Intermittent Demand Forecasting: Context, Methods and Applications
- Length: 400 pages
- Edition: 1
- Language: English
- Publisher: Wiley
- Publication Date: 2022-08-22
- ISBN-10: 1119976081
- ISBN-13: 9781119976080
- Sales Rank: #0 (See Top 100 Books)
INTERMITTENT DEMAND FORECASTING
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.
Cover Table of Contents Title Page Copyright Dedication Preface Glossary About the Companion Website 1 Economic and Environmental Context 1.1 Introduction 1.2 Economic and Environmental Benefits 1.3 Intermittent Demand Forecasting Software 1.4 About this Book 1.5 Chapter Summary Technical Note 2 Inventory Management and Forecasting 2.1 Introduction 2.2 Scheduling and Forecasting 2.3 Should an Item Be Stocked at All? 2.4 Inventory Control Requirements 2.5 Overview of Stock Rules 2.6 Chapter Summary Technical Notes 3 Service Level Measures 3.1 Introduction 3.2 Judgemental Ordering 3.3 Aggregate Financial and Service Targets 3.4 Service Measures at SKU Level 3.5 Calculating Cycle Service Levels 3.6 Calculating Fill Rates 3.7 Setting Service Level Targets 3.8 Chapter Summary Technical Note 4 Demand Distributions 4.1 Introduction 4.2 Estimation of Demand Distributions 4.3 Criteria for Demand Distributions 4.4 Poisson Distribution 4.5 Poisson Demand Distribution 4.6 Incidence and Occurrence 4.7 Poisson Demand Incidence Distribution 4.8 Bernoulli Demand Occurrence Distribution 4.9 Chapter Summary Technical Notes 5 Compound Demand Distributions 5.1 Introduction 5.2 Compound Poisson Distributions 5.3 Stuttering Poisson Distribution 5.4 Negative Binomial Distribution 5.5 Compound Bernoulli Distributions 5.6 Compound Erlang Distributions 5.7 Differing Time Units 5.8 Chapter Summary Technical Notes 6 Forecasting Mean Demand 6.1 Introduction 6.2 Demand Assumptions 6.3 Single Exponential Smoothing (SES) 6.4 Croston's Critique of SES 6.5 Croston's Method 6.6 Critique of Croston's Method 6.7 Syntetos–Boylan Approximation 6.8 Aggregation for Intermittent Demand 6.9 Empirical Studies 6.10 Chapter Summary Technical Notes 7 Forecasting the Variance of Demand and Forecast Error 7.1 Introduction 7.2 Mean Known, Variance Unknown 7.3 Mean Unknown, Variance Unknown 7.4 Lead Time Variability 7.5 Chapter Summary Technical Notes 8 Inventory Settings 8.1 Introduction 8.2 Normal Demand 8.3 Poisson Demand 8.4 Compound Poisson Demand 8.5 Variable Lead Times 8.6 Chapter Summary Technical Notes 9 Accuracy and Its Implications 9.1 Introduction 9.2 Forecast Evaluation 9.3 Error Measures in Common Usage 9.4 Criteria for Error Measures 9.5 Mean Absolute Percentage Error and its Variants 9.6 Measures Based on the Mean Absolute Error 9.7 Measures Based on the Mean Error 9.8 Measures Based on the Mean Square Error 9.9 Accuracy of Predictive Distributions 9.10 Accuracy Implication Measures 9.11 Chapter Summary Technical Notes 10 Judgement, Bias, and Mean Square Error 10.1 Introduction 10.2 Judgemental Forecasting 10.3 Forecast Bias 10.4 The Components of Mean Square Error 10.5 Chapter Summary Technical Notes 11 Classification Methods 11.1 Introduction 11.2 Classification Schemes 11.3 ABC Classification 11.4 Extensions to the ABC Classification 11.5 Conceptual Clarifications 11.6 Classification Based on Demand Sources 11.7 Forecasting‐based Classifications 11.8 Chapter Summary Technical Notes 12 Maintenance and Obsolescence 12.1 Introduction 12.2 Maintenance Contexts 12.3 Causal Forecasting 12.4 Time Series Methods 12.5 Forecasting in Context 12.6 Chapter Summary Technical Notes 13 Non‐parametric Methods 13.1 Introduction 13.2 Empirical Distribution Functions 13.3 Non‐overlapping and Overlapping Blocks 13.4 Comparison of Approaches 13.5 Resampling Methods 13.6 Limitations of Simple Bootstrapping 13.7 Extensions to Simple Bootstrapping 13.8 Chapter Summary Technical Notes 14 Model‐based Methods 14.1 Introduction 14.2 Models and Methods 14.3 Integer Autoregressive Moving Average (INARMA) Models 14.4 INARMA Parameter Estimation 14.5 Identification of INARMA Models 14.6 Forecasting Using INARMA Models 14.7 Predicting the Whole Demand Distribution 14.8 State Space Models for Intermittence 14.9 Chapter Summary Technical Notes 15 Software for Intermittent Demand 15.1 Introduction 15.2 Taxonomy of Software 15.3 Framework for Software Evaluation 15.4 Software Features and Their Availability 15.5 Training 15.6 Forecast Support Systems 15.7 Alternative Perspectives 15.8 Way Forward 15.9 Chapter Summary Technical Note ReferencesReferences Author Index Subject Index End User License Agreement
Donate to keep this site alive
1. Disable the AdBlock plugin. Otherwise, you may not get any links.
2. Solve the CAPTCHA.
3. Click download link.
4. Lead to download server to download.