So indicators are a set of metrics. You may have something like this to measure client service:
| Indicator | Actual Value | Target |
|---|---|---|
| Percentage of pizzas delivered within 30 minutes | 90% | 100% |
| Percentage of calls answered within 2 minutes of entering the queue | 80% | 100% |
Now, to get an aggregate score for client service, you could just take the average of the 2 indicators, that would give you (90+80)/2=85. However, you may decide that the indicators don't all have the same importance, the so they shouldn't all have the same weight. Let's say people hate waiting in a telephone queue, but won't notice if there pizza is 2 minutes late. In that case, the indicator for call wait time is more important, so we'll give it a weight of 70%, and we'll give a weight of 30% to the pizza delivery time. That would give us a score of (90*0.3)+(80*0.7)=27+56=83.
A few notes on this:
be careful of the units you use, in the example, we used 2 percentages with the same target, so we know they'll be fairly close and that they are fairly comparable. But if you were measuring something like the number of units sold and average call wait time in minutes, your units would be too different to be compared directly. What can you do? Use the target, and compare the result to the target. That will give you 2 results in "percentage of target achieved", which can than be directly compared to one another. If you use that method, setting meaningful targets becomes essential if you want your aggregate indicator score to be meaningful and useful.
In the example, the weights used add up to 1. It doesn't necessarily have to. But having a score that has an understandable maximum (100 in this case) makes it more understandable and intuitive. The resulting aggregate indicator score in the example, is not in a particular unit: all we know is that it's maximum is 100. There are times when, because of either your indicator or target your result may exceed 100. There is nothing wrong with that, but it highlights the importance of explaining how you go about measuring your performance, and how your data should be interpreted.
Finally, defining weights is a tricky exercise, and some managers may abuse this system by assigning low weights to indicators on which they know they will perform poorly. Another aspect to consider is that you may want to assign low weights to indicators for which the results are not very reliable.
