Enterprise-wide Planning capabilities aligned to your organization's business window
The end of a fiscal year is an arbitrary date and planning/forecasting should not stop just because of a date on the calendar. Rolling forecasts provide critical longer visibility into, and therefore time to act on, potential revenue or operational challenges & opportunities.
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Driver-based modeling is a core component of this approach, leveraging statistical techniques where appropriate to create automated processes for rapid forecasting. BetterVu's approach incorporates both the process and technology to build functional and enterprise-wide forecasts aligned to your organization’s business window.
WHY Data-driven FORECASTing?
The most important feature of a timely and actionable forecast is that it provide visibility further into the future. This requires that the forecast roll with the calendar, constantly updating projections far enough to allow actions to be taken.
Lets look at Forecast Sales (red line) versus Budget Sales (blue line) in the chart: the good news is that we’re growing faster than expected. The bad news is that the green line, Capacity Before, is falling below forecast sales. But seeing this information several quarters ahead allows us to increase our capacity through expansion, overtime, sub-contracting, deferring planned maintenance, etc. or, conversely, altering our sales plans to shift demand to different periods or products … with the results shown in the chart via the purple line, Capacity After. A strong forecast provided the insights to act now and minimize the problem.
More Accurate Forecasting
Of course, the forecast itself is only useful if one can assume that it’s reasonably accurate. And that in turn requires drawing on the right sources of data: what is the best predictor of future sales? In some industries, history is the ideal predictor; in others, the order book itself is key; and in yet others, manual estimates by the sales force are ideal.
The blue line shows the sales force’s forecast, the red line shows history, and the green line is derived from the order book. It seems clear that our sales force is usually optimistic while the order book doesn’t help predict at all; in this case, it seems that the past portends the future. This knowledge helps us develop the best possible initial forecast, in this case driver-based and updated automatically.
While statistical methods, such as extrapolating from history, might work reasonably well, the best way to improve accuracy is to allow forecasters to see their forecasts and compare them to actuals and other forecasts, understand the reasons for variances, and target to close the gaps. Our approach provides a customized view for each forecaster to see the changing magnitude of their errors over time and how well (or not so well) they forecast relative to their peer group.
In this example, Rep 1’s forecasts are steadily improving over time, although consistently being on the optimistic side. Rep 3 is all over the map, missing by fairly significant amounts on both the optimistic & pessimistic sides. And Rep 2 seems to be pretty accurate but with no discernible trend. This information can help all reps seek to learn from Rep 2 why she is relatively accurate while Rep 1 can share what he has done to improve his accuracy over time.