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 [...]
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. […]
An outlier is a data point that falls outside of the expected range of the data (i.e., it is an unusually large or small data point). If you ignore outliers in your data, there is a danger that they can have a significant adverse impact on your forecasts. This article surveys three different approaches to [...]
When you use a statistical model to generate a 12-month forecast, you get more than just twelve numbers. You also get a great deal of information about how the forecast was generated, the model’s fit to the historic data and different measures of expected forecast accuracy. In this article, we [...]
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. […]
Preparing forecasts using data that contain one or more unusually large or small demand periods can be challenging. Depending on your forecasting approach, these “outliers” can have a significant impact on your forecasts. This article surveys three different approaches to forecasting data containing unusual demand periods, discusses the pros and cons of each and recommends [...]
Human review of a statistically-generated forecast is an important step in the forecast process. Ideally, every statistical forecast should be inspected for plausibility. At times, the sheer volume of the forecasts being generated precludes exhaustive individual inspection. In these instances, exception reports are an effective tool to help you sift through the forecasts and focus [...]
Demand history can provide forecasters with key insights into current trends, seasonal patterns and the relationships between demand and explanatory variables. Thus, creating forecasts when little or no demand history is available is particularly challenging. In this installment of Forecasting 101 we’ll examine different approaches to creating forecasts when little or no demand history is available. [...]
A forecasting technique which generates a forecast based solely on an item’s past demand history is referred to as a time series method. Typically, time series methods will capture structure in the history—such as current sales levels, trends and seasonal patterns—and extrapolate them forward. When the data are not trended and are not seasonal, a time [...]
Many organizations need to generate forecasts at very detailed levels. For example, a consumer products company may need an SKU-by-customer forecast, a shoe manufacturer may need a shoe-by-size forecast, or an automobile manufacturer may need a parts-level forecast. One approach to generating low-level forecasts is to apply statistical forecasting methods directly to the lowest-level demand [...]
