Forecasting is a fundamental business task, yet few companies perform it efficiently and well – or at least not as efficiently and well as they would like! If our attention is misdirected to the current fads, hype, and peripheral issues, we can lose focus on the fundamentals that truly impact effective forecasting. This article provides a few basic guidelines for business forecasting in the form of aphorisms – concise statements of principle. These seven statements as well as their corollaries and lessons are meant to raise awareness of underlying issues and direct effort into key areas for improvement.
Forecasting is a huge waste of management time. This is not to imply that forecasting is unnecessary and should be eliminated. Clearly, good forecasts can help a company become more effective in utilizing its resources and satisfying its customers. Rather, this aphorism is meant to highlight the inordinate amount of company resources invested in the typical forecasting process, and to question whether all this effort is really making a worthwhile difference.
Unless your company runs in a totally automated forecasting mode, management resources are involved in the forecasting process. At minimum, this includes the forecast analysts and demand planners who manage the statistical forecasting models and provide manual overrides. In the usual situation, however, forecasting draws time and attention from areas such as sales, marketing, finance, operations, and executive management. This is high-cost management time. Are these forecasting participants skilled and can they improve the forecast? Or are they contaminating what should be an unbiased and scientific process with their politics, wishes, and personal agendas?
MAPE and the other standard performance metrics cannot detect waste in the forecasting process. Instead, Forecast Value Added (FVA) analysis is used to identify the change in performance metrics caused by particular steps or participants in the forecasting process. Efforts that reduce MAPE are adding value by making the forecast better. Efforts that fail to reduce MAPE are simply wasteful and need to be removed from the process.
Lesson: Measure the performance of each step and of each participant in your forecasting process. Identify and mercilessly eliminate non-value adding activities. The result will be better forecasts with less effort.
Accuracy is determined more by the nature of the demand pattern being forecast than by the specific method being used to forecast it. Under favorable conditions, demand can be forecast accurately with simple techniques. At other times, we can never quite reach the level of accuracy we want, no matter how much data, statistical analysis, and human intervention we employ. This is not our fault – it is simply the reality of dealing with randomness and variation in demand.
The Coefficient of Variation (CV) is the metric for expressing demand variation (or “volatility”). CV, which is expressed as a percentage, is the ratio of a pattern’s standard deviation to its mean. For example, if demand for item XYZ averages 100 units and the standard deviation of the demand is 30 units, then the “demand volatility” is CV = 30%. CV exceeding 100% is not unheard of at granular levels of detail, such as the retailer’s Store / SKU. A 50% CV would be common for a consumer products manufacturer at the Warehouse / SKU level.
Highly seasonal, greatly promoted, and short lifecycle “fashion” items will have higher volatility than long running core or “basic” items – making it no coincidence that fashion items are more difficult to forecast! Note that both volatility and forecast error will decrease as you aggregate granular level demand to higher levels in the organizational hierarchy. This is because the ups and downs of granular level demand (as well as granular level forecast errors) will cancel each other out in the total, resulting in “smoother” demand patterns at higher levels as well as easier-to-forecast demand.
A visual representation of the general relationship between demand volatility and forecast accuracy is easy to create. Simply calculate the CV and MAPE for each of your items and graph the points on a scatter plot. You will see that the higher the volatility, the greater the forecast error.
Lesson: Be careful when interpreting forecasting performance benchmarks. The accuracy achieved by those companies with the best results may be due more to the forecastability of their demand patterns than to the excellence of their forecasting process. It is a worthwhile exercise to benchmark your own forecasting performance against what you would have achieved with a naïve forecast.
Do not set arbitrary forecasting performance goals. Goals for forecasting performance must be based on the demand forecastability. Arbitrary performance goals may be set too low, thereby rewarding inferior performance. Goals that are set unrealistically high will demoralize the forecasting staff and encourage cheating. Consider these two scenarios:
- Your job each day is to forecast the results of a fair coin toss (heads or tails). Over several years, you have consistently achieved 50% accuracy in your forecasts. Management is dissatisfied with your results and gives you a new goal of 60% accuracy or you will be fired. What would you do next?
- You are responsible for forecasting the sales of a popular product that has a volatile demand. Your bonus is based purely on forecast accuracy, but management has set unrealistically high accuracy goals. How would you maximize your bonus?
In the first scenario, you can either resign or you can wait around long enough to be fired and receive a severance package! By the nature of the pattern you are forecasting (tossing a fair coin) you are doomed to failure – you will never consistently achieve 60% accuracy.
In the second scenario, you may be able to cheat your way to highly accurate forecasts by constraining supply. Since this is a popular product with high demand, a constrained supply will assure that almost every available unit is sold. By making sales forecast equal to the expected supply in each time period, your error is only as large as the error in your supply projection. Whether constraining supply is in the best interest of the company is a separate matter – but at least your bonus is maximized.
Lesson: Given the nature of your demand patterns, seek to understand what level of forecast accuracy is “reasonable to expect.” The accuracy achieved by a “naïve” forecasting method (such as random walk or moving average) provides a lower bound for the accuracy that you should be able to achieve. A reasonable goal for the forecasting function – as pathetic as this may sound – is to beat the accuracy of a naïve forecast. It is also reasonable to strive continuously for process improvement. Improvement can be in terms of increased accuracy, reduced bias, and elimination of process waste.