How do Complex Forecasting Methods Stack Up?

Executive Summary

  • History shows some interesting insights as to complex methods.
  • There are frequent misunderstandings regarding forecastability which leads to the misuse of naive methods.
  • There are important incentives that promote more complex forecasting methods or more simple.

Introduction to Complex Forecasting Methods

Selecting the right forecasting methods is a critical component of forecasting. You will learn why complex forecasting methods tend to be overestimated and how to select the right level of complexity.

Misunderstanding Forecastability

Individuals and companies and have a great tendency to overestimate forecastability. For difficult to forecast products there is an interpretation that they can be overcome with more complicated methods. In finance very, complex models are trusted which are often not much better than a guess. For instance, difficult to forecast products have weak demand patterns, and because of this, any statistical method employed has difficulty in predicting the future.

Underuse of Naive Methods

Naive forecasting methods, which should be used for many products, end up not being used because testing is often not performed which compares the naive forecast to the more sophisticated method.

How the Incentives Work to Promote More Complex Forecasting Methods

Software vendors know that companies are looking for this magic bullet and this is why a large amount of marketing effort goes into including more advanced methods in the application. Similarly, academics have incentives to publish on sophisticated techniques because more complex methods are more publication worthy and considered more prestigious than less complex methods.

As an example, it would be tough to get a paper published regarding the benefits of using a simple moving average because academic journals require that researchers submit “original work.”

The Effect of Computerization

There is little doubt; forecasting can be logically broken into pre-computerization and post-computerization. Automation was introduced at an elite level, so in the 1950s researchers users computers to calculate mathematical methods applied to supply chain problems. Gradually computers enabled everything from warehouse workers to supply chain planners to do their job more efficiently.

George Plossl on Historical Inventory Planning

George Plossl had the following to say about historical methods for inventory planning:

“Planning was manual, slow and crude. Large clerical groups calculated gross planning requirements for major components of their products and time-phased (albeit very roughly) these and their procurement. Revising such plans was even more tedious and was rarely done. The capability of massive data storage and manipulation required for sound inventory management planning simply did not exist at that time. Because of this constraint, stock replenishment (OP/EOQ) methods predominated prior to the 1970s. Inventory control was attempted utilizing paper records and electoromachanical desk calculators to apply essentially simple mathematical formulas for order-quantity and safety stock calculations.”

Computers and the Methods Applied

As computerized forecasting methods gave way to more advanced statistical approaches that were encapsulated in software, there became a bias within software vendors to overestimate and over-sell the ability of their algorithms to forecast difficult to forecast products. For whatever reason, rather than introducing complex algorithms that were viewed as magic bullets by executives making the purchasing decisions, but often with little real-world experience performing forecasting or working as demand planners themselves.

How Was This Done?

This was often done while shorting the effort placed into developing either better demand planning interfaces or better data layers. This turned out to be a poor allocation of development resource. This is because of more advanced statistical models most often failed to outperform simpler methods. The infatuation with increasingly esoteric forecasting algorithms prevented executives from understanding that there was an upward boundary regarding how much a forecast for a product with little discernable pattern to its demand history could be improved. As pointed out by Michael Gilliland a very reliable source of forecasting:

“Forecast accuracy is determined more by the forecastability of the demand than by the sophistication of a statistical technique. Statistical techniques work well as long as the historical patterns are properly captured in the forecasting model, there is not a lot of randomness in the patterns and these patterns continue to be the same in the future. Unfortunately, these conditions don’t always apply.”

Michael Gilliland describes the efficiency of statistical forecasting:

“A major benefit of the statistical approach is that it can be a very efficient way to generate forecasts. Once the system is implemented and the models are built, it can run on autopilot with little or not management intervention—which is about as efficient as an approach can get. In addition to being efficient, the statistical approach can also be effective in situations when demand follows consistent and detectable patterns, and continues to behave obediently into the future.”


Complex forecasting methods are often assumed to be more accurate than less complex simply because of the “wow” factor. Furthermore, more complex methods are less well understood by a larger percentage of the buyers of forecasting systems, and directors of forecasting, giving them what is often an unearned prestige.

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Brightwork Forecast Explorer for Forecastability

Improving Your Understanding of Your Forecasting Database

Companies required an understanding of the forecastability of their datasets. Brightwork Research & Analysis offers the following software which provides the measure of forecastability.

It is free to use until it receives “serious use,” and is always free for students and academics. See by clicking the image below:


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.

Forecasting Parameters Book

Forecast Parameters

Uses of Forecast Parameters

Statistical forecasting is one of the major approaches to creating forecasts across many different fields. Statistical forecasting has proven unmatched in the ability to cost effectively create enormous numbers of items, and the statistical forecast is often the first forecast which is created – although manual adjustments may follow.

The Need to Understand Forecast Parameters

However, in many statistical forecasting systems, it is necessary to understand the parameters, which control the forecasting model and to tune it for the specific items to which it applied. This book not only explains the statistical forecast parameters (dividing them into three parameter categories) but also shows many examples of parameters and how the parameters affect the different forecasting examples.
 By reading this book you will:
  • Understand the different categories of forecast parameters.
  • How different statistical forecasting applications work with forecast parameters (learn the difference between manually set and internally set forecast parameters and so-called best-fit forecasting)
  • Learn how changes in forecast parameters create changes to the forecast produced.
  • How to compare and contrast forecast parameters to understand better the forecast profiles which a company uses.


  • Chapter 1: Introduction
  • Chapter 2: Where Forecasting Fits within the Supply Chain Forecasting Footprint
  • Chapter 3: The Common Problems with Statistical Forecasting
  • Chapter 4: Forecast Parameters
  • Chapter 5: Introduction to Best-Fit Forecasting
  • Chapter 6: Conclusion

Software Ratings: Demand Planning

Software Ratings

Brightwork Research & Analysis offers the following free demand planning software analysis and ratings. See by clicking the image below: