Forecast Variance Calculation

Hello all can you help,

Is there a standard calculation to measure the variance of the forecasted calls against actual calls?

The calculation I currently use is ((Forecasted calls-Actual calls)/Actual calls) which gives the forecasted call variance as a percentage of the actual. This ensures the forecasts are measured against the actual and it is this difference that is reported.

However, we are trying to standardise how forecast variance is reported across all our business units to ensure consistency and it has turned out to be a much bigger issue than expected as there have been a number of calculations put forward.

Any advice on a final calculation will be much appreciated.

Question asked by Rod

Forecast Variance

The calculation you have seems fine to me. I guess other members can comment on a standard approach.

I don’t think you need to have a definative industry standard, but you need to set your own company standard. I understand your position well.

Many parts of an organisation all reporting the same factor but with different calculations. The best resolution ( if you can manage this) is to pick one suitable method and ensure everyone uses the same calculation.

It doesn’t then matter too much if it is an “industry” accepted method because you can make direct comparisons within your business.

With thanks to David

Variance

Works for me.

Of course everyone will say the method that portrays them
in the best light will be the correct one.

For what it’s worth I use 1-(Actual/Forecast)as a percentage.

Looks like this will open up the debate!

Roll up roll up…

With thanks to Dave

+ / –

found 2p in my pocket, so thought i would give my tuppence worth…

dont forget to convert daily varience to a postive % when doing monthly varience, otherwise if they are out +5% one day & -5% the next, the average variance will be 0%

With thanks to Dylan

Forecast Variance Calculation

Rod

Starting to wonder if I have too much time on my hands. In my defence though this is something that we have had to look at before.

Following on from Dylan’s point on accuracy there is a way around this to calculate the accuracy of a forecast in a single period (1/4 hour period, hour, day etc) and to take these periods into account when looking at the accuracy of a forecast over a number of periods (say a month).

I have prepared the below table to demonstrate the calculation.

My calculation is basically the inverse of Dave’s for individual periods (days in this example but could just as easily apply to 15-min, half-hour or hour periods). When looking at the overall accuracy as the forecast was overall only 5 calls out then it looks like the forecast was only 4% off. To take the individual periods into account we take the actual from the forecast in each period and square it, sum those squares and take the square root from that figure.

The accuracy of forecast figure is then 1- (square root of the sum of the squares of the variance in forecast versus actual call figures).

This essentially measures the accuracy of forecast across the whole period and takes the wild variations into account.

For Actual Eamon Forecast – actual
Mon 10 8 -20% 2
Tue 11 12 9% -1
Wed 23 14 -39% 9
Thu 34 34 0% 0
Fri 12 12 0% 0
Sat 15 26 73% -11
Sun 7 11 57% -4
Overall 112 117 4% -5

Sum of Squares 223
Square root 14.9
% Accuracy of forecast 87%

With thanks to Eamon