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In the world of distribution, the question is often asked, “What is the best interval at which to create a forecast—monthly or weekly?”  Over the next few weeks, I’d like to weigh in on the debate.  I’ll start with monthly.  Creating a forecast for twelve periods throughout the year often produces a ‘lower forecast error’ than generating one 52 times.  There are several reasons for this:

1)  Monthly forecasting normalizes to a larger bucket size so that timing issues are not as disruptive.  For instance, if an end-customer states they are going to order a SKU at a given time, but instead, the order comes in during another week.  This modifies the monthly time series in a nontrivial way.  The jitter is nearly four times as likely to be absorbed into the same monthly bucket, causing no net change to the time series.

2)  Monthly bucketing reduces the number of zero entries in the time series and allows the law of averages to work for us.   For example, if a customer places an order for 100 units every 2 weeks, we see an average of 50 per week.  However, the forecast error relative to that average is always wrong.  The order quantity is either 100 or 0, but never 50.   Looking at this series in a monthly view gives a near constant usage of 200 units per month, which is easy to forecast.

3)  Seasonality is easier to detect and forecast using a monthly timeframe.  There are a lot of natural cycles that fall into monthly boundaries, i.e. paychecks, quotas, budgets, holidays, and average weather conditions.  As well, by definition, months are in the same position every year.  Weeks, on the other hand, can move around +/- 4 days in either direction.  This generally muddies the seasonality analysis given a limited number of years to evaluate.  Putting these seasonal factors into monthly lumps more reliably allows general tendencies to develop and be projected into the time series.

As you might imagine, we at RockySoft are generally proponents of monthly forecasting for the vast majority of cases.  This is based largely on the fact that we have forecasted millions of time series, and realize there is a limit to improving forecast accuracy.  Why not allow more of the noise to be filtered out by using the larger monthly buckets (instead of weekly buckets)?  This approach generates fewer exceptions, and allows forecasts to be reviewed less frequently.  

Next week, I’ll discuss the reasons why one might choose weekly forecasting.

Tags: Forecasting

This entry was posted on Wednesday, July 22nd, 2009 at 11:04 am and is filed under Forecasting. You can follow any responses to this entry through the RSS 2.0 feed. Both comments and pings are currently closed.