Accuracy is a critical element of forecasting in every organization. The more accurate forecasting is to decision makers the more likely that their decisions will result in a desired ROI for the company. As with many other challenges described herein, Excel desktop forecasting is at the root of most of these challenges. Teams must systematically measure accuracy in hopes of improving it over time.
Measuring the accuracy of the final results does not provide ciritcal insights into what key assumptions drove that level of inaccuracy. When one can connect accuracy drivers back to key assumptions, it is possible to then trace whether they were driven by controllable or uncontrollable dynamics.
Accuracy must be measured below the surface of final results in order to identify key business dynamics for decision makers. It is entirely possible to have a highly accurate sales forecast with fundamental issues in the business that cancel each other out. The market grew faster than anticipated but your company is still losing market share. Isn’t it critical for the business leaders to address issues of competitive threat in this case?
Improving accuracy over time is a multi-dimensional process that benefits greatly from an Enterprise- class forecasting platform. In addition to providing increased access to the measures of accuracy and drivers of variance, it can also provide additional capabilities. One of the most significant of these is providing best practice statistics in comparison to forecaster generated assumptions.
i2e by Scarsin has evolved significantly over the years to provide support for improved accuracy. Enabling technology does not necessarily drive the majority of accuracy improvements but it does facilitate the process improvement within Forecasting teams.