An examination of the size of orders from customers, their characterisation and the implications for inventory control of slow moving items

Abstract

This paper examines half a million observations of the size of orders from customers at an electrical wholesaler. It notes: the distribution of the size of customer orders for a single item (stock keeping unit or SKU) is very skewed and resembles a geometric distribution; while the average size of an order is different for different items, for one SKU the mean order size is effectively the same at different branches even when the branches have very different demand rates; across a range of SKUs there is a strong relationship linking the mean and the variance of order size. The general results above are shown to apply to even the slowest movers. This extension is important because for items with intermittent demand the size of customer orders is required to produce an unbiased estimate of demand. Also a knowledge of the distribution of demand is important for setting maximum and minimum stock levels and the scheme employed is described.

Emergency orders in the periodic-review inventory system with fixed ordering costs and compound Poisson demand

“…In order to simplify the exposition and the presentation of our numerical examples with compound Poisson demand we assume that customer demands follow a geometric distribution. This compounding distribution has also been observed in practice (Johnston et al, 2003). Mohebbi and Posner (1999) also treat an inventory system with emergency orders under compound Poisson demand but with lost sales.…”

Forecasting of compound Erlang demand

“…When demand occurs, the demand size may be constant or variable, perhaps highly so (Johnston et al 2003) leading to what is often termed as lumpy demand.…”

“…Many distributions have been suggested to model the sizes of demand when demand occurs (Axsäter 2006), such as the geometric (Watson 1987, Johnston et al 2003, Chew and Johnson 2006, logarithmic (Syntetos and Boylan 2006), lognormal (Syntetos et al 2009a), etc. However, and as discussed in the next section, modeling for forecasting purposes is insensitive to such a distributional assumption.…”

Effects of Slow Moving Inventory in Industries: Insights of Other Researchers

“…It is observed that chance of more than one demand in a lead time is small for slow moving items. [16].Tavares and Almeida developed a model for slow moving items to decide whether it is economical to have 0 or 1 item in stock and the model was applied to real situation resulting in substantial savings [17]. Nenes et al developed a Decision support system (DSS) to manage inventories in a small company located in Greece.…”