Forecasting Education

How do you use Statistical Models to Forecast Sales?

2018-02-22T09:17:36+00:00February 22nd, 2018|Categories: Forecasting Education|Tags: , , , , , |

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, [...]

Utilizing Time Fences in Forecast Pro TRAC

2016-11-17T14:48:28+00:00November 17th, 2016|Categories: Forecasting Education, Using Forecast Pro|Tags: , , |

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 & [...]

How to Leverage Forecasting and a Demand Control Process to Improve Customer Service

2016-02-17T11:44:18+00:00February 17th, 2016|Categories: Forecasting Education, Latest News and Events|Tags: , , , , |

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. […]

Understanding Pareto (ABC) Analysis

2015-12-15T16:23:47+00:00December 15th, 2015|Categories: Forecasting Education|Tags: , , , |

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 [...]

What are Time Series Methods and When Should I Use Them?

2015-08-17T12:27:18+00:00August 17th, 2015|Categories: Forecasting Education|Tags: |

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 [...]

Working with Alternative Baseline Forecasts

2015-05-11T12:39:06+00:00May 11th, 2015|Categories: Forecasting Education, Using Forecast Pro|Tags: , , , |

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 [...]

A Guide to Forecast Error Measurement Statistics and How to Use Them

2015-04-02T15:10:33+00:00April 2nd, 2015|Categories: Forecasting Education|Tags: , , , , |

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 [...]

Using Seasonal Simplification to Improve Forecasts

2014-11-10T12:16:40+00:00November 10th, 2014|Categories: Forecasting Education|Tags: , |

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. […]

Box-Jenkins Forecasting

2014-08-13T14:41:16+00:00August 13th, 2014|Categories: Forecasting Education|Tags: , , |

Box-Jenkins (ARIMA) is an important forecasting method that can yield highly accurate forecasts for certain types of data. In this installment of Forecasting 101 we’ll examine the pros and cons of Box-Jenkins modeling, provide a conceptual overview of how the technique works and discuss how best to apply it to business data. […]