Operations Management

Historical Demand and How to Remove Abnormalities

Written by Andrew Goldman for Gaebler Ventures

Historical demand is the basis for many important small business decisions. You want to be sure you are capturing this data correctly, so it can be properly analyzed.

Historical demand is the basis for some very important decisions.

Historical Demand and How to Remove Abnormalities

Almost any small business captures historical demand data and refers to it when making decisions about the future.

Indeed, historical company data is used by a large portion of small companies to forecast future demand.

It helps determine how much labor, materials and equipment to purchase for the upcoming year. It can be used to obtain outside financing or for presentation to outside investors.

How to Capture Historical Demand Data

First and foremost, your company should have a software system that captures historical demand. When you go to analyze this data, however, the historical demand can have abnormalities and inaccuracies that can cause serious problem for the small business looking to forecast future demand.

Depending upon the forecasting technique used by your company, chances are you take demand from previous periods and account for potential growth. From this historical data, a budget of expected demand is laid out in advance. This budget is used to plan production, purchase equipment and obtain raw materials needed to meet the expected demand.

Under-forecasting demand can lead to lost sales while over-forecasting your demand can lead to overpaying for surplus materials and resources. As a result, how accurately a small business forecasts can adversely affect their bottom line.

Forecasts are never going to be 100% accurate, but the idea is to be as close as possible. The best way to do this is to base your forecasts on the most accurate information possible.

Unfortunately, this is not as simple as taking last year's forecast and adding a percentage. A good portion of the historical data stored in your system may not be an accurate representation of actual demand. As a result, you need to set up your system to capture actual demand instead of historical sales.

The timing of when sales are booked into the system is a good example of inaccuracies in our historical data. Many companies try to squeeze a sale in early or delay it a few days in order to meet sales goals or for a salesperson to obtain a higher commission. While this may make sense for bookkeeping, it needs to be separated when analyzing demand. For example, if I sell $15,000 worth of product at the end of August, but I wait to book the sale until September, next year's September forecast will be higher and August lower, even though the reverse is what actually happened.

Another major hiccup in demand forecasting is one-time-only sales. A sale, which is made as a one-time deal should not be included in next year's forecast.

If we know a sale is not going to repeat itself, then we should not count on that sale when creating our forecast. Since the sale was a one-time deal, it is not a true measure of expected demand and it should not be included when we forecast.

It should also be noted, that potential demand can be lost if you did not have stock to meet an order. If a customer demanded additional product that you did not have in stock, this would not be captured in the historical data. Make sure that you have a system that captures this data as it was actual demand.

Many industries experience significant seasonal trends in their demand. In addition, there may be cyclical trends in your demand, where customers order during specific times of the month.

In either case, there are forecasting techniques available that remove the seasonalities and trends from our forecasting data. These techniques can be found via books, software or the Internet and can provide you with a picture of your actual demand without the trends involved.

This can be a great exercise to better plan for the future and analyze your actual monthly demands.

Andrew Goldman is an Isenberg School of Management MBA student at the University of Massachusetts Amherst. He has extensive experience working with small businesses on a consulting basis.

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