Moving Annual totalFloating annual total
Total Moving Amount (MAT) Sales and Value: My task is to create a metric (or filter) for the moving annual total (MAT).
Yearly moving average
This paper considered the Moving Annual Total (MAT) as a way to easily eliminate seasonal effects over a 12-month horizon in order to discover the trends behind a given datarow. MAT works well for flowing positions such as revenue, profit and transport volumes as these can be accumulated over timeframes to obtain significant amounts.
However, we cannot use a MAT for an inventory line such as accounts receivable or number of employees, or for statistics such as gross margin percentage or exchange rate /$, because it does not make much point to add these line across period. Calculation of the Moving Annual Average (MAA) is the answer. But the easiest way to do this is to use a very similar calculation method to that used to compute a MAT - the end score is then split by 12 (or how many cycles we have within our year) to get an annual number.
We can calculate a more precise monthly weighed mean for each period in cases of statistical data such as gross margin percentage (from one flows position split by another flows position) by computing the sum of the last 12 periods for both the meter and the ratios denominator separately and then divide them.
Statistics such as return on capital employed percentage (formed from a cash position divided by a inventory position) require a more complex computation because the value for such a quota is usually shown as an annual value. Therefore, we must compute the sum of the profits of the last 12 moths and split it by the weighting of the capital employed over this 12-month horizon.
MAA transforming is a very versatile process because it can be used on all kinds of elements of data, whether streams, inventories, or statistic. In contrast, you can only calculate the MAT for transaction items and must therefore use them with caution.