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(Ebook PDF) Intermittent Demand Forecasting: Context, Methods and Applications 1st Edition by Aris Syntetos, John Boylan isbn 9781119135302 1119135303 full chapters
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Intermittent Demand Forecasting: Context, Methods and Applications 1st Edition Aris A. Syntetos Digital Instant Download
ISBN(s): 9781119976080, 1119976081
Edition: 1
File Details: PDF, 4.62 MB
Year: 2022
Language: English
(Ebook PDF) Intermittent Demand Forecasting: Context, Methods and Applications 1st Edition by Aris Syntetos, John Boylan -Ebook PDF Instant Download/Delivery:9781119135302, 1119135303
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ISBN 10:1119135303
ISBN 13:9781119135302
Author: John E. Boylan; Aris A. Syntetos
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.
Table of Contents:
- 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
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Aris Syntetos, John Boylan,Intermittent,Forecasting,Context,Methods,Applications
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