John Wiley & Sons Intermittent Demand Forecasting - Context, methods and applications Cover Intermittent Demand Forecasting - Context, methods and applications covers the entire breadth of wor.. Product #: 978-1-119-97608-0 Regular price: $69.07 $69.07 Auf Lager

Intermittent Demand Forecasting - Context, methods and applications

Boylan, John / Syntetos, Aris

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1. Auflage Juni 2021
400 Seiten, Hardcover
Wiley & Sons Ltd

ISBN: 978-1-119-97608-0
John Wiley & Sons

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Intermittent Demand Forecasting - Context, methods and applications covers the entire breadth of work in intermittent demand forecasting and the very latest research findings in the following topics:

* time series (parametric) methods
* bootstrapping, both parametric and non-parametric
* causal models
* neural networks

In order to enable a full understanding of how these methods may be implemented and how they may complement existing operational functions, other chapters include:

* demand classification (what exactly constitutes intermittence and how it may be distinguished from other patterns in operational terms)
* measures for assessing intermittent demand forecasting accuracy
* interactions between forecasting and stock control
* qualitative considerations (soft managerial issues that should be taken into account)

The methods and approaches used in forecasting intermittent demands is relevant to many industries such as the military, aerospace, automotive, and information technology.

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 259

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

Index 365
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