Let us suppose that a factory needs to know when certain products are increasing the profit. This factory produces an huge number of products each with different targets. So the factory need to compare their trend in a relative fashion.
trend itself is not so good when comparing more products. For instance, explore this scenario:
- product A: goes from 50 sold pieces in January to 100 sold pieces in December.
- product B: goes from 100 sold pieces in January to 150 sold pieces in December.
- product C: goes from 100 sold pieces in January to 200 sold pieces in December.
(for simplicity let us suppose the unit price is the same for all products and does not varies in time)
Trend of product A equals the
trend of product B, but product A doubled the sold pieces. Also,
trend of product B is greater than the
trend of product A, while both have doubled their sold pieces.
I wonder what is a good standard indicator to capture such behaviour: an indicator that have the same value for products which double their profit. So that A and C have the same value which is greater than B.
I guess representing the trend a-dimensionally should resolve this problem: this would prevent the dependency of the number of pieces and should result in a more relative indicator.
EDIT: I don't have only two points: not only January and December, but let's say, a set of points for each month. Currently, as trend I use linear regression. I know that dividing this trend (which has dimension of pieces/month) by the first point will normalize it. But I find the using of the first point arbitrary. Why not the second one? why not the average?