for inventories should be assessed. We also look at how stock keeping units should be classified for forecasting purposes, and examine methods designed specifically to address maintenance and obsolescence. The next two chapters deal with methods that can tackle more challenging demand patterns. We conclude with a review of forecasting software requirements and our views on the way forward.
We are grateful to those pioneers who inspired us to study this subject, and who have given us valuable advice over the years, especially John Croston, Roy Johnston, and Tom Willemain. We would like to express our thanks to those who commented on draft chapters of this book: Zied Babai, Stephen Disney, Robert Fildes, Thanos Goltsos, Matteo Kalchschmidt, Stephan Kolassa, Nikos Kourentzes, Mona Mohammadipour, Erica Pastore, Fotios Petropoulos, Dennis Prak, Anna‐Lena Sachs, and Ivan Svetunkov; and to Nicole Ayiomamitou and Antonis Siakallis who helped with the figures.
Lancaster and Cardiff
January 2021
John E. Boylan
Aris A. Syntetos
Glossary
ADIDAaggregate–disaggregate intermittent demand approachAICAkaike information criterionARautoregressiveARIMAautoregressive integrated moving averageARMAautoregressive moving averageAPEabsolute percentage errorBObackorderBoMbill of materialsBSBrier scoreCDFcumulative distribution functionCFEcumulative forecast errorCSLcycle service level (all replenishment cycles)
About the Companion Website
This book is accompanied by a companion website.
www.wiley.com/go/boylansyntetos/intermittentdemandforecasting
This website includes:
Datasets (with accompanying information)
Links to R packages
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
