The Problem Using a Monthly Forecast Percent Error

Executive Summary

  • The percent forecast error is commonly calculated in a problematic way.
  • We cover the proper forecast error measurement in the time dimension.
  • There is a general mismatch of this forecast percentage error calculation with reality.
  • This is the problem with using a month as the forecast measurement interval.

Introduction to Forecast Error

Not much thought is given to this topic, yet all forecast errors reported by companies are questionable because of an important consideration. That is the interval over which the forecast error is calculated. You will learn about how forecast error is managed.

How the Percent Error is Calculated and the Mismatch with Lead Times

If we review how the forecast error is commonly calculated by supply chain companies, we find the following.

  • Typically forecast error is calculated on a month per month basis. That is the forecast is divided by the actual demand for a product location (or for whatever level of aggregation is being measured). In a dynamic safety stock calculation, the error is calculated over the lead-time. If the lead-time for the product is two months in length, and the month to month MAPE is 50 percent error, if 50 percent error is used, while the two-month MAPE is 25 percent error, the calculated safety stock will be too high.
  • If on the other hand the lead-time is two weeks, and the 50% MAPE is used, the safety stock will be too small.

The Proper Forecast Error Measurement in the Time Dimension

The only proper forecast error measurement is over the lead time. This can be seen by taking an example. If you have a one-week lead-time, then you can reorder every week. Therefore you can reorder the following week. On the other hand, if the lead-time is three months, you cannot adjust the forecast during the three month period after the order is placed.

Therefore under the standard monthly forecasting error measurement interval, the forecast error will be overestimated for the product with the weekly lead time and underestimated for the product with the three month lead time.

The Problems with Using a Month as the Forecast Measurement Interval

A month is used in many cases to measure forecast error, and I do it myself on projects because if one has an overall database of products, it is too much work to adjust the forecast error per lead time per product as each product has a different lead-time.

Now I have never once seen this topic raised on projects. But it is undeniably true. Therefore, due to the complexity of measuring forecast error in this way, the standard and inaccurate interval of a month continue to be used for prediction error.

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Brightwork Forecast Explorer for Monetized Error Calculation

Improving Your Forecast Error Management

How Functional is the forecast error measurement in your company? Does it help you focus on what products to improve the forecast? What if the forecast accuracy can be improved, by the product is an inexpensive item? We take a new approach in forecast error management. The Brightwork Explorer calculates no MAPE, but instead a monetized forecast error improvement from one forecast to another. We calculate that value for every product location combination and they can be any two forecasts you feed the system:

  • The first forecast may be the constant or the naive forecast.
  • The first forecast can be statistical forecast and the second the statistical + judgment forecast.

It’s up to you.

The Brightwork Forecast Explorer is free to use in the beginning. See by clicking the image below:


I cover this topic in depth in the following book.

Forecasting Software Book



Supply Chain Forecasting Software

Providing A Better Understanding of Forecasting Software

This book explains the critical aspects of supply chain forecasting. The book is designed to allow the reader to get more out of their current forecasting system, as well as explain some of the best functionality in forecasting, which may not be resident in the reader’s current system, but how they can be accessed at low-cost.

The book breaks down what is often taught as a complex subject into simple terms and provides information that can be immediately put to use by practitioners. One of the only books to have a variety of supply chain forecasting vendors showcased.

Getting the Leading Edge

The book also provides the reader with a look into the forefront of forecasting. Several concepts that are covered, while currently available in forecasting software, have yet to be widely implemented or even written about. The book moves smoothly between ideas to screen shots and descriptions of how the filters are configured and used. This provides the reader with some of the most intriguing areas of functionality within a variety of applications.


  • Chapter 1: Introduction
  • Chapter 2: Where Forecasting Fits Within the Supply Chain Planning Footprint
  • Chapter 3: Statistical Forecasting Explained
  • Chapter 4: Why Attributes-based Forecasting is the Future of Statistical Forecasting
  • Chapter 5: The Statistical Forecasting Data Layer
  • Chapter 6: Removing Demand History and Outliers
  • Chapter 7: Consensus-based Forecasting Explained
  • Chapter 8: Collaborative Forecasting Explained
  • Chapter 9: Bias Removal
  • Chapter 10: Effective Forecast Error Management
  • Chapter 11: Lifecycle Planning
  • Chapter 12: Forecastable Versus Unforecastable Products
  • Chapter 13: Why Companies Select the Wrong Forecasting Software
  • Chapter 14: Conclusion
  • Appendix A:
  • Appendix B: Forecast Locking
  • Appendix C: The Lewandowski Algorithm.