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.
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.
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 (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 forecasting data containing outliers, discusses the pros and cons of each and makes recommendations about when it is best to use each approach.
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 when it is best to use each approach.
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 dissect and catalogue the different components of a statistical forecast.
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 on the items where human attention is most needed.
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 series method will often generate a flat-line forecast reflecting the current sales level. Because a flat line is often an implausible scenario for the future, delivering a flat-line forecast to management may require explaining the distinction between a scenario for the future and a statistical forecast of the future. This article explains this distinction and discusses when a flat-line forecast is and is not appropriate.