243
11.2 Classification Schemes 244
11.2.1 The Purpose of Classification 244
11.2.2 Classification Criteria 245
11.2.3 Summary 245
11.3 ABC Classification 246
11.3.1 Pareto Principle 246
11.3.2 Service Criticality 246
11.3.3 ABC Classification and Forecasting 247
11.3.4 Summary 248
11.4 Extensions to the ABC Classification 248
11.4.1 Composite Criterion Approach 249
11.4.2 Multi-criteria Approaches 250
11.4.3 Classification for Spare Parts 250
11.4.4 Summary 251
11.5 Conceptual Clarifications 251
11.5.1 Definition of Non-normal Demand Patterns 251
11.5.2 Conceptual Framework 252
11.5.3 Summary 253
11.6 Classification Based on Demand Sources 254
11.6.1 Demand Generation 254
11.6.2 A Qualitative Classification Approach 254
11.6.3 Summary 255
11.7 Forecasting-based Classifications 255
11.7.1 Forecasting and Generalisation 256
11.7.2 Classification Solutions 257
11.7.3 Summary 258
11.8 Chapter Summary 259
Technical Notes 260
12 Maintenance and Obsolescence 263
12.1 Introduction 263
12.2 Maintenance Contexts 264
12.2.1 Summary 265
12.3 Causal Forecasting 265
12.3.1 Causal Forecasting for Maintenance Management 266
12.3.2 Summary 268
12.4 Time Series Methods 268
12.4.1 Forecasting in the Presence of Obsolescence 269
12.4.2 Forecasting with Granular Maintenance Information 272
12.4.3 Summary 273
12.5 Forecasting in Context 273
12.6 Chapter Summary 275
Technical Notes 276
13 Non-parametric Methods 279
13.1 Introduction 279
13.2 Empirical Distribution Functions 280
13.2.1 Assumptions 281
13.2.2 Length of History 281
13.2.3 Summary 282
13.3 Non-overlapping and Overlapping Blocks 282
13.3.1 Differences Between the Two Methods 282
13.3.2 Methods and Assumptions 284
13.3.3 Practical Considerations 284
13.3.4 Performance of Non-overlapping Blocks Method 285
13.3.5 Performance of Overlapping Blocks Method 285
13.3.6 Summary 286
13.4 Comparison of Approaches 286
13.4.1 Time Series Characteristics Favouring Overlapping Blocks 286
13.4.2 Empirical Evidence on Overlapping Blocks 287
13.4.3 Summary 289
13.5 Resampling Methods 289
13.5.1 Simple Bootstrapping 289
13.5.2 Bootstrapping Demand Sizes and Intervals 290
13.5.3 VZ Bootstrap and the Syntetos-Boylan Approximation 292
13.5.4 Extension of Methods to Variable Lead Times 293
13.5.5 Resampling Immediately After Demand Occurrence 293
13.5.6 Summary 294
13.6 Limitations of Simple Bootstrapping 294
13.6.1 Autocorrelated Demand 294
13.6.2 Previously Unobserved Demand Values 295
13.6.3 Summary 296
13.7 Extensions to Simple Bootstrapping 296
13.7.1 Discrete-time Markov Chains 296
13.7.2 Extension to Simple Bootstrapping Using Markov Chains 297
13.7.3 Jittering 299
13.7.4 Limitations of Jittering 300
13.7.5 Further Developments 300
13.7.6 Empirical Evidence on Bootstrapping Methods 300
13.7.7 Summary 302
13.8 Chapter Summary 302
Technical Notes 303
14 Model-based Methods 305
14.1 Introduction 305
14.2 Models and Methods 305
14.2.1 A Simple Model for Single Exponential Smoothing 306
14.2.2 Critique ofWeighted Least Squares 307
14.2.3 ARIMA Models 307
14.2.4 The ARIMA(0,1,1) Model and SES 308
14.2.5 Summary 309
14.3 Integer Autoregressive Moving Average (INARMA) Models 309
14.3.1 Integer Autoregressive Model of Order One, INAR(1) 310
14.3.2 Integer Moving Average Model of Order One, INMA(1) 312
14.3.3 Mixed Integer Autoregressive Moving Average Models 312
14.3.4 Summary 313
14.4 INARMA Parameter Estimation 313
14.4.1 Parameter Estimation for INAR(1) Models 313
14.4.2 Parameter Estimation for INMA(1) Models 314
14.4.3 Parameter Estimation for INARMA(1,1) Models 314
14.4.4 Summary 315
14.5 Identification of INARMA Models 315
14.5.1 Identification Using Akaike's Information Criterion 315
14.5.2 General Models and Model Identification 316
14.5.3 Summary 317
14.6 Forecasting Using INARMA Models 317
14.6.1 Forecasting INAR(1) Mean Demand 318
14.6.2 Forecasting INMA(1) Mean Demand 318
14.6.3 Forecasting INARMA(1,1) Mean Demand 319
14.6.4 Forecasting Using Temporal Aggregation 319
14.6.5 Summary 319
14.7 Predicting the Whole Demand Distribution 319
14.7.1 Protection Interval of One Period 320
14.7.2 Protection Interval of More Than One Period 320
14.7.3 Summary 322
14.8 State Space Models for Intermittence 322
14.8.1 Croston's Demand Model 323
14.8.2 Proposed State Space Models 324
14.8.3 Summary 325
14.9 Chapter Summary 325
Technical Notes 325
15 Software for Intermittent Demand 329
15.1 Introduction 329
15.2 Taxonomy of Software 330
15.2.1 Proprietary Software 330
15.2.2 Open Source Software 332
15.2.3 Hybrid Solutions 333
15.2.4 Summary 333
15.3 Framework for Software Evaluation 333
15.3.1 Key Aspects of Software Evaluation 334
15.3.2 Additional Criteria 335
15.3.3 Summary 336
15.4 Software Features and Their Availability 336
15.4.1 Software Features for Intermittent Demand 336
15.4.2 Availability of Software Features 337
15.4.3 Summary 338
15.5 Training 339
15.5.1 Summary 340
15.6 Forecast Support Systems 340
15.6.1 Summary 341
15.7 Alternative Perspectives 341
15.7.1 Bayesian Methods 342
15.7.2 Neural Networks 342
15.7.3 Summary 343
15.8 Way Forward 343
15.9 Chapter Summary 345
Technical Note 345
References 347
Author Index 365
Subject Index 367
1
Economic and Environmental Context
1.1 Introduction
Demand forecasting is the basis for most planning and control activities in any organisation. Unless a forecast of future demand is available, organisations cannot commit to staffing levels, production schedules, inventory replenishment orders, or transportation arrangements. It is demand forecasting that sets the entire supply chain in motion.
Demand will typically be accumulated in some pre-defined 'time buckets' (periods), such as a day, a week, or a month. The determination of the length of the time period that constitutes a time bucket is a very important decision. It is a choice that should relate to the nature of the industry and the volume of the demand itself but it may also be dictated by the IT infrastructure or software solutions in place. Regardless of the length of the time buckets, demand records eventually form a time series, which is a sequence of successive demand observations over time periods of equal length.
On many occasions, demand may be observed in every time period, resulting in what is sometimes referred to as 'non-intermittent demand'. Alternatively, demand may appear sporadically, with no demand at all in some periods, leading to an intermittent appearance of demand occurrences. Should that be the case, contribution to revenues is naturally lower than that of faster-moving demand items. Intermittent demand items do not attract much marketing attention, as they will rarely be the focus of a promotion, for example. However, they have significant cost implications for a simple reason: there are often many of them!
Service or spare parts are very frequently characterised by intermittent demand patterns. These items are essentially components or (sub-) assemblies contributing to the build-up of a final product. However, they face 'independent demand', which is demand generated directly from customers, rather than production requirements for a particular number of units of the final product. In the after-sales environment (or 'aftermarket'), we deal exclusively with 'independent demand' items. Service parts facing intermittent demand may represent a large proportion of an organisation's inventory investment. In some industries, this proportion may be as high as 60% or 70% (Syntetos 2011). The management of these items is a very important task which, when supported by intelligent inventory control mechanisms, may yield dramatic cost reductions.
Industries that rely heavily on after-sales support, including the automotive, IT, and electronics sectors, are dominated by intermittent demand items. The contributions of the after-sales services to the total revenues of organisations in these industries have been reported to be as high as 60% (Johnston et al. 2003). This signifies an opportunity not only to reduce costs but also to increase revenues through a careful balancing of keeping enough in stock to satisfy customers but not so much as to unnecessarily increase inventory investments. There are tremendous economic benefits that may be realised through the reappraisal of managing intermittent demand items.
There are also significant environmental benefits to be realised by such a reappraisal. Because of their inherent slow movement, intermittent demand items are at the greatest risk of obsolescence. The problem is exacerbated by the greatly reduced product life cycles in modern industry. This affects the planning process for all intermittent demand items (both final products and spare parts used to sustain the operation of final products). Better forecasting and inventory decisions may reduce overall scrap and waste. Furthermore, the sustained provision of spare parts may also reduce premature replacement of the original equipment.
The area of intermittent demand forecasting has been neglected by researchers and practitioners for too long. From a business perspective, this may be explained in terms of the lack of focus on intermittent demand items by the marketing function of organisations. However, the tough economic conditions experienced from around 2010 onwards have resulted in a switch of emphasis from revenue maximisation to cost minimisation. This switch repositions intermittent demand items as the focus of attention in many companies, as part of the drive to dramatically cut down costs and remain competitive. In addition, the more recent emergence of the after-sales business as a major determinant of companies' success has also led to the recognition of intermittent demand forecasting as an area of exceptional importance.
Following a seminal contribution in this area by John Croston in 1972, intermittent demand forecasting received very little attention by researchers over the next 20 years. This was in contrast to the extensive research conducted on forecasting faster-moving demand items. Research activity grew rapidly from the mid-1990s onwards, and we have now reached a stage where a comprehensive body of knowledge, both theoretical and empirical, has been developed in this area. This book aims to provide practitioners, students, and academic researchers with a single point of reference on intermittent demand forecasting. Although there are considerable openings for further advancements, the current state of knowledge offers organisations significant opportunities to improve their intermittent demand forecasting. Numerous reports, to be discussed in more detail later in this chapter, indicate that intermittent demand forecasting is one of the major problems facing modern organisations. Specialised software packages offer some forecasting support to companies but they often lag behind new developments. There are great benefits that have not yet been achieved in this area, and we hope that this book will make a contribution towards their realisation.
There are three main audiences for this book:
- Supply chain management (SCM) practitioners, broadly defined, who wish to realise the full benefits of managing intermittent demand items.
- Software designers wanting to incorporate new developments in forecasting into their software.
- Students and academics wishing to learn and incorporate into their curricula, respectively, the state of the art in intermittent demand forecasting.
In summary, business pressures to reduce costs and environmental pressures to reduce scrap (often introduced in the form of prescribed policies imposed by national governments or the EU for example) render intermittent demand items, and forecasting their requirements, one of the most important areas in modern organisations.
There are great benefits associated with forecasting intermittent demand more accurately, and those benefits are far from being realised. This may be explained by the well reported innovation-adoption gap, which arises from the divergence between innovations and real-world practices. Organisational practices typically lag behind software developments, and software developments typically lag behind the state of the art in the academic literature. It is the aim of this book to bridge these gaps and show how intelligent intermittent demand forecasting may result in significant economic and environmental benefits.
In the remainder of this chapter, we first discuss in more detail the potential benefits that may be realised through improved intermittent demand forecasting. We then provide an overview of the current state of supply chain software packages and enterprise resource planning (ERP) solutions with regard to intermittent demand forecasting. This is followed by a section where we elaborate on both the structure of this book and the perspective that we take regarding the material presented here. We close with a summary of the chapter.
1.2 Economic and Environmental Benefits
Intermittent demand for products appears sporadically, with some time periods showing no demand at all. Moreover, when demand occurs, the demand size may be constant or variable, perhaps highly so, leading to what is often termed 'lumpy demand'. Later in this chapter, we discuss why forecasting sporadic and lumpy demand patterns is a very difficult task. Specific characterisations of intermittent demand series are considered in detail in Chapters 4 and 5.
1.2.1 After-sales Industry
Intermittent demand items dominate service and repair parts inventories in many industries (Boylan and Syntetos 2010). A survey by Deloitte Research (2006) benchmarked the service businesses of many of the world's largest manufacturing companies with combined revenues reaching more than $1.5 trillion; service operations accounted for an average of 25% of revenues. In addition to their contribution to revenues, these items present a distinct opportunity for cost reductions. Maintenance, repair, and operations (MRO) inventories typically account for as much as 40% of the annual procurement budget (Donnelly 2013). Increased revenues and reduced costs naturally lead to increased profits. Many organisations have repeatedly testified to the importance of after-sales services for their businesses and the profits they generate. Companies such as Beretta, Canon, DAF Trucks, Electrolux, EPTA, GE Oil & Gas, and Lavapiu have reported contributions of the after-sales services to their total profit of up to 50% (Syntetos 2011). Comparable numbers have been reported by Gaiardelli et al....
