In a 2010 white paper from IBM the authors, Steve Player and Steve Morlidge, blame antiquated processes and tools combined with misconceptions about forecasting accuracy for the “illness” of most forecasting systems in business today. In their paper Seven Symptoms of Forecasting Illness (© IBM 2010) they point to 7 symptoms of potential forecasting trouble, which we’ll highlight below.
- Semantic Confusion: Does your firm find it uncomfortable to cope with unexpected or unwelcomed forecasts? Like the manager who is asked for a “best estimate” and then is held accountable for it, or criticized for “making changes” in a forecast update, or one which management doesn’t like. The bottom line is the blurring between “forecasts” and “targets” (or goals). Resolution involves more honest, open and direct communication from the top down.
- Visual Impairment: Are you obsessed with the year-end forecast number to the exclusion of everything else? Or surprised by early-year unexpected developments? The root cause here tends to be inflexibility or lack of adaptability to changing conditions, and is best resolved via use of a rolling (i.e., cumulative/adjusted monthly or quarterly) type of forecast.
- Delusions of Accuracy: Are you obsessed with accuracy, pouring too much quality managerial time and talent agonizing over a forecast’s development, trying to hit it on the nose? The price paid for error here may include bonuses, promotional expense, or losing sight of what’s possible “as internal views obscure external learning.” Here again, forecasting more periodically and being quickly adaptive to outside changes will help.
- Systemic Overload: Are forecasts too detailed? Is there too much pressure to provide greater detail and analysis? These cause the system to become bloated and unwieldy in a downward spiral of frustration. The fallacy is in believing that more data is always better. Instead, the authors opine, limit your forecasting to a few key critical drivers that truly affect company performance. Use the 80/20 rule, and emphasize analysis over data gathering in extremis.
- Prosperity Syndrome: Do your forecasts tend to trend up over-optimistically regardless of industry or conditions? Are they too biased toward growth? Ignoring the reality of industry or economic cycles exposes any firm to strategic missteps. Don’t mistake a happy event for a trend. And above all, don’t neglect your key customer-satisfying strategic differentiators. Instead, recognizes your biases, focus on current, key market and economic realities, and competitive realities.
- Lack of Coordination: Are forecasting views internally characterized by chaos and conflict? Do different departments see the future differently? Are managers’ biases reflected in their (conflicting) forecasts? Lack of integration by management of key forecasting projections is at the root. A system is required company-wide that users can believe and have faith in – one that does not discourage differing views, and inspires collaboration.
- Asocial Behavior: Does the firm routinely manipulate or distort forecasts even when not in the company’s long-term best interests? Is knowledge withheld or manipulated? You could end up rewarding sand-baggers and masking problems (or opportunities) in the marketplace and obscure the firm’s true potential. If so, take a look at bonuses, comp and reward systems for undesirable links between forecasting and performance, or incentives not well aligned with overall company goals. Reward employees for the value they create rather than the targets they negotiate.
The full IBM White Paper can be found at the website of proformative.com. You should be able to find the link here.