The Smart Forecaster
Pursuing best practices in demand planning,
forecasting and inventory optimization
Companies launch initiatives to upgrade or improve their sales & operations planning and demand planning processes all the time. Many of these initiatives fail to deliver the results they should. Has your forecasting function fallen short of expectations? Do you struggle with “best practices” that seem incapable of producing accurate results?
For ten years, the editorial team at Foresight: The International Journal of Applied Forecasting has been telling readers about the struggles and successes of forecasting professionals and doing all we can to educate them about methods and practices that really work. We do that with articles contributed by forecasting professionals as well as respected academics and authors of highly-regarded books.
As Founding Editor of Foresight, I’d like to invite you to join us for the upcoming Foresight Practitioner Conference entitled “Worst Practices in Forecasting: Today’s Mistakes to Tomorrow’s Breakthroughs.”
This 1.5-day event will take place in Raleigh, North Carolina, October 5-6. There we will take a hard look at common practices that may be inhibiting efforts to build better forecasts. Our invited speakers will share how they and others have uncovered and eliminated bad habits and worst practices in their organizations for dramatic improvements in forecasting performance.
Some of the topics to be addressed include:
• Use and Abuse of Judgmental Overrides
• Avoiding Dangers in Sales Force Input to Forecasts
• Improper Practices in Inventory Optimization
• Pitfalls in Forecast Accuracy Measurement
• Worst Practices in S&OP and Demand Planning
• Worst Practices in Forecasting Software Implementation
Foresight is published by the non-profit International Institute of Forecasters (IIF), an unbiased, non-commercial organization, dedicated to the generation, distribution and use of knowledge on forecasting in a wide range of fields. (Smart Software’s own Tom Willemain serves on Foresight’s Advisory Board.) Foresight is just one of the resources made available by the IIF. Additional publications, a host of online resources, an annual symposium and periodic workshops and conferences are available to all IIF members. The Smart Forecaster previously interviewed IIF past-president Dr. Mohsen Hamoudia. Visit the IIF site for information about joining.
(Len Tashman is the editor of Foresight: The International Journal of Applied Forecasting. The unusual practice-related conference he describes, upcoming in October 2016, will appeal to many of readers of The Smart Forecaster. For instance, those who have received Smart Software’s training have been alerted to the possibility that overriding statistical forecasts can backfire if done cavalierly. Two sessions at the conference focus on the use of judgement in the forecasting process. — Tom Willemain)
Just-In-Time (JIT) ensures that a manufacturer produces only the necessary amount, and many companies ignore the risks inherent in reducing inventories. Combined with increased globalization and new risks of supply interruption, stock-outs have abounded. So how can you execute a real-world plan for JIT inventory amidst all this risk and uncertainty? The foundation of your response is your corporate data. Uncertainty has two sources: supply and demand. You need the facts for both.
Consider the problem of replenishing inventory. To be specific, suppose the inventory item in question is a spare part. Both you and your supplier will want some sense of how much you will be ordering and when. And your ERP system may be insisting that you let it in on the secret too.
In this video, Dr. Thomas Willemain, co-Founder and SVP Research, talks about improving Forecast Accuracy by measuring Forecast Error. We begin by overviewing the various types of Error Metrics: Scale-dependent error, Percentage error, Relative error, and Scale-free error Metrics. While some error is inevitable, there are ways to reduce it, and forecast metrics are necessary aids for monitoring and improving forecast accuracy. Then we will explain the special problem of intermittent demand and divide-by-zero problems. Tom concludes by explaining how to assess forecasts of multiple items and how it often makes sense to use weighted averages, weighting items differently by volume or revenue.