Intermittent Demand Forecasting. John E. Boylan

Intermittent Demand Forecasting

(see Chapter 4). However, for renewal demand processes, including Erlang arrival processes that are not memoryless, this is no longer true in general (see Chapter 5). Rather, for these processes the passage of time itself may carry information about the demand process. Thus, it may be optimal that a certain time span should trigger a replenishment order, even if a demand has not occurred. Therefore, an order may not only be triggered by a change in the inventory position (defined in the usual way). Heuristically, and for practical purposes, replenishment orders may, of course, be allowed only at the time instances just after a demand has occurred (or at predetermined time intervals, as in a periodic review system). This issue has implications for the kind of information that is useful for inventory control purposes but is not discussed further in this book.

Note 2.5 Optimisation of (R,S) and (s,Q) Systems

For optimisation of control parameters, the results obtained for the left-parenthesis upper R comma upper S right-parenthesis system can be easily transferred to an left-parenthesis s comma upper Q right-parenthesis system by substituting for upper S , upper L for upper L plus upper R , and upper Q for upper D slash upper R (where upper L is the lead time and upper D the annual demand). The left-parenthesis upper R comma upper Q right-parenthesis combination does not take into account the variability of demand and hence should not be applied in a probabilistic demand context.

3 Service Level Measures

3.1 Introduction

In Chapter 2, we reviewed inventory rules that may be used to manage the stock of intermittent demand items, paying particular attention to the left-parenthesis upper R comma s comma upper S right-parenthesis and policies. In both of these policies, the inventory position is reviewed every upper R periods, and enough stock is ordered to raise it to the order‐up‐to level, upper S , also known as the OUT level. We noted that is often used for intermittent demand items because of its simplicity and robustness.

The left-parenthesis upper R comma upper S right-parenthesis policy requires the determination of the review interval and OUT level for each individual stock keeping unit (SKU). In practice, the review interval is usually set to be the same for all SKUs or for whole classes of SKUs, for reasons that were discussed in Chapter 2. The setting of the review interval varies according to industry sector. In grocery retail, this may be every day or half day, whereas in automotive spare parts, the review may be weekly or monthly.

The OUT level, upper S , should be set separately for each SKU, to take account of its demand uncertainty. The determination of an OUT level for an individual SKU is an important issue for ‘mission‐critical’ items, for example spare parts without which a grounded plane cannot fly. For other SKUs, the determination of OUT levels may be less critical but is still important because of its effect on aggregate inventories. As discussed in Chapter 1, a whole range of SKUs may account for significant stock holding, the level of which is influenced by the OUT levels.

The setting of the OUT level in service‐driven inventory systems depends on three main factors:

1 Service measure.

2 Demand distribution.

3 Forecasting method.

The first factor, the service measure, is analysed in this chapter. Chapters 4 and 5 focus on the second factor, with discussion of various demand distributions and the criteria they should satisfy. The following two chapters are concerned with the third factor: Chapter 6 concentrates on methods to forecast the mean demand, while Chapter 7 is devoted to forecasting the variance of demand and its associated forecast error. All of these elements are brought together in Chapter 8, which explains how, for a given service measure, the OUT level can be found for intermittent demand items.

In this chapter, we begin by arguing against using rules of thumb for setting OUT levels, and by stressing the strategic significance of aggregate level financial and service targets. The choice of SKU‐level service measures is examined, noting their links to inventory costs, before moving on to the calculation of the two operational service level measures that are most commonly employed in inventory systems. Then, we return to the setting of aggregate service targets, emphasising the importance of ‘what‐if’ modelling capabilities. The chapter concludes with comments on the use of judgement and points to the need for reliable demand distributions to assess the service implications of different ordering policies.

3.2 Judgemental Ordering

In this book, we argue for a systematic and analytical approach to forecasting and inventory management. This should be based on inventory replenishment rules and forecasting methods that are well grounded statistically and have solid evidence of good performance in practice. From our work with a variety of organisations, we are aware that practitioners may use ordering rules that are ad hoc, or may adjust computer‐generated orders using their own judgement. In this section, we make some brief remarks on these practices.

3.2.1 Rules of Thumb for the Order‐Up‐To Level

Suppose that the review interval is one week and the lead time is two weeks, giving a total protection interval of three weeks. Suppose, further, that our forecasted mean demand is two units per week, and the demand is non‐trended and non‐seasonal. It may be tempting to set the OUT level as the forecasted mean demand over the protection interval, namely six units. This would be correct if it were certain that demand would be for the exact mean demand predicted, but this is rarely the case. More commonly, the demand will be fluctuating. Not taking account of these fluctuations can lead to frequent

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Intermittent Demand Forecasting. John E. Boylan