There is a third benefit of this approach, which relates to understanding the financial impacts of individual changes. By putting each change in separately and then taking it out again, the cost variances of the resulting forecasts can be used to develop an understanding of the costs of each initiative. This is particularly useful for checking that business cases stack up when applied to the live models. Care needs to be taken, however, because the order in which changes get applied can matter, and the claims that are sometimes made turn out to be false. For example, imagine we have a call centre operation that costs £10m per year to run and that we have an initiative that will halve our call volume. Our £10m call centre will become a £5m call centre. Suppose we have another initiative that will halve our AHT; the project manager expects to take our £10m call centre to a £5m call centre. Both initiatives are expecting to save £5m. Both initiatives are put into our model as overlays, one into the calls forecast and one into the AHT forecast. If you put the calls project in first it will show a saving of £5m and the AHT one, going in second, shows a saving of only £2.5m. Putting the AHT one in first shows a saving of £5m and the calls reduction project shows a saving of just £2.5m. Or, put the calls one in first (saving £5m), then put the AHT one in (saving £2.5m), then remove the calls one for some reason (adding £2.5m!) and you end up with £5m. Trying to explain this kind of problem to senior managers is extremely difficult when there are 10, 15, or more different but inter-related overlays, some adding and some taking away, not least because it is complicated to model this kind of system. So although we now have the power to assess the impact of individual changes we must always keep in mind that things don’t always add up as we’d expect or hope, and that the most important figure is always the total, combined result when all the overlays are added together.