by Jon Schreibfeder

Distributors today face more competition than ever before. This competition has resulted in decreased profit margins and increased competition. In order to remain both profitable and competitive, distributors have to provide more product availability and services using fewer dollars.

In this environment, it is critical to remove all excess inventory from your warehouse(s). Excess inventory is any quantity of a stocked product that is not necessary to meet your customers’ needs before being replenished, at a cost that allows you to competitively sell the product.

It is important to realize that excess inventory is not limited to "dead stock" (those products with no sales in the last X months). It also includes larger than necessary quantities of products sold on a regular basis.

Excess inventory of a product that is sold or used on a regular basis is often the result of an inaccurate forecast of future demand. If the prediction of future demand is too high, distributors tie up cash in unneeded inventory.

Excess inventory may also result from forecasts that underestimate actual demand. How? If a distributor underestimates customer demand and experiences a stockout, he or she may panic and override the computer system’s recommendations for product replenishment. The result: an unusually large quantity of the product in order to protect customer service.

Any part of this unusually large quantity not actually needed to protect customer service is excess inventory. It is money tied up in an asset that is not producing a return on investment. In order to prevent excess inventory, forecasts of future demand must be as accurate as possible.

Most systems forecast future demand with a formula that utilizes past sales or usage history. There is an old buyer’s expression, "What was sold in the past is a good indication of what will be sold in the future." Often, the formula using past usage or sales is supplemented by a trending factor that compensates for recent increases or decreases in usage.

Forecasting future demand solely on past usage history is like predicting weather from a windowless room using a stack of old newspapers. Sure, there is plenty of data available, but wouldn’t the forecaster get better information if he or she contacted other nearby weather stations, looked at current satellite data or perhaps even opened a window?

A better method
Collaborative Planning Forecasting and Replenishment (CPFR or just collaborative forecasting) is the process of setting up a continual line of communication between you and those customers with the ability to predict the future needs of the products they buy from you. As your customer’s work schedule is developed, their system automatically alerts your system when they will need a specific quantity of each of the products you supply. If their work schedule changes, your system automatically learns how the changes will affect the delivery of these items. Let’s look at an example.

Dower Industries uses a No. 456 gasket in the process of rebuilding a No. A4000 power unit. On average, Dower rebuilds two power units each month. Because the No. A4000 power unit is critical to Dower’s operations, its supplier, Ajax Distribution, normally keeps four No. 456 gaskets in stock.

But in early September, Dower’s engineers decide they need to rebuild eight power units in November. Although the No. 456 gasket is a critical component in the rebuilding process, it is only a small element of

the total procedure. Ajax Distribution always has an ample supply of gaskets in stock, so it doesn’t occur

to Dower’s buyer to notify Ajax of the increased need for the product occurring in eight weeks.

What happens?
On November 1, Dower starts rebuilding the power units. After completing four units, they’re stopped dead in the water because there are no more gaskets. Dower’s management strongly voices its displeasure at the buyer, who in turn unloads on his contact at Ajax. Ajax offers excuses, citing the unusual demand and offers to increase its normal inventory of No. 456 gaskets from four pieces to eight. The result: Ajax has disappointed the customer and brought in additional stock that is probably excess inventory.

This situation could have been avoided if Ajax and Dower implemented a program to exchange need and availability information. Using a CPFR system, Dower would have notified Ajax of the increased need for the gaskets as soon as it made the decision to accelerate maintenance operations. Ajax would have ordered more gaskets for a late October or early November delivery.

Better communication
Advances in electronic commerce have facilitated better communications between computer systems that has resulted in the development of electronic CPFR systems. Many distribution systems can now be set to receive inventory requirement information from their customer’s scheduling and production software. These requirements are considered in the distributor’s demand forecast calculations.

Instead of basing the forecast just on past usage, these systems add a factor for the collaborative forecast. Let’s use a part No. B389 as an example:

Forecast for November

Why is there still a component based on past usage? Not all customers can provide reliable collaborative forecast information. Note that if a customer routinely provides collaborative forecasting information to the distributor, sales to that customer are not included in usage history. After all, we do not want to consider sales to that customer twice (once in usage history and again as part of the collaborative forecasts).

Also, realize that for a CPFR system to be successful, it is crucial that the distributor monitor the accuracy of the collaborative forecasts. In other words, determine if the customer regularly buys a quantity close to what he has forecast.

Distributors that rely on collaborative forecasts often base customer discounts on the accuracy of the forecast. Even those distributors that cannot implement an electronic collaborative forecasting system receive tremendous benefits simply by asking customers about their future projects and needs on a regular basis.

Finally, don’t confuse collaborative forecasts with advanced orders placed by the customer. Under an advanced order system, a customer places orders for goods far in advance of when he or she needs them.

In our original example, Dower Industries might have placed an order for eight No. 456 gaskets to be delivered on November 1. Several of those gaskets would sit on Dower’s storeroom shelf for close to a month before being needed. Dower’s cost of procuring material would be unnecessarily high. After all, they have to pay for the material, store it, protect it from damage, etc.

Collaborative forecasting allows a customer to pull material from the distributor’s stock as needed. The customer can acquire material on a just-in-time basis. Their cost of acquiring and carrying material is minimized. And, because of having a better forecast of future demand, the distributor achieves a high level of customer service with minimal reserve inventory. In other words, they can consistently meet their customers’ requirements with a minimal amount of inventory. It is easy to see how successful collaborative forecasting results in a win-win situation.

It is not surprising that CPFR is considered one of the most exciting and profitable applications in electronic commerce. But even a manually maintained collaborative forecasting system will reduce excess inventory for both a distributor and his or her customers.

Remember, the supply chain is made up of a series of interconnected links. A single unconnected link is not much use to anyone.

This article originally appeared in the March/April 2001 issue of Progressive Distributor. Copyright 2001.

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