There have been some very promising advances in the area of intermittent demand forecasting, some of which have found their way into software applications. However, much still remains to be done in terms of software companies keeping up with important methods that have recently been developed and particularly those that have been empirically tested and shown to yield considerable benefits.
1.4 About this Book
In this section we briefly review the stance taken, the scope of discussion, and the structure of the book.
1.4.1 Optimality and Robustness
Intermittent demand patterns are very difficult to model and forecast. It is the genuine lack of sufficient information associated with these items (due to the presence of zero demands) that may preclude the identification of series' components such as trend and seasonality. Demand histories are also very often limited, which makes things even worse. Demand arrives sporadically and, when it does so, it may be of a quantity that is difficult to predict. The actual demand sizes (positive demands) may sometimes be almost constant or consistently small in magnitude. Alternatively, they may be highly variable, leading to ‘erratic’ demand. Intermittence coupled with erraticness leads to what is known as ‘lumpy’ demand. The graph in Figure 1.1 shows examples of intermittent and lumpy demand patterns, based on annual demand history for two service parts used in the aerospace industry.
From Figure 1.1, two things become apparent: (i) the annual demand history contains only five positive demand observations and (ii) variability refers to both the demand arrivals (how often demand arrives) and the size of the demand, when demand occurs. The lack of information associated with intermittent demand patterns coupled with this dual source of variability calls for simplifying assumptions when modelling these patterns. A common simplifying assumption is that the demand is non‐seasonal. Such simplifications may impede the development of solutions that are optimal in a statistical sense, but do allow for the development of methods that potentially are very robust and easy to implement. Robustness is defined here as a ‘sufficiently good’ performance across a wide range of possible conditions. Optimality is defined, for particular conditions, as the ‘best’ performance.
Figure 1.1 Intermittent and lumpy demand.
Source: Boylan and Syntetos (2008). ©2008, Springer Nature.
We shall return to robustness and statistical optimality in later chapters but, for the time being, it is sufficient to say that robustness is essential in practical applications. While optimality is desirable, it should not be at the expense of robustness. Many of the methods to be discussed in this book have been found to be robust by such software companies as Blue Yonder, LLamasoft, Slimstock, and Syncron International, helping their customers to dramatically reduce inventory costs.
1.4.2 Business Context
With robustness in mind, this book presents a range of approaches to intermittent demand forecasting that are applicable in any industrial make to stock (MTS) setting. In addition to an MTS setting, unless otherwise specified, we focus on single stock keeping unit (SKU), single stocking location environments, as explained below.
Make to stock. In an MTS environment, customers are willing to wait no more than the time it takes to deliver the particular item to them and so the item needs to be available in stock, ready to be dispatched, or, in the case of retailing, it needs to be available on the shelf. In this case, demand is not known and needs to be predicted. The alternative environment is known as make to order (MTO), where the products are not assumed to be in stock, and the customer must wait until the manufacturer assembles the product for them. In this case, customer demand is known and does not need to be predicted. This situation is common for some products (e.g. furniture) but not for others (e.g. automotive or aerospace spare parts). There is also a move to 3D printing of products in some industries, which is a form of MTO but with shorter delays (Technical Note 1.1).
Single stock keeping unit (SKU) approaches. We are looking at forecasting the requirements (and managing the inventories) of single SKUs. Although some of the methods to be discussed in this book rely upon collective considerations (across a group of SKUs), the rest of the material considers single SKU problems. This is because higher levels of aggregation are, typically, not associated with intermittent demand. Consider, for example, 10 intermittent demand items, all of which are replenished from the same supplier. It makes sense to consider the aggregate demand of those items to facilitate efficient transportation arrangements. However, although demand at the individual SKU level may be intermittent, aggregate demand (across all 10 SKUs), most probably, will not be intermittent.
Single stocking location approaches. We focus on determining inventory replenishment requirements at each single location, without taking into account interactions between locations. As such, we do not consider the possibility of satisfying demand by lateral transshipments of stocks between stores. This is because these decisions relate explicitly to joint inventory‐transportation optimisation, which is beyond the scope of this book. Further, and as discussed above, aggregate demand (across different locations in this case) is typically not associated with intermittence.
We should also mention that, although the term ‘demand’ is being used in this book when referring to forecasting, demand will not always be known and, in this case, actual sales must be used as a proxy. The terms ‘demand’ and ‘sales’ are used interchangeably in this book although, strictly speaking, the latter is often used as an approximation for the former.
1.4.3 Structure of the Book
This book starts by contextualising intermittent demand forecasting in the wider scholarship and practice of inventory management. We begin in Chapter 2 with a discussion of inventory management and some of its implications for forecasting. Then, in Chapter 3, we examine the service drivers of inventory performance. The focus shifts in Chapters 4 and 5 to the characterisation of intermittent demand patterns by demand distributions. This forms a natural foundation for the next two chapters, which focus on forecasting methods. Chapter 8 takes us back to inventory replenishment and the linkage between forecasting and inventory control. In the next chapter, we move on to the measurement of forecasting accuracy and inventory performance. Forecasting accuracy assessment is a notoriously difficult problem for intermittent series, and the chapter highlights the traps for the unwary and gives some pointers to good practice.
Although the main emphasis of this book is on forecasting, classification methods are also important in practical applications. In Chapter 10, we lay some of the groundwork for classification methods, discussed in Chapter 11, which have been designed specifically to address intermittence. In the next chapter, we turn our attention to obsolescence and forecasting methods that are
