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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.