Join us in Orlando, Florida for a comprehensive three-day educational seminar Business Forecasting: Techniques, Applications and Best Practices on March 29-31, 2017.
Sometimes making changes to near-term forecasts can be an expensive proposition. Last minute changes often significantly increase production and procurement costs, decrease profitability, and negatively impact other aspects of the business. To protect against these effects, many companies establish “time fences” to prohibit changes to the forecast over a defined short-term horizon. This edition of Tips & Tricks details how to set time fences in Forecast Pro TRAC to prevent new statistical forecasts from being generated and/or new forecast overrides from being applied within the fenced horizon.
The newly-published reference book Business Forecasting: Practical Problems and Solutions compiles important and influential literature in the field into a single comprehensive resource for improving your forecasting process.
In the 19th century Dr. Wilfredo Pareto, an Italian economist, gave birth to the “80/20 rule” when he observed that 80% of the country’s wealth was held by 20% of the population. Today, many organizations find that the 80/20 rule (or a similar ratio) applies to their products—80% of their revenue comes from 20% of their products.
Time series methods are forecasting techniques that base the forecast solely on the demand history of the item you are forecasting. They work by capturing patterns in the historical data and extrapolating those patterns into the future. Time series methods are appropriate when you can assume a reasonable amount of continuity between the past and the future. They are best suited to shorter-term forecasting (say 18 months or less). This is due to their assumption that future patterns and trends will resemble current patterns and trends. This is a reasonable assumption in the short term but becomes more tenuous the further out you forecast.
In Forecast Pro TRAC, you have the ability to import externally-generated forecasts into the override grid view. We can choose which of these forecasts we want to use as our “baseline” forecast. This can be done either on an item-by-item basis or for groups of items. If we have specified a baseline forecast that is different than the statistical forecast, we can also use the tracking reports in Forecast Pro to track accuracy for our baseline forecasts separately from our statistical and/or our adjusted forecasts.
Error measurement statistics play a critical role in tracking forecast accuracy, monitoring for exceptions, and benchmarking your forecasting process. Interpretation of these statistics can be tricky, particularly when working with low-volume data or when trying to assess accuracy across multiple items (e.g., SKUs, locations, customers, etc.). This installment of Forecasting 101 surveys common error measurement statistics, examines the pros and cons of each and discusses their suitability under a variety of circumstances.