38 September/October 2010 | APICS magazine

Perception and PlanningStrategies for taking the sting out of your forecast

I volunteered as a Scout leader for a few years, and it always was interest-ing to work with the boys to plan an outing. We considered it good leader-ship for every boy to participate in the planning. Some of the information was determined easily, such as how long a trip would last or how many people were coming. Some things, however, needed a forecast. Was it likely to rain? What would we do if it did rain? The best plans had two essential ingredients: First, everyone worked to the same plan; second, all pertinent information was included in that plan.

In the business world, there are many methods of forecasting product demand, and they must include all known infor-mation. Extrinsic forecasting meth-ods involve factors such as economic conditions, market trends, competition, government regulations, or the sale of related goods. These techniques look for patterns or correlations linking prod-uct demand with these outside factors. Qualitative forecasting techniques most often are used for extrinsic forecasting. They are employed by senior managers and involve using good judgment, intu-ition, and informal opinions. Qualitative

forecasting is necessary for products where no previous sales data exist.

Intrinsic forecasting, on the other hand, uses data from previous sales, and the forecast is developed using that sales history. This quantitative forecasting is done by most members of a supply chain, especially those near the final consumer. Many factors can be included in the forecast along with traditional methods to improve forecast reliability.

Forecasting principlesForecasts have six major characteristics, which, when sufficiently understood, can provide insight into the need for further enhancements in a purely math-ematical projection.

Principle 1: Forecasts almost always are wrong. Outside of sheer luck, it is very difficult to exactly predict future events. We must learn to accept this and not use incorrect forecasts as an excuse for late ship-ments, stockouts, or excess inventories. Small errors in forecasting often take the blame, leaving real problems unat-tended.

Principle 2: Forecasts must include some measure of error. The forecast is going to be wrong—but by how much? Estimates of forecast error can be made by studying past perfor-mance. This then can be used to plan extra capacity into the supply chain and, of course, to calculate safety stocks.

Principle 3: Forecasts are more accurate for families or groups. The demand for individual products within a group is random even when there is stability for the overall group. Because of market forces, there is an overall demand for products; but an infinite number of variables come into play when trying to predict individuals’ choices. For capacity-planning pur-poses, forecasts of product groups should be based on similarity of the process or resources used. Individual item demand may shift dramatically within the group, but overall capacity will remain relatively stable. This often is difficult to manage within an orga-nization because marketing people will base group designations on customer patterns rather than commonality of production resources.

By Lloyd M. Clive, CFPIM

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APICS magazine | September/October 2010 39

Principle 4: Forecasts are more accurate in the near-term. It is far eas-ier to predict the weather for tomorrow than for next month. Similarly, using current demand to forecast a product’s future demand increases the likelihood of accuracy. The extrinsic factors such as demographics, economic condi-tions, and market environment tend to change very slowly over the long term. In the short term, outside influences are known, and the effect on current sales is obvious.

Principle 5: Wide-range forecasts are more accurate. The total demand for a product can be more accurately predicted over a number of periods than for individual periods. For example, the forecast of rainfall for a given month consistently is more accurate than indi-vidual daily predictions. With product sales, an overall demand exists, but daily sales are influenced by many factors. Early sales will offset later ones

because overall market demand likely shifts only slightly over time.

Principle 6: Forecasts are no substitute for calculated demand. Manufacturers need to forecast finished products. However, the demand for the subassemblies and components that go into a final product is entirely depen-dent on the demand for the finished products. This can be calculated more accurately than any attempt at forecast-ing. Errors in forecasting components will cause shortages of some materials and overages of others. This increases expense while limiting the final output to the lowest of the individual compo-nent forecasts.

Smoothing the forecastThe first principle of forecasting says forecasts almost always are wrong, which is of course unsettling for those who have to work with them. The market for many products has a certain total demand, and small fluctuations in sales for individual periods often are a result of timing. An increase in sales one day is offset by a decrease the next. Smoothing removes these effects, helps avoid disruptive daily demand swings, and works well when communicating demand to production.

Moving averages is the most widely used smoothing method because it is easily understood and calculated. It takes into account swings in demand due to timing; however, it lags the demand for products that are increasing

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40 September/October 2010 | APICS magazine

or decreasing in demand. Weighting the average improves the moving-average calculation, but other factors are needed.

Exponential smoothing is a form of weighted moving average that reacts to differences in current demand by moving the average by a specified amount in the direction of the differ-ence. Exponential smoothing simulta-neously adjusts the forecast base, trend, and seasonality as new information becomes available. Smoothing constants are used, which can be adjusted for sensitivity to the change.

Formulas for exponential smooth-ing appear quite daunting at first, and manual calculations are impractical. When the formulas are loaded into a spreadsheet, multiple calculations are easily performed, and the smoothing constants can become a useable tool to model forecast performance.

Factors influencing demandMany factors influence a forecast’s accuracy, and, once identified, they should be used to adjust the forecast. The demand for products is affected by promotions or sales incentives that are within the control of the company. Fortunately, there is a method of enhancing the mathematical fore-cast to include adjustments for these

influences. Such an enhancement is reached by adjusting promotional indices similar to seasonal indices using simple models.

Forecast inaccuracy may be caused by random variation or some extrinsic influence. However, the error often is a result of actions on the company’s part and is not reflected in a system that sim-ply uses the past to predict the future. Adjustments rarely are made to forecasts for the following known factors, whether internal or external:

Principle 4 states that forecasts are •more accurate for nearer periods of time. The implication here is that we continually can improve a forecast that originally was made far enough in advance to plan production and accommodate lead time. However, this does not happen in practice because it is human nature to resist changing something that already is in place. Outside influences on demand are not •included in the mathematical model. These include general swings in the economy and variations in competi-tive forces. Senior managers consider these factors and make collective decisions to overall projections, often expressed in qualitative “gut feeling” terms such as “sales will be strong next quarter”; or more broad-brushed adjustments such as “preholiday sales will be up 10 percent this year.”

There are activities within a com-•pany’s control that will influence demand in ways that are not cap-tured. Product promotions will have an effect on demand that marketers can predict with some degree of accuracy. A company’s forecast-ing system shouldn’t be caught unaware by its own internal efforts to increase sales.The least common reason for •adjusting the forecasting model is to keep the database of past demand as valid as possible. For example, a company might experience much higher than normal demand for a product due to some unforeseen calamity that affected its competi-tion. A mathematical model alone will interpret this as an overall increase in demand and one that will reoccur annually. This can be corrected in the forecasting data-base, improving it for the subse-quent year. Note that past sales data should not be changed in the main database in any way.

Characteristics of demandFigure 1 represents a sales history that displays three fundamental patterns: the base, a growth trend, and a regular variation about the base due to season-ality. These are well-accepted factors for planning business sales, products, and labor. Through price incentives and other methods, it’s possible to change the demand for products by making them more appealing to customers. The change in product sales will fol-low predictable patterns, which can be represented by promotional profiles.

A company’s forecasting system shouldn’t be caught unaware by its own internal efforts to increase sales.

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Figures 2, 3, and 4 depict the sales demand for three types of promotions. Figure 2 shows a basic advertising promotion meant to increase product awareness and increase long-term product demand. Figure 3 shows a price reduction that results in increased sales during the promotion followed

by a decrease in sales due to tempo-rary market saturation. Figure 4 shows the results of a new model or product upgrade. Note that overall trend and seasonality have been removed for simplification.

The profiles can be additive. That is, the profile is simply the number of

increased sales due to the promotion. Or, the profiles can be multiplicative. Sales are indicated by their percentage increase in a given period. Showing a percentage increase in sales is easier to apply to groups of products.

Let’s assume a company has tracked sales over a few years. There would be

Seasonality

Base

Time

TrendDem

and

Increase in sales

Old product

Figure 1: A sample sales history

Increase in sales

Figure 3: Price promotion Figure 4: New model introduction

Increase in sales

Figure 2: Advertising promotion

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42 September/October 2010 | APICS magazine

an estimate of the base demand for sales, how they trend over time, and how they behave because of seasonal-ity. To make a forecast into the future, professionals would take the base and adjust each period by multiplying by the seasonality. If a promotional profile also had been determined, it would be added to each future forecast to arrive at an even better forecast.

The promotional profile works independently of all historical informa-tion, making it easier to adjust as new information becomes available. When recording historical sales, the promo-tional profile is removed from the data to correctly track seasonality and trend.

The real opportunity in this area is not the manipulation of data in an attempt to reduce forecast error, but the resulting discussion and activ-ity that take place. Frequent com-munications are required between marketing, production, and supply departments in order to include as many adjustments to the forecast as are practical. More information will result in product promotions without surprises and overall improvements to customer service.

Equipped for successSurprises on a Scout outing may result in unhappy campers. Surprises in the business world cause lost sales, higher costs, and unhappy customers.

Promotional profiles enable the input of many people, improve the accuracy of anticipated demand, and help opera-tions managers live up to the Scout motto—“Be prepared.”

Lloyd M. Clive, CFPIM, is coordinator of

materials management at Fleming College

in Peterborough, Ontario. He may be

contacted at lclive@flemingc.on.ca.

Editor’s note: Author Lloyd M. Clive, CFPIM, will present an educational session at the 2010 APICS International Conference & Expo. His presentation, Modeling Plans and Patterns for Improved Forecasting, will enable participants to better understand what factors influence demand; illustrate common demand patterns; and provide tools for improving forecast accuracy, timeliness, and effectiveness. For more information, visit apicsconference.org.

An exponential smoothing model in Excel that uses the profiles featured in this article may be downloaded at apicskawartha.ca.

To comment on this article, send a message to feedback@apics.org.

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