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.
Forecasters dealing with large, complex hierarchies face the challenge of managing thousands, or even tens of thousands, of forecasted items. In order to streamline navigation and to provide flexibility in reporting, Forecast Pro offers the “Hot List.” The Hot List is an easy-to-use utility for creating subsets of your data for viewing, applying modifiers and reporting. Taking advantage of the Hot List can save you time and effort.
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.
It is easy to understand why most Forecast Pro users rely heavily on the software’s expert selection mode to generate their forecasts–expert selection automatically analyzes their data, selects the appropriate forecasting techniques and generates the forecasts. This article discusses how the length of a data set can influence your decision to choose expert selection or an alternative approach for forecasting your data.
Forecast Pro is used in a wide variety of businesses and organizations worldwide. While often used for sales forecasting, the software is also widely deployed in the government sector, across the federal, state, and municipal levels. Forecast Pro was recently featured in the US Government Finance Officers Association (GFOA) newsletter, in an article highlighting some of the features that make Forecast Pro an excellent solution for government organizations.
To meet its customers’ needs, Enesco Limited—a worldwide distributor of gifts, home décor and collectables—has the challenging task of creating accurate forecasts for a large number of SKUs. In this case study, you will learn how the UK-based company shortened its lead time for existing products, cut inventory and reduced product shortages by implementing Forecast Pro.
Many Forecast Pro users rely on the software’s expert selection capability to choose the appropriate forecasting techniques and generate the forecasts, and then “tweak” the forecasts as needed. One way to adjust a forecast is to change the forecasting method used; when you do this, a “forecast modifier” appears next to the item in the Navigator to record and maintain your selection. A second option for adjusting the statistical forecast is to apply overrides.
In this edition of Tips and Tricks, we explain how to import overrides and modifiers into Forecast Pro directly from Excel which can save substantial time if you work with large data sets.
Forecast Pro includes a forecasting approach called seasonal simplification. Seasonal simplification is an extension of exponential smoothing which “simplifies” the modeling of the seasonal pattern by reducing the number of indices used. In many cases the seasonally simplified model can substantially improve forecast accuracy.