Do you also constantly monitor your forecasting accuracy? Are you also wondering which key figure best measures forecast accuracy? Forget this way and do it right now!
In principle, there seems to be nothing wrong with measuring forecast accuracy, because if you don’t measure a target value, you can’t judge whether you have improved. However, the focus on forecasting accuracy often narrows the perspective and all action is focused on improving the quality of the forecasts rather than on the actual goal of delivery capability.
In fact, many companies still have considerable room for improvement when it comes to the quality of their forecasts. Many do not make use of the mathematical possibilities available today and many try to improve their forecasting quality by “manual work”: the sales department should fix it and make more effort or demand better data from the customers. The thumb screw of forecast accuracy seems to be just right.
Mathematics instead of manual reworking
However, it makes little sense to use the lever of forecasting accuracy here, as you will not achieve systematically better forecasts with manual processes. There may be a small improvement under the thumb of forecasting accuracy if everyone is urged to be more careful; but at what cost in terms of employee motivation and acceptance?
In order to systematically achieve better forecasts, you must first start with the math. Using statistics, simulation and artificial intelligence, special forecasting systems offer companies that only use their ERP or merchandise management systems to determine forecasts significant potential for improvement. However, the forecasts will never be precise, because every market demand behaves chaotically to a certain extent and chaos cannot be predicted. It doesn’t help to upgrade mathematically beyond a certain level.
It doesn’t really make sense to determine the accuracy of forecasts at this point, because if something can no longer be systematically improved from an economic point of view, there is no need to constantly measure it; there is no need to push algorithms to be more accurate.
Accepting chaos as a factor
It can make sense to supplement “technical” forecasts with sales estimates. This raises the question of what added value can be achieved. The comparison between the accuracy of the “technical” forecast and the accuracy of the “hybrid” forecast is the only place where measuring the accuracy of the forecast might make sense.
In all cases, it makes much more sense to measure the breadth of the chaos of market demand and to design the safety stocks accordingly. This is because the battle for delivery readiness can only be won through safety stocks, not through forecasting accuracy.
By the way, if you need a little refresher on forecast accuracy, we have something for you: In a nutshell – forecast accuracy
