Forecast accuracy is one of those strange things: most people agree that it should be measured, yet hardly anybody does it. And the crazy thing is that it is not all that hard. If you utilize a planning tool like IBM Cognos TM1, Cognos Planning or any other package, the calculations are merely a by-product – a highly useful by-product.
Forecast accuracy is defined as the percentage difference between a forecast and the according actuals (in hindsight). Let’s say I forecast 100 sales units for next month but end up selling 105, we are looking at a 95% accuracy or a 5% forecast error. Pretty simple. Right?
Why should we measure forecast accuracy? Very simple. We invest a lot of time into the forecast process, we utilize the final forecast to make sound business decisions and the forecast should therefore be fairly accurate. But keep in mind that forecasts will never be 100% accurate for the obvious reason that we cannot predict the future. Forecast accuracy provides us with a simple measure to help us assess the quality of our forecasts. I personally believe that things need to get measured. Here are three key benefits of measuring forecast accuracy:
- Detect Problems with Models: Forecast accuracy can act like a sniffing dog: we can detect issues with our models. One of my clients found that their driver calculations were off resulting in a 10% higher value. A time-series analysis of their forecast error clearly revealed this after just a few months of collecting data.
- Surface Cultural Problems: Accuracy can also help us detect cultural problems like sandbagging. People are often afraid to submit an objective forecast to avoid potential monetary disadvantages (think about a sales manager holding back information to avoid higher sales targets). I recently met a company where a few sales guys used to bump up their sales forecast to ‘reserve’ inventory of their hot products in case they were able to sign some new deals. Well, that worked ok until the crisis hit. The company ended up with a ton of inventory sitting on the shelves. Forecast accuracy can easily help us detect these type of problems. And once we know the problem is there and we can quantify it, we can do something about it!
- Focus, focus, focus: Measuring and communicating forecast accuracy drives attention and focus. By publishing accuracy numbers we are effectively telling the business that they really need to pay attention to their forecast process. I have seen many cases where people submit a forecast ‘just because’. But once you notice that somebody is tracking the accuracy, you suddenly start paying more attention to the numbers that you put into the template. Nobody wants to see their name on a list of people that are submitting poor forecasts, right?
Overall, forecast accuracy is a highly useful measure. But it has to be used in the right way. We cannot expect that every forecast will be 100% accurate. It just can’t be. There is too much volatility in the markets and none of us are qualified crystal-ball handlers. There is a lot more to consider, though. Over the next few days, I will share some additional tips & tricks that you might want to consider. So, start measuring forecast accuracy today!