Aphorism 6: The Surest Way to Get a Better Forecast is to Make the Demand Forecastable Forecast accuracy is largely dependent on volatility of demand, and demand variation is affected by our own organizational policies and practices. So an underused yet highly effective solution to the forecasting problem can be
Aphorism 3: Organizational Policies and Politics Can Have a Significant Impact on Forecasting Effectiveness We just saw how demand volatility reduces forecastability. Yet our sales, marketing, and financial incentives are usually designed to add volatility. We reward sales spikes and record weeks, rather than smooth, stable, predictable growth. The forecast
The Aphorisms of the New Defensive Paradigm I want to finish this blog series with a set of 7 aphorisms – concise statements of principle – that characterize the new Defensive paradigm for business forecasting. The first is that: Aphorism 1: Forecasting is a Huge Waste of Management Time This
Academic Research In an approach akin to FVA analysis, Paul Goodwin and Robert Fildes published a frequently cited study of four supply chain companies and 60,000 actual forecasts.* They found that 75% of the time an analyst adjusted the statistical forecast. They were trying to figure out, like FVA does,
Typical Business Forecasting Process Let’s look at a typical business forecasting process. Historical data is fed into forecasting software which generates the "statistical" forecast. An analyst can review and override the forecast, which then goes into a more elaborate collaborative or consensus process for further adjustment. Many organizations also have
The Means of the Defensive Paradigm The Defensive paradigm pursues its objective by identifying and eliminating forecasting process waste. (Waste is defined as efforts that are failing to make the forecast more accurate and less biased, or are even making the forecast worse.) In this context, it may seem ridiculous
Why the Attraction for the Offensive Paradigm? In addition to the reasons provided by Green and Armstrong, I'd like to add one more reason for the lure of complexity: You can always add complexity to a model to better fit the history. In fact, you can always create a model
Implications for the Offensive Paradigm The worldview promulgated by the Offensive paradigm is that if we only had MORE – more data, more computational power, more complex models, more elaborate processes – we could eventually solve the business forecasting problem. But this just doesn’t seem to be the case. Operating
Is Complexity Bad? It’s necessary to point out that Goodwin’s article is not arguing against complexity per se, and I’m not either. When you have a high value forecast, where it is critical to be as accurate as possible, of course you are going to want to try every technique
Anomalies: The Beginning of a Crisis While even trained scientists can fail to see things that fall outside what they are looking for, anomalies eventually start to get noticed. But still, for a long time, anomalies within an existing paradigm are seen as mere “violations of expectation.” The response within
The Current Paradigm for Business Forecasting So what is the current paradigm that we, the community of business forecasting practitioners and researchers, are operating under? I’d argue that for at least the last 60 years, since 1956 when Robert G. Brown published his short monograph Exponential Smoothing for Predicting Demand,
In a February 2015 post Offensive vs. Defensive Forecasting, I sought to distinguish two very different approaches to the business forecasting problem: Offensive: The "offensive" forecaster is focused on forecast accuracy -- on extracting every last fraction of a percent of accuracy we can hope to achieve. The approach is
