Planning Multiple Location Inventories

Abstract

Distribution networks frequently contain multiple locations where product is held as inventory. These may be plants, warehouses, and retail outlets. Traditionally, inventory levels at these locations have been determined by optimizing the cost, demand, and customer service factors local to the particular inventory. With improvements in business information systems, it has become increasingly popular to treat multiple inventory locations as virtual inventories. That is, when customer demand cannot be served from the primary assigned location, other comparable inventory locations, usually more distant, may be used as backup stocking points for serving customer demand. Although delivery cost may be increased, customers actually receive fill rates approaching 100% even when the product in-stock probabilities for the individual stocking points are somewhat lower. While maintaining high fill rates for customers, stocking at low system-wide inventory levels is the appeal of filling customer orders from multiple inventories.In this article, a traditional inventory planning approach is compared with one that is based on filling customer demand from any one of several stocking locations. This provides insights into the impact of inventory control methods, item fill rates, demand dispersion among inventory locations, and relevant inventory costs on system inventory levels. Guidelines are provided for determining the items that should be handled in the traditional manner and those that should be managed as virtual inventories, thus creating a mixed control strategy for the inventoried items. TRADITIONAL INVENTORY PLANNINGCommon pull-type inventory control systems set stocking levels based on demand, costs, and service requirements associated with the defined demand territory of the inventory location. These territories are often determined from location analyses that make demand assignments among multiple facilities. The motivation for this localized control is that it leads to lower inventory levels in a stock location than from alternative control methods such as a push-type method. As shown in Figure 1, all demand is assumed to be served from its primary assigned location. Demand that JOURNAL OF BUSINESS LOGISTICS, Vol.24, No.2, 2003 65 cannot be filled immediately is either backordered or lost. Demand and inventory at other locations play no role in setting the stocking levels at any particular location.Pull-type stocking methods range from EOQ-based to stock-to-demand approaches. The EOQbased methods are commonly known as reorder point, periodic review, min-max, or their variants. On the other hand, stock-to-demand methods set inventory levels in direct proportion to demand. Targeting a certain number-of-weeks supply (inventory turnover) and using a multiple of the forecast are examples of this method.Product availability, or fill rate, is set at less than 100% to avoid excess carrying costs. Depending on the stocking method, product availability is determined statistically or through quantities added to forecasted re...

The impact of different demand allocation rules on total stock levels

“…Research into virtual inventory management, variously referred to as demand allocation, inventory pooling, inventory consolidation, portfolio effect, and consolidation effect, has been around in literature in several forms for quite some time (Landers et al, 2000;Ballou & Burnetas, 2003). The basic idea is that inventory increases as the standard deviation of either demand or lead time increases, and, as a result, companies may attempt to reduce inherent variation by pooling it.…”

“…Evers ' (1996; 1997) and Ballou & Burnetas' (2003) studies specifically link the cross filling practice to portfolio and consolidation effects. Evers (1996) showed how the portfolio effect model could be employed to evaluate the use of regular transshipments so as to reduce safety stocks.…”

“…In Evers (1997), emergency transshipments were included into the portfolio effect analysis indicating that they result in the pooling of demand and lead time uncertainties. Finally, Ballou & Burnetas (2003) incorporated cycle stock consolidating effects to the portfolio effects on safety stock, when studying the cross filling between locations. Several restrictive assumptions were made regarding no lead time uncertainty, uncorrelated demands, and equal inventory-related costs.…”

“…This paper aims to explore the impacts of inventory centralization and cross filling on total stock levels, employing the consolidation effect model in terms of their underlying demand allocation rules. More precisely, should a given centralized facility supply the same fraction of demand to each decentralized location, like in Tyagi & Das (1998), or should a fraction of the demand be supplied by a primary source and the remainder by secondary sources, like in Ballou & Burnetas (2003)? The impact of both allocation rules on the consolidation effect and their implications in terms of inventory levels are addressed in order to show how the consolidation effect model can be used as a cornerstone tool to evaluate under what conditions it may be preferable to adopt one demand allocation rule to the detriment of the other.…”

“…Specifically considering Tyagi & Das' (1998) and Ballou & Burnetas' (2003) results, this paper differs from them, not only for considering less restrictive assumptions in terms of lead time uncertainties, demand correlations, and inventory-related costs, but also for developing a framework for deciding the more adequate demand allocation rule in light of different lead time and demand characteristics. This paper starts off by discussing the foundations of inventory consolidation planning; then, the two demand allocation rules are reviewed and linked to their respective inventorypooling models in the ambit of the consolidation effect.…”