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How to Find the Source of Error in Your Operations Management Forecast

Operations management forecasts tend to be inaccurate, and you need to find out how (in)accurate your forecasting model is. Forecasting error is the difference between the forecast and actual values. Forecasts are inaccurate for many reasons. Here are some of the most common sources of errors:

  • Incorrectly identifying the relationship between variables: Identify the correlation between one variable and another. In reality, there may be more than one variable determining an outcome. For example, sales of electric automobiles can be related to not only the price of gasoline but also the price of the car itself and the availability of public charging stations in your town. Correctly identifying variables has an impact on your forecast.

  • Not recognizing trends in demand: When you fail to recognize trends (either upward or downward) and don’t account for them in your forecasting model, your forecast will significantly lag your actual demand. Trends can change quickly and be subtle and therefore be difficult to observe. Using the wrong trend line is a common mistake.

  • Not updating forecasting assumptions and techniques: You should monitor your forecasting method on a regular basis to detect any changes in demand patterns. Fundamental shifts in demand may require you to change your forecasting technique. By monitoring your forecasting error, you can quickly detect changes in your demand.

  • Projecting past trends into the future: When you use the time-series methods (moving average and exponential smoothing), you’re making the assumption that past patterns will continue in the future. This can be dangerous, especially in rapidly changing markets, where products experience tremendous growth in demand or become obsolete quickly.

  • Reacting to random or special cause variations: Random variation is the natural shifts that occur from many minor sources. Special cause variation is fluctuation that can be contributed to an event that doesn’t normally occur, such as a hurricane warning that forces evacuations and causes a rise in hotel stays in certain areas. Don’t react to these variations, because they’re unpredictable and nonrecurring.

  • Relying on biased information sources: Sales performance is often measured based on actual sales as compared to the forecast. If actual sales exceed the forecast, salespeople are often rewarded, so low forecasts offer them a greater chance of exceeding it. Production staff tend to prefer high demand forecasts so they have more resources available to meet the forecasted demand. Always consider the source of information for your forecasts.

  • Using an insufficient number of data points: Using time-series data often requires a significant amount of data, especially if trends or seasonality conditions exist. What may look like a pattern in your data could be random variation in demand. You want to make sure you have enough points to observe the pattern over several seasons. Exactly how much past data you need depends on the nature of your business.

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