Demand Forecasting Techniques: Moving Average & Exponential Smoothing

Lesson Transcript
Christian George

Christian has a PhD in Business Management and an MA in Accounting & Financial Management

Expert Contributor
Jerry Allison

Jerry holds a Doctor of Business Administration and a Master’s in Mathematics. He has taught business, math, and accounting for over 25 years.

This lesson will discuss demand forecasting with a focus on sales of established goods and services. It will introduce the quantitative techniques of moving average and exponential smoothing to help determine sales demand. Updated: 11/26/2019

What Is Demand Forecasting?

Picture the holiday season. Kids are ready for a visit from Santa, and parents are stressed out over shopping and finances. Businesses are finalizing their operations for the calendar year and preparing to move into whatever lies ahead.

ABC Inc. manufactures telephone wire. Their accounting and operations time periods run on a calendar year, so the end of the year allows them to wrap up operations before the holiday break and plan for the beginning of a new year. It's time for managers to prepare and submit their department's operational plans to senior management so they can create an organizational operations plan for the new year.

The sales department is stressed out of their minds. Demand for telephone wire was down in 2015, and the general economic data suggests a continuing downturn in construction projects that require telephone wire. Bob, the sales manager, knows that senior management, the board of directors, and stakeholders are hoping for an optimistic sales forecast, but he feels the ice of industry recession creeping up behind him to tackle him.

Demand forecasting is the method of projecting customer demand for a good or service. This process is a continual, where managers use historical data to calculate what they expect the sales demand for a good or service to be. Bob uses information from the company's past and adds it to economic data from the marketplace to see if sales will grow or decline. Bob uses the results of demand forecasting to set goals for the sales department, while trying to keep in line with company goals. Bob will be able to evaluate the results of the sales department next year to determine how his forecast came out.

Bob can use different techniques that are both qualitative and quantitative to determine the growth or decline of sales. Examples of qualitative techniques include:

  • Educated guesses
  • Prediction market
  • Game theory
  • Delphi technique

Examples of quantitative techniques include:

  • Extrapolation
  • Data mining
  • Causal models
  • Box-Jenkins models

These examples of demand forecasting techniques are only a short list of the possibilities available to Bob as he practices demand forecasting. This lesson will focus on two additional quantitative techniques that are simple to use and provide an objective, accurate forecast.

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  • 0:03 What Is Demand Forecasting?
  • 2:22 Moving Average Formula
  • 3:47 Exponential Smoothing
  • 5:55 Lesson Summary
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Moving Average Formula

A moving average is a technique that calculates the overall trend in a data set. In operations management, the data set is sales volume from historical data of the company. This technique is very useful for forecasting short-term trends. It is simply the average of a select set of time periods. It's called 'moving' because as a new demand number is calculated for an upcoming time period; the oldest number in the set falls off, keeping the time period locked. Let's look at an example of how the sales manager at ABC Inc. will forecast demand using the moving average formula.

The formula is illustrated as follows:

Moving Average = (n1 + n2 + n3 + ...) / n

Where n = the number of time periods in the data set. The sum of the first time period and all additional time periods chosen is divided by the number of time periods. Bob decides to create his demand forecast based on a 5-year moving average. This means that he will use the sales volume data from the past 5 years as the data for the calculation.

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Additional Activities

Moving Averages and Exponential Smoothing:

Calculation Problem 1

FunkyTunes has revenue in January of $5000, in February of $6000, in March of $7000, and in April $8000. Forecast the revenue for May using a three-month moving average.

Using the same data, assume the forecast for April was $8200. If FunkyTunes uses a smoothing constant of 0.6, what would be the forecast for May using exponential smoothing>

Calculation Problem 2

We-B-Tools has a sales forecast of 630 tools for November. After November is over, the actual sales amount was 600. How much is the error in the forecast (use the absolute value)? What is the percentage error of the actual sales amount?

Research Project

Demand is not the only thing that forecasting can predict. There are many things in the economists that economists try to predict. Using the Internet, find three things that economists try to predict on a monthly basis.

Discussion Questions

1. Under what situations would you consider a moving average to be a better forecast model than exponential smoothing? Why?

2. When do you think a two-month moving average is better than a three-month moving average? What about a four-month moving average? Why?

3. Typically, when one discusses demand, business sales are referenced. However, consider an Internet server. Web browsers all over the world create a demand for information from this server. For businesses, we often think of demand in terms of months and days. For an Internet server, what time frame do you think would be appropriate? Why?

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