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,
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 stockouts.
An alternative calculation, which attempts to address this issue, is to multiply the forecasted demand per week by a period that is longer than the protection interval, to allow for demand uncertainty. For example, we could multiply the forecasted demand, of two per week, by four weeks, instead of three, to give an OUT level of eight units. The problem now is that the setting of four weeks is arbitrary. Why not use five or six weeks instead of four? There is no guarantee of hitting service level targets using this type of calculation. For highly unpredictable demand, we may set the OUT level too low and, for more predictable demand, we may set it too high. Therefore, although this rule of thumb has the merit of simplicity, it risks service level targets being missed or targets being achieved with excessive stocks.
3.2.2 Judgemental Adjustment of Orders
Some demand planners exercise judgement by adjusting stock orders directly, whilst others do so by adjusting demand forecasts. Judgemental ordering has been the subject of some research, mainly through studies of behaviour in simulated environments. An early study of a simulated inventory distribution system (known as the ‘beer game’) showed that participants in the game often misperceived the feedback they received in multiple‐stage supply chains. This leads to ordering decisions that can be far from optimal (Sterman 1989) and can cause amplification of order variability throughout the supply chain, known as the ‘bullwhip effect’. In a different setting, based on a single‐stage supply chain, Tokar et al. (2014) found that participants tended to over‐order in anticipation of a spike of demand, relative to the optimal orders that would minimise the total inventory costs.
Although the experimental evidence on judgemental ordering is not encouraging, there are situations when it can be beneficial. A prime example is to take advantage of discounts from suppliers that are available only for a limited period of time. In making a judgemental adjustment to the order, the demand forecast should be left untouched if no change in demand from end‐customers is anticipated. In other circumstances, it may be appropriate to adjust the forecasts themselves. We shall return to forecast adjustments in Chapter 10, where there is a detailed discussion of the subject.
To summarise, judgemental adjustment of orders may have positive effects in gaining price discounts but can also contribute to the bullwhip effect, leading to additional inventory holdings. The effect of order adjustment may not be as great as the impact of forecast adjustment, as shown by a system dynamics investigation (Syntetos et al. 2011b) and a further empirical study (Syntetos et al. 2016b). Nevertheless, any price benefits should be weighed against increased inventory costs. If order adjustments are frequent, it would be good practice to keep records of the original recommended orders, as well as the adjusted orders. In this way, an organisation can monitor the effect of judgemental adjustments and evaluate whether they are beneficial or, more specifically, the circumstances under which they are beneficial.
3.2.3 Summary
Order‐up‐to (OUT) levels are sometimes set by using a rule of thumb of multiplying the forecasted demand (per period) by a set number of periods. This ignores the degree of demand uncertainty, leading to a misdirection of inventory investment. Consequently, rules of thumb like this are not recommended.
Judgemental adjustment of orders may be worthwhile in some situations. These adjustments should be continually monitored, to evaluate their effects on price benefits and inventory costs.
3.3 Aggregate Financial and Service Targets
Organisations
