The purpose of a smarter forecasting process is to identify and evaluate systematically all factors, which are most likely to affect the course of demand to produce a realistic view of the future. At first glance, such a process may seem inefficient and interminable; but in practice, you will discover that a reasonable course will often become apparent, especially with experience using predictive visualization.

Example 1. A predictive visualization of the impact of a driver of demand..

Uncertainty is a certain factor in characterizing change and chance.

To summarize the impact of a driver on demand, I create a predictive visualization of the driver or factor by relating a score to the size of a dartboard surface.

The direction would indicate the impact a factor would have on demand if it were increasing or decreasing. For example, price increases would tend to result in decreases (-) in demand, while increases in holidays and festivals would tend to increase (+) the demand for most consumer products.

The quantification of the resulting impact is based on informed judgment and domain expertise and signifies the intensity on a numerical scale, say 1 to 5. To create the display, use the relationship of Area = π (radius)2 to determine the size of the circles shown in the diagram above. For instance, for a score of 5, the circle should appear five times larger than the one with a score of 1. To get the correct visual effect, you can accomplish this as follows: Score 1 equals radius 1, Score 2 equals radius √ 2. Score 3 equals radius √ 3, Score 4 equals radius 2 and Score 5 equals radius √ 5. If you have deeper domain knowledge, you can extend the scale to 7 or 11. 

Example 2. Prediction-Realization Diagrams – A Useful Tool to Compare Forecasting Performance in Sales Regions

Another useful visual approach is in monitoring forecasting and forecaster performance with the prediction-realization (PR) diagram introduced in 1958 by the economist Henri Theil (1924–2000). If the predicted values are indicated on the vertical axis and the actual values on the horizontal axis, a straight line with a 450 slope will represent perfect forecasts. This is called the line of perfect forecasts, shown below. In practice, the PR diagram is sometimes rotated so that the line of perfect forecasts appears horizontal.

The diagram has six sections. Points falling in sections II and V are a result of turning-point errors. In Section V, a positive change was predicted, but the actual change was negative. In Section II, a negative change was predicted, but positive change occurred.

The remaining sections involve predictions that were correct in sign but wrong in magnitude. Points above the line of perfect forecasts reflect actual changes that were less than predicted. Points below the line of perfect forecasts represent actual changes that were greater than predicted.

The prediction-realization diagram can be used to record forecast results on an ongoing basis. Persistent over-runs or under-runs indicate the need to adjust the forecasts or to re-estimate the mode. In this case, a simple error pattern is evident and we can raise or lower the forecast based on the pattern and magnitude of the errors.

The prediction-realization diagram shows at a glance how well you did in getting the direction of a forecast correct. The original diagram can be enhanced to include an assessment of whether the forecasts can be improved by comparisons with naïve (no-change) forecasts. Professor Roy Pearson (personal communication) has added a line, labeled U=1, where U is the Theil statistic used in economic forecasting https://www.youtube.com/watch?v=fhjJWI7rw2A). This line has a slope of two times the actual change. Predictions falling on this line have the same error as predictions from a no change forecast, which is frequently referred to as a naïve or NAÏVE_1 forecast. Points along the horizontal axis (other than the origin itself) also are outcomes where U=1, since the forecaster is predicting no change, exactly the same as a naive forecast.

More important, the diagram indicates turning-point errors that may be due to misspecification or missing variables in the model. The forecaster may well be at a loss to decide how to modify the model forecasts. An analysis of other factors that occurred when the turning-point error was realized may result in inclusion of a variable in the model that was missing from the initial specification

The prediction-realization diagram indicates how well a model or forecaster has predicted turning points and also how well the magnitude of change has been predicted given that the proper direction of change has been forecast. 

Demand forecasting is a structured process that produces a specific output, namely advice about the future. Because the future is not completely predictable, I describe in my book the systematic structure of a smarter forecasting process to establish the foundation on which the most important ingredient (human judgment and intuition) is based.

Additional examples of predictive visualization for forecasting applications are provided in my book available in paperback and Kindle eBook on Amazon. In addition, through Delphus, we conduct onsite training and professional development workshops. The Workshop Manual for Smarter Forecasting and Planning is also available on Amazon.

Hans Levenbach, PhD is Executive Director, CPDF Training and Certification Programs. He conducts hands-on Professional Development Workshops on Demand Forecasting and Planning for multi-national supply chain companies worldwide. Hans is a Past President, Treasurer and former member of the Board of Directors of the International Institute of Forecasters. He is group manager of the LinkedIn groups (1) Demand Forecaster Training and Certification, Blended Learning, Predictive Visualization, and (2) New Product Forecasting and Innovation Planning, Cognitive Modeling, Predictive Visualization.

I invite you to join these groups and share your thoughts and experiences.