How to Best Understand Collaborative Forecasting

Executive Summary

  • Forecasting collaboration is quite often misunderstood in term of how to effectively perform the collaboration.
  • How it should be measured and overall how collaboration can be best performed for improving forecast accuracy.


Collaborative forecasting is the sharing of forecasts between different companies.

Forecast Collaboration the Reality…

If you read some of the literature on collaborative forecasting, it can sound as if the companies come together to arrive at a forecast, and it can sound suspiciously like (consensus based prediction) CBF. However, in fact, the vast majority of collaborative forecasting comes down to one company sharing its forecast with another company. The receiving company then chooses, quite unilaterally if it uses the forecast.

What is One of the Most Surprising Things About Collaborative Forecasting?

Many forecasts that are shared between companies are never used.

Ways of Performing Collaborative Forecasting

  • With documents (EDI, XML, Excel)
  • With collaborative applications (SAP SNC, E2Open, etc..)

How Do The Applications Stack Up?

There are a few SaaS forecasting solutions in the market for detailed forecast collaboration, with E2Open the closest to what I have seen as a collaborative SaaS solution that could get partners to use it. SAP has a product called SAP SNC (Supplier Network Collaboration, which is a misnamed as it supports much more than supplier collaboration), however…

  • SNC is not SaaS
  • SNC has a weak user interface, which is challenging to get partners to use.
  • SNC has significant implementation complexities in its messaging side, (the combination of skill sets required to implement SAP SNC is the main reason it is perpetually is-staffed).

However, it may be interpreted as an attractive solution, by companies that use SAP ERP and other modules in the SAP APO/SCM suite as it is partially integrated to SAP (We hesitate to say fully integrated because it requires a good deal of configuration and maintenance).

The actual level of SAP to SAP integration with all SAP products is always significantly overestimated and oversimplified for corporate buyers.) E2Open is an example of a SaaS collaboration forecasting environment for particular level forecast collaboration. This level of cooperation can be directly imported into a demand planning system at the product location level.

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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.

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