International Symposium on Forecasting (ISF)
Boulder, Colorado, USA
June 17 – 20, 2018
Early bird deadline: March 31, 2018
The International Symposium on Forecasting (ISF) draws the world’s top forecasters and researchers to learn about and discuss cutting-edge developments in forecasting. Sponsored annually by the International Institute of Forecasters (IIF), the 38th conference in Boulder, CO features a “Forecasting in Practice” track offering professional development to business forecasting practitioners.
In addition, Eric Stellwagen will present the workshop Business Forecasting: Techniques, Applications and Best Practices offering practitioners a deep dive into proven forecasting methods and demonstrating practical solutions that can be applied in their own organizations.
Visit the ISF Website for details on the full conference agenda and to take advantage of the early bird discount for registering by March 31, 2018.
Sales and demand forecasters have a variety of techniques at their disposal to predict the future. While most analysts will examine historical sales or other kinds of data as a guide, many forecasters rely heavily on judgment. There’s no question that judgment can (and probably should!) play a significant role in arriving at your final, consensus forecast–but statistical forecasting can offer a level of automation and insight that can substantially improve your forecast accuracy, particularly when you are producing large quantities of forecasts on a rolling basis.
This article covers two common approaches for forecasting sales using statistical methods: time series models and regression models. The advantage of these approaches is that they offer a lot of “bang for your buck”. On one hand, they are robust methods that can detect and extrapolate on patterns in your data like seasonality, sales cycles, trends, responses to promotions, and so on. On the other hand, they are easily accessible approaches, especially with the right tools.
Join us in Orlando, Florida for a comprehensive three-day educational seminar Business Forecasting: Techniques, Applications and Best Practices on March 7-9, 2018.
If your organization is introducing new products in 2018, you’ll want to join our free educational Webinar Effective Methods for Forecasting New Products on January 25, 2018 at 1:30 pm EST (UTC -5:00). We know that forecasting new product sales is a challenge, but you can hit the ground running after learning about the pragmatic approaches reviewed in this live one-hour Webinar.
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 educational Webinar How to Leverage Forecasting and a Demand Control Process to Improve Customer Service presented by Business Forecast Systems and Oliver Wight is now available to view on demand.
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.
A Time Series 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.