PROBLEM SOLVING WITH STATISTICS

Problem Solving With Statistics

Problem Solving With Statistics

Introduction

In an ideal world, supply chain partners would work together to rethink and restructure business practices, as necessary, to provide consumers with products and services better/faster/cheaper than ever before. System success would be predicated on a free exchange of critical information and commitment to reaching shared goals. While this may sound somewhat removed from reality, such a scenario is fairly close to what many leading edge companies are doing today (Davis, 1998). Automatic replenishment systems have been implemented in a great number of firms in recent years. With automatic replenishment programs (ARP), inventory restocking is triggered by actual needs rather than relying on long-range forecasts and layers of safety stock “just in case” (Andel, 1996; Cottrill, 1997; Keh and Park, 1997).

Problem Solving With Statistics

Some firms that have instituted ARP are now taking supply chain co-operation to another level through involvement in collaborative planning/forecasting/replenishment (CPFR). CPFR attempts to lessen the problems associated with traditional anticipatory demand forecasts by co-operating with trading partners to better match supply and demand. Thus, it makes firms better prepared and ready to respond to market signals.

ARPs provide day-to-day guidance for inventory replenishment. In contrast, CPFR relates to long-term planning. CPFR involves collaborating and jointly planning to make long-term projections which are constantly up-dated based on actual demand and market changes (competitive efforts, new promotional plans, etc.). CPFR has been described as a step beyond efficient consumer response, i.e. automatic replenishment programs, because of the high level of co-operation and collaboration needed (Tosh, 1998).

Collaborative planning, forecasting and replenishment

Forecasting demand (and subsequently setting inventory levels) is difficult owing to the influence of promotions, changing demand patterns, and competitive pressures. The traditional answer to inventory problems has been to simply hold increased inventories. Holding high levels of anticipatory inventory may offer a way to avoid out-of-stocks, but it is a very expensive method of avoidance. As an alternative, many value-chain participants (i.e. the buyer-seller dyad) have determined that a better approach is to aggressively work together to manage inventory. Co-operative planning between trading partners facilitates better matching of supply and demand. Rather than trying to independently project demand patterns, buyers and sellers share information in advance and work together to develop realistic, informed, and detailed estimates that can be used to guide business operations.

Traditionally, separate and disjointed operating units across companies have independently made plans. This has often resulted in “uncoordinated store, procurement, and logistics planning for the retailer while manufacturers see sales, distribution, and production planning being out of synch” (Koloszyc, 1998, p. 28). CPFR attempts to eliminate such problems through detailed exchange of point-of-sale and other information on a real-time basis (Wolfe 1998). Internal co-ordination is also needed. As Joseph Andraski, vice president of customer marketing operations at Nabisco has noted, many companies develop forecasts in disparate areas including marketing, finance, purchasing, and logistics. There is no assurance that these plans will ever “come together” or be reconciled. Instead, someone within the organization makes a decision as to ...