Supply Chain Forecasting Software

Screenshots and examples from ToolsGroup, Demand Works, Consensus Point, Right90, and Inkling Markets.

What the Book is About

This book explains the essential 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. The book also provides the reader with a look into the leading edge of forecasting. Several concepts that are covered, while currently available in forecasting software, have yet to be widely implemented.

The book moves smoothly between concepts to screenshots and descriptions of how the screens are configured and used. This provides the reader with some of the most intriguing areas of functionality within a variety of applications.

Reviews for Supply Chain Forecasting Software

This book has received some nice reviews. Here are some quotations and some links below:

“In 2011 I came across Shaun Snapp’s blog (, and immediately took a liking. In particular, I was drawn to his independent and critical tone, and a willingness to challenge the “objectivity” of many commentators and advisors in the forecasting industry. He also takes a reasoned look at the common dogmas of our profession, with an eye to encouraging what can work, while exposing the marketing fluff and shoddy thinking of the really bad ideas. He is not just another cheerleader hyping the latest forecasting buzzword or fad.”

To see the full review see the link.

Areas of Focus in Supply Chain Forecasting Enterprise Software

Here is a sampling of what the book covers:

  • Explains how consensus based forecasting software and statistical forecasting software compare to one another.
  • Why most companies are using forecasting systems that have poor hierarchical capabilities.
  • The concept of virtual hierarchies versus static hierarchies and why virtual hierarchies are so preferable.
  • Innovation in the data layer that supports forecasting applications.
  • What makes a statistical method “statistical.”
  • How to perform demand forecasting for lumpy items (and how it relates to supply planning), with examples of how general demand lumpiness is increasing and reducing forecast accuracy.
  • How and why the IT department can and should be taken out of the role of maintaining the attribute database, and what this means for increasing the flexibility of the forecasting system.
  • How to enabled causal forecasting in your company using a new technique that leverages attributes. Rather than the traditional technique requiring placing regression formulas into the forecasting application.
  • How to easily created forecasts by customer and how this can support service level agreements (SLAs).
  • How to quickly perform top-down forecasts.
  • How different vendors approach lifecycle planning and a novel approach to lifecycle forecasting, which significantly reduces the effort involved.
  • How to determine what component of your product database is unforecastable, and what to do about unforecastable products.
  • Why big consulting companies often cannot help you select the appropriate forecasting application for your needs.
  • How Lean applies to forecasting.
  • How the Lewandowski forecast algorithm relates to forecasting simulation.
  • Why supply chain and product database simplification is critical to improving forecast accuracy.
  • Why it is essential to produce a naive forecast (as a simulation) for determining how to leverage best-fit functionality.
  • What is forecast simulation, and how it can improve decision-making?
  • Examples from Netflix, Blockbuster Video, Nike, Trader Joe’s, the grocery industry, publishing, and service parts.

A Book Based in Reality, Emphasizing Actionable Knowledge

The book provides many examples from real-life project experiences, the emphasis being on the reality of supply chain forecasting projects. While the majority of forecasting books spend a lot of time covering forecasting methods that companies will very rarely (if ever) use, this book adjusts the content to cover topics that can be implemented by companies.

Interconnected to Web Information

In order the keep the book at a manageable and easily readable length, the book also provides numerous links out to the Brightwork Research & Analysis site, where supporting articles allow readers to get into more detail on topics that interest them.

Case Studies

The book also contains case studies of how various forecasting techniques were used on different projects.

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  • Chapter 1: Introduction
  • Chapter 2 Where Demand Planning Fits within the Supply Chain Planning Footprint
  • Chapter 3: Statistical Forecasting Explained
  • Chapter 4: Why Attribute-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 Demand Planning Software
  • Chapter 14: Conclusion

Table of Contents

Chapter 1: Introduction

  • The Importance of Software Screenshots and Vendor Diversity
  • How Writing Bias is controlled at Brightwork Research & Analysis an Brightwork Research & Analysis Press
  • Making the Perfect Book for Those Hungry for Precise Information on Demand Planning Software
  • Why Are There So Few Books on Enterprise Demand Planning Software?
  • Why Are There So Few Books on Enterprise Consensus Based Forecasting?
  • The Brightwork Research & Analysis Site
  • Who Is This Book For?
  • Abbreviations

Chapter 2 Where Forecasting Fits within the Supply Chain Planning Footprint

  • Background
  • Where Demand Planning Software within Supply Chain Software
  • The Demand Planning Application Categories
  • The Forecast Category, Method and Model Employed
  • Connecting the Demand Planning System to Supply Planning
  • Conclusion

Chapter 3: Statistical Forecasting Explained

  • The Initial Attempts at Demand Planning Software Implementation
  • What Makes Statistical Forecasting “Statistical”?
  • Statistical Forecasting Versus Other Forms of Statistics Such as Polling
  • Statistical Forecasting and Inferential Statistics
  • The Relationship between Probability Theory and Statistics
  • Statistical Forecasting Methods
  • Time Series Techniques
  • Exponential Smoothing
  • Regression or Causal Forecasting
  • The Actual use of Regression or Causal Forecasting
  • Correlation Versus Causation
  • High-Level Causal Models
  • Implementing Causal Forecasting
  • Best-Fit Functionality
  • The General Confusion on Best Fit Functionality
  • Best-Fit Functionality and the Selection of the Constant Model
  • Best-Fit Statistics
  • Manual Adjustments to the Statistical Forecast
  • User Adoption Issues and the Type of Forecasting System Selected
  • Conclusion
  • Case Study: Forecast Parameter Management
  • Case Study: How Sales Teams Often Circumvent Client User Feedback

Chapter 4: Why Attributes Based Forecasting is the Future of Statistical Forecasting

  • Background
  • Understanding Static Hierarchies for Demand Planning
  • Attribute-Based Forecasting
  • Attributes in Demand Works Smoothie
  • Adjusting Smoothie’s Virtual Hierarchy
  • Creating the Attributes in the Application Database
  • Forecasting by Customer
  • Performing the Top Down Forecast
  • Forecasting by the Customer and Service Level Agreements
  • Can Multi-Attribute Forecasting Be Performed?
  • Forecast Disaggregation
  • Increasing or Decreasing the Forecast for a Product Group by Attribute
  • Conclusion
  • Case Study: Attribute-Based Forecasting

Chapter 5: The Statistical Forecasting Data Layer

  • Background
  • The Importance of Technology in the Data Layer for Forecasting
  • What are MOLAPs?
  • What is a ROLAPs / Star Schema?
  • MOLAPs and ROLAPs / Start Schemas in Both Forecasting and Analytics
  • Common Problems with MOLAPs and Star Schemas
  • The Workaround of the External Realignment Table
  • Getting Data Into the Forecasting System with Attribute-Based Forecasting and Specially Tuned ROLAPs
  • Historical Adjustment
  • Historical Adjustment in the Demand Planning Application
  • Conclusion

Chapter 6: Removing Demand History and Outliers

  • Historical Removal
  • What Happens to Demand History Data?
  • How to Perform Historical Removal Testing
  • Automating the Historical Removal Test
  • Outlier Removal
  • Outlier Management in Demand Planning Systems
  • Determining Whether Outliers Should be Removed
  • Conclusion
  • Case Study: Historical Removal

Chapter 7: Consensus Based Forecasting Explained

  • Background
  • Where Consensus Based Forecasting Originated
  • The Rise of Prediction Markets for CBF
  • A Different Model for Obtaining Consensus Input
  • Aggregation Using Consensus Point
  • Making SaaS Work, and Enabling Collaboration
  • Disaggregation with CBF
  • Inkling Markets
  • The Current Thinking on CBF
  • The Origins of Forecasting Myths
  • A More Accurate Way to Think About CBF
  • The Historical Problem with Consensus Methods
  • CBF and Bias
  • The Sales Forecast
  • The S&OP Forecast
  • The MPS and S&OP
  • S&OP Software
  • Why Forecasting Software is Only One Part of an S&OP Analysis
  • Constraint Evaluation
  • Sensitivity Analysis
  • Shadow Prices
  • Demand Side S&OP
  • Steered to the Inappropriate Solutions CBF Vendors by the Major Consulting Companies
  • Where Major Vendors Stand with CBF
  • Conclusion
  • Case Study: Product Database Segmentation

Chapter 8: Collaborative Forecasting Explained

  • Collaborative Forecasting
  • Collaborative vs. Consensus Forecasting
  • Types of Forecast Collaboration Software
  • File-Based Forecast Collaboration
  • Non-EDI File-Based Forecast Collaboration
  • Application Based Forecast Collaboration
  • The Appropriate Application Design for Collaboration
  • Controlling the Collaborative Forecasting Input
  • Collaborative Forecasting vs. CPFR
  • Conclusion

Chapter 9: Bias Removal

  • Introduction
  • What is Bias?
  • Financial Industry Forecast Bias
  • Bias Accounted for at the UK Department of Transportation
  • Bias Identification Within the Application
  • The Importance of Distinct Bias Removal Workflow and Functionality
  • Using Bias Removal as Forecast Improvement Strategy
  • Conclusion

Chapter 10: Effective Forecast Error Management

  • Forecast Error Measurement Methods
  • Understanding The Contextual Factors of Forecast Error
  • The Forecast Error Context of Aggregation
  • The Forecast Error Context of Product Type
  • The Forecast Error Context of the System Generated Versus the Final Forecast
  • The Forecast Error Context of Location
  • The Forecast Error Context of Duration
  • The Lead Time Demand
  • Using the Naïve Forecast for Baseline-ing
  • How to Perform a Naïve Forecast Comparison
  • Conclusion

Chapter 11: Lifecycle Planning

  • Background
  • Lifecycle Planning in SAP DP
  • Lifecycle Planning in ToolsGroup
  • Lifecycle Planning in JDA Demand Management
  • Copying Demand History Between Like Products
  • Lifecycle Planning (and Other Adjustments) in Demand Works Smoothie
  • Method 1: Copy Paste Spreadsheet Functionality in Smoothie for Lifecycle Planning
  • Method 2: Setting Life Cycle as an Attribute
  • Conclusion

Chapter 12: Forecastable Versus Unforecastable Product

  • The Inconvenient Truth About Statistical Forecasting
  • Intermittent or Lumpy Demand
  • ToolsGroup and Lumpy Demand
  • Trends in Lumpiness 1: Trader Joe’s Versus Normal Supermarkets
  • Trends in Lumpiness 2: Netflix and the Long Tail
  • Dealing with Lumpy Demand with Complex Methods
  • Examples of Unforecastable Demand
  • Research into Methods for Dealing with Lumpy Demand
  • The Right Approach for Dealing with Difficult to Forecast Products
  • Is It Necessary to Actively Forecast the Entire Product Database?
  • How to Identify Unforecastable Products
  • Excluding Dependent Demand Products from the Analysis
  • Managing Products with No Forecast with Supply Planning
  • Conclusion

Chapter 13: Why Companies Select The Wrong Forecasting Software

  • Background
  • Obtaining Quality Information on Demand Planning Software
  • The False Dichotomy of Consensus vs. Statistical Methods
  • Conclusion

Chapter 14: Conclusion

  • Introduction
  • Attribute-Based Forecasting
  • Different Forecasting Software for Different Forecasting Processes
  • Bias Removal
  • Forecastablility
  • The Future

Appendix A:

  • Saving the Prior Forecast for Forecast Adjustment
  • Using Measures to Store Different Forecasts
  • Comparative Measures for Testing

Appendix B:

  • Forecast Locking

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Questions about the Book? 

Do you have any questions about the book? If so, please comment below, and we will address your issues.