cycle. Chapter 13 presents an alternative perspective on demand forecasting, concentrating on methods that do not assume any particular form of demand distribution. By contrast, Chapter 14 delves more deeply into methods that are based on demand distributions. The book closes with Chapter 15, which contains a discussion of software solutions for intermittent demand forecasting.
1.4.4 Current and Future Applications
Recent IT developments have greatly expanded the areas of application of intelligent intermittent demand forecasting methods. Data at a very low level of granularity have become available, which means that environments where traditionally intermittence would not be a problem now become natural candidates for further consideration. Take the retailing sector as an example: this is a traditionally fast demand environment where even the slower moving items sell in considerable volumes every day, making intermittent demand forecasting redundant. However, the current availability and utilisation of data for replenishment purposes, as often as three times per day, means that more items have intermittent demand. Although daily demand may not be intermittent, half‐daily demand could be, and demand over a third of a day most probably will be.
Another factor in retail, highlighted by Boylan (2018), is the broadening of product ranges in larger retail outlets, with grocery stores introducing more clothing lines, for example. These items will often be slower moving than staple food ranges, thereby increasing the proportion of intermittent items. Recent discussions with major supermarkets in the United Kingdom such as Sainsbury's and Tesco indicate that intermittent demand forecasting has become one of their major problems.
Intermittent series occur in many other settings. For example, the planning of inventories for emergency relief must address highly intermittent and lumpy demand. Indeed, the benefits of good forecasting and planning (for any type of series) apply just as much to charitable and not‐for‐profit organisations as they do for profit‐making wholesalers and retailers. Support for the promotion and realisation of these wider benefits is being offered at the time of writing by the ‘Forecasting for Social Good’ (www.f4sg.org) and ‘Democratising Forecasting’ initiatives launched in 2018 by Dr Bahman Rostami‐Tabar.
Nikolopoulos (2021) made a strong case for the use of intermittent forecasting methods for series that are not intermittent but have sporadic peaks. These time series can be decomposed into two subseries: a baseline series and one containing more extreme values. Standard time series or causal methods can be used for the former. The latter include rare but expected events (‘grey swans’) and truly unexpected special events (‘black swans’) (Taleb 2007) and can be addressed using intermittent forecasting methods, at least as a benchmark against which other methods may be compared. Methods to address intermittence have their origins in inventory planning, but Nikolopoulos (2021) argued that these forecasting methods can be used more widely in business, finance, and economics or, indeed, in any other discipline. This line of enquiry will not be pursued in this book, although it seems a very promising direction for future research.
1.4.5 Summary
The material presented in this book reflects the authors' emphasis on robust solutions that may perform well under a wide range of differing conditions. We present the state of the art in intermittent demand forecasting, paying particular attention to the interface between forecasting and stock control. There is a very considerable market for the application of those methods, and this can only expand as more highly granular data become available.
1.5 Chapter Summary
The associated cost of inventories of purchased goods has been estimated to be between 25% and 35% of the value of those goods (e.g. Chase et al. 2006): a firm carrying $20 million in purchased goods inventory would, accordingly, incur additional costs of $5–7 million. These are costs that, once reduced, can significantly improve the firm's net profits (Wallin et al. 2006). The total cost of purchased goods inventory can be quite alarming, calling for innovative approaches to cut it down. Intelligent intermittent demand forecasting offers such opportunities.
In the defence environment, Henry L. Hinton Jr, Assistant Comptroller General, National Security and International Affairs Division, stated, ‘Our work continues to show weaknesses in DoD's inventory management practices that are detrimental to the economy’ (GAO 1999, p. 1). Sixteen years later, only minor improvements were reported (GAO 2015), and public announcements on the poor management of defence inventories and the resulting detrimental impacts on the economy constitute a recurring issue in the news. Similarly, the expansion of the after‐sales industry and the increasing importance of commercial service operations have not been adequately reflected in the development of ERP and supply chain software packages, the functionality of which has often been judged to be inadequate (Syntetos et al. 2009b). In addition to the after‐sales and MRO environment and the military sector, intermittent demand items dominate the inventories in a wide range of industries, calling for improved solutions for their cost‐effective management.
There have been rather minor improvements in practical applications in this area since around 2000, but there have been major improvements in empirically tested theory since that time. Many of these theoretical advancements have not yet been incorporated in commercial software, and hence, there are major opportunities for improving real‐world applications. We hope this book will help towards moving in that direction.
Technical Note
Note 1.1 3D Printing
An alternative form of MTO, enabled by advances in additive manufacturing, is 3D Printing (3DP) an item on demand. In this case, decision‐making relates to the level of investment in 3DP machinery/technology that may ‘print’ the requested number of items upon demand. Potentially, this is very appealing in the case of spare parts and is currently being explored by various organisations seeking to cut down their inventories. For example, in 2018 the Dutch Army initiated a collaboration with the 3DP company DiManEx to examine spare part supply challenges. At the time of writing, there is no empirical evidence on the effect of 3DP on spare parts inventory management and how this compares with MTS approaches.
2 Inventory Management and Forecasting
2.1 Introduction
In the previous chapter, we showed that the management of intermittent demand items is an important task for many organisations. These items require certain operational decisions to be taken at the level of an individual stock keeping unit (SKU) including whether to stock the item at all and, if so, how much to stock. Both of these decisions will be addressed in this chapter. We discuss the major inventory replenishment policies and their appropriateness for intermittent demand items. This is the foundation for inventory forecasting and is an essential component of all software dedicated to inventory management. We also consider what forecasts are important when demand is intermittent. The interface between forecasting and stock control has been rather neglected in the academic literature although it is vital in real‐world inventory applications.
2.2 Scheduling and Forecasting
Before discussing the integration of forecasting and inventory control for intermittent demand items, a distinction needs to be made between the inventory management practices required for dependent and independent demand items.
