As we have been running our series about Inventory Management we were approached by Jon Schreibfeder to help share with the readers a more comprehensive way to look at their inventory for all types of business practices. Of course we said yes because of his credentials in working with Inventory issues. If you remember our overview of Gordon Graham several weeks ago well John is one of the Graham disciples as well as Alan Silver, a gentleman I had the pleasure of working with about 30 years ago. These professionals help us focus on the theory and more practical use of that theory in managing your Inventory. Jon is president of Effective Inventory Management Inc. and you can find out more about him at EffectiveInventory.com or look over his brief bio at the bottom of the newsletter.

How Well Can You Predict the Future?

By Jon Schreibfeder

The two basic questions faced by every purchasing agent and buyer are:

When do you replenish your inventory of a stocked item?
When you issue a replenishment order how much do you buy?

Both of these questions rely on having a good idea of how much of each product will be sold or used in the future. This prediction is commonly referred to as a demand forecast. If you do not have an accurate demand forecast, that is you do not predict well, you will probably either be over stocked (because you ordered material too early or you ordered too much) or you will experience stock outs (because you placed a replenishment order too late or your ordered too little).

The accuracy of your demand forecasts is very important to the success of your business. Unfortunately most manufacturers, distributors and retailers do not do a good job of forecasting future demand of products. We conducted a study several years ago for the National Association of Wholesale Distributors (NAW). For this study we analyzed the demand forecasts and subsequent actual usage for every item in the inventory of 24 representative companies over a three month period. The companies we analyzed came from a wide variety of industries, ranged in size from $2 million to $20 billion in sales and used a number of different software packages. To calculate the forecast error we used the equation:

Absolute Value of (Demand Forecast – Actual Usage)
Smaller of Demand Forecast or Actual Usage

Actual usage represents all outgoing shipments including sales, transfers and work orders. Here are several examples of how we calculated the forecast error:

Item	Demand Forecast	Actual Usage	Equation	Forecast Error%
A100	50	100	Absolute Value of (50 - 100) ÷ 50	100.0%
B200	100	50	Absolute Value of (100 – 50) ÷ 50	100.0%
C300	95	100	Absolute Value of (95 – 100) ÷ 95	9.5%

Look at the first two items in the above table. Note that we receive the same forecast error with a demand forecast of 50 and actual usage of 100 as a demand forecast of 100 and actual usage of 50. We believe it is as bad to be overstocked as under stocked.

It was surprising that the mean average forecast error for all of the distributors in the study was 681%. We calculated this figure by totaling all of the forecast errors for each distributor and dividing the total by the number of items forecast by that distributor. The median forecast error for all of the distributors was 382%. The median was determined by sorting all of the items by the forecast error and taking the middle value. That is, for the average distributor in our study half the forecast errors were less than 382% and half exceeded this value.

Many people have asked us how you can have a forecast error that exceeds 100%. It is easy. What if you were forecast demand of one piece of a product this month and have actual usage of 7 pieces. We would calculate that to be a forecast error of 600% [(Absolute Value of 7 – 1) ÷ 1]. Others wonder why their sales departments continually claimed that they continually come within 5% - 10% of forecast demand. A common reason is that that salespeople tend to look at total aggregate sales of the company against the total demand forecast. In our study (as well as experience with other customers) it is not uncommon to find a similar number of products whose demand is overestimated as underestimated. Unfortunately customers order specific products and the most accurate measurement of demand forecast accuracy is to examine the results on an item level.

We found that improving the accuracy of demand forecasts is one of the most effective ways of improving corporate profitability. An informal survey of our customer base found that those companies in the upper quartile of profitability in their industries tend to have a median average demand forecast error (as calculated above) of less than 25% - 30%. This is about one tenth the forecast error of the companies that participated in our study.

In the next few newsletters we will explore ways you can improve the accuracy of your demand forecasts. But as an initial step in improving the productivity and profitability of your investment in stock inventory, why don’t you calculate the forecast error of every inventory item for each of the past three months. If an item has a high forecast error (i.e., above 75%) examine the product and see if you determine the reason behind the forecast inaccuracy.

Jon Schreibfeder is president of Effective Inventory Management, Inc., a firm dedicated to helping manufacturers, distributors, and large retailers get the most out of their investment in stock inventory. For over 20 years, Jon has helped over two thousand firms improve their productivity and profitability through better inventory management. Jon has designed several inventory management computer systems and has also served as a distribution industry "troubleshooter" for two major computer companies. He is the author of numerous articles and a series of books on effective inventory management, including the recently published Achieving Effective Inventory Management (5th edition) and the National Association of Wholesale Distributors' Guess Right – Best Practices in Demand Forecasting for Distributors.

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