Control patterns for management under uncertainty
47 Forecasting with statistical extrapolation
The work of reforecasting can sometimes be reduced by FORECASTING WITH STATISTICAL EXTRAPOLATION.
47 Forecasting with statistical extrapolation
Example: A company that hires audio visual equipment for use at conferences and in companies found that asking sales managers and other managers for forecast nancial numbers was time consuming and rarely accurate, but statistical extrapolation from past results was more reliable and less subject to manipulation and bias. They were then in a position to move to an approach that combined statistical extrapolation with judgements and plans input by managers.
FORECASTING WITH STATISTICAL EXTRAPOLATION is useful for forecasting and as a comparator that allows managers to review critically and challenge forecasts submitted by other managers. This control is only applicable where the results are suf ciently regular. It can be used in REFORECASTING TO COMPLETION.
If you have a history of past results and a computer then reforecasting by statistical extrapolation is quick and easy.
A forecast made by statistical extrapolation is a forecast that uses mathematics to guess what the future will be like assuming it is similar to the past. Similarity to the past can be quite sophisticated, picking up cycles, trends, and variability. There are many different formulae that could be used, though happily studies of accuracy in practical use tend to show that it is the simplest methods (e.g. exponentially smoothed moving averages) that tend to work best.
If you break a number down and forecast each component then a slightly better forecast may result, with some types of extrapolation, but usually the advantage is slight so it makes sense to work with numbers that are already summarized enough to t on a spreadsheet.
Calculating statistical extrapolations is very easy and quick compared to asking lots of managers to give their best estimate of their contribution to an overall result.
Also, formulae do not try to manipulate expectations or suffer from secret biases.
In addition, statistical extrapolations should re ect an overview of all relevant data from which regular patterns can be seen, whereas individual managers tend to see only part of the picture so things look less regular to them. On the other hand, formulae do not understand about unrepresentative events in the past or future changes or plans.
Experience shows that statistical extrapolations are more accurate and reliable than we like to admit, and often more reliable and accurate than human estimates even though the human estimator has access to more information.
The performance of a statistical extrapolation formula can be tested by seeing what it would have predicted for numbers you now know. If you have, say, two years of past numbers and need six months of data for the rule to make an extrapolation then that gives 18 months where extrapolations can be compared with actuals.
This analysis can also be used to calculate prediction intervals. A prediction interval is a range around a predicted number that indicates how likely it is that the actual value, when known, will be near to the predicted number. For example, looking back at past forecasts and actuals may show that 90 per cent of the time the forecast has been right to within plus or minus 5 per cent. Plus or minus 5 per cent would then be a good choice of prediction interval.
Prediction intervals should always be given. They remind readers that the forecast is only a forecast, not a fact, and they quantify the uncertainty involved.
Statistical extrapolations can be used to review and challenge estimates submitted. If an estimate seems very much out of line with the extrapolation then that means the person who made the estimate thinks things are going to be very different in future. Why? They need to be able to give some good reasons for that, especially if they have an obvious motive for trying to manipulate expectations.
A statistical extrapolation that is giving results comparable with adding up human estimates can usefully replace those estimates because it will be easier and quicker to do and its weaknesses can be understood.
However, there is one more problem to solve that is at the heart of making forecasting valuable. A forecasting model should allow users to try alternative action plans and see what the implications for results might be. A statistical extrapolation on its own does not allow that.
Fortunately, there is a simple way to add this functionality to a statistical extrapolation and so combine the ease of these forecasts with the engagement and what-if capability of other forecasting methods.
The technique is to create lists of ‘variations’ i.e. events or actions that are either unrepresentative blips in the past history or are actions planned or events anticipated in the future that will be different from normal past practice. Against each of these the impact for results (e.g. costs) needs to be estimated period by period. Software then:
runs through the variations erasing blips from past history;
1.
re-extrapolates; and then 2.
adds future variations to the new extrapolation to produce the nal forecast.
3.
Past history, the raw extrapolation, and the projection incorporating variations can be shown using time series line graphs. This makes reviews and conversations easier because everyone can see how the forward numbers compare to the past and what impact doing things differently is expected to have.
With this technique discussions of what-if forecasts can focus on what actions will be different and what their implications will be. It is only necessary to consider variations for important items.
This means that the statistical extrapolation still takes away a lot of the drudgery of forecasting but gives freedom to adjust though in a way that is easy to review and challenge.
The usual alternative that most managers will be familiar with is one person adjusting aspirational numbers in a bunch of budget spreadsheets and then another person hunting through trying to nd out what they have done. All of this happens without ever having to discuss how actions will be different.
In summary, this control cuts the cost of forecasting and helps reduce gaming by forecasters. Therefore:
Test the possibility of using statistical extrapolation for forecasting and if the data support it then use this in combination with human input.
There are no subsidiary control patterns to this one in this book.
Notes
1 A small selection of these cases is reported in their book: Hope, J., and Fraser, R.
(2003) Beyond Budgeting. Cambridge, MA: Harvard Business School Press.
2 According to recent surveys risk-adjusted performance measures are about as common in nancial services as risk appetite statements (laying down a list of limits). Deloitte’s fth Global Risk Management survey found that 66 per cent of their sample of large institutions around the world had a formal statement of overall risk appetite, while PRMIA’s global survey, ‘Risk Adjusted Performance Measurement’, found that 44 per cent of organizations were already using risk adjusted performance measures and a further 45 per cent were in various stages of planning to. Both surveys were conducted in the latter part of 2006 and published in 2007.