Blog Title Goes Here...

The last couple weeks I’ve outlined some of the advantages and disadvantages of using a monthly vs. weekly interval for creating a forecast.  Over the years, I’ve found that many software providers require customers to choose, up front, which products are forecast weekly and which are forecast monthly.  Short of embarking on another implementation, it is difficult if not impossible to change your mind!

RockySoft’s challenge, then, was to implement a system that could take advantage of all positives listed in the last two posts, for both approaches.   Despite our experience and confidence in monthly forecasting, the marketplace reality is that many of the dominant “Big Box” players develop and provide their forecasts to their suppliers – our customers – in weekly formats.

Rather than force you into a mold, we chose to develop on-the-fly flexibility to view and analyze both approaches.  This allows the customer to better benefit from both their relative merits.   Why force a decision that cannot be reversed?  Why not let people dynamically switch between methods to gain insight into the products behavior?  This ability to dynamically switch among forecast period frequency is a lot like zooming into a stock market graph:  Are you looking for close in volatility or long term trend?  What can you see up close that was not evident from a wider view?

This capability opens up new possibilities not seen before, such as starting a new item out as a weekly forecasted product, allowing data points to more quickly accumulate for better reactivity, before flipping it to monthly forecasts once seasonality and a trend is established.

Further, the ability to analyze a set of sales data in different views provides another arrow in your quiver towards maximizing forecast accuracy.  Recall the note in an earlier post about the tendency towards lower error in monthly forecasts.  When both weekly and monthly models are available for analysis, you can compare the relative error rates of monthly and weekly generated models.  Then translate those into a forecast error dollar impact on your inventory, subsequently choosing the method that will allow you to save your company money.  The best metric by which to compare to disparate forecasting methods and arrive at a comparable metric of their relative useful accuracies is a fresh opportunity we continue to explore, as is developing automated methods for this analysis.

As we further delve into the possibilities opened by this dynamic forecasting capability, now in RockySoft 10.1, we grow more excited and surprised at the possibilities to improve accuracy, optimization, and analysis.  We are finding the dynamic forecast changeability offers all the advantages of varying forecast approaches to be delivered to the business, without being locked into any of the penalties.

Last week I built a case for monthly forecasting, so why do some people prefer weekly forecasts?  Obviously, there must be some good reason, as many large consumer packaged goods (CPG) companies rely on them.

There are a couple advantages to weekly forecasts:

1)  Compatibility:  If your customer is communicating with you in terms of weekly forecasts or weekly Point-of-Sale (POS) information (i.e. CPG), generating your own forecasts in kind provides an invaluable direct linkage with them.  Getting that direct linkage with data closer to the retail customer will outweigh any potential internal forecast accuracy improvements.

 2)  Medium Volume Items:  If you are dealing with medium volume items, a weekly approach allows more rapid stabilization of the trend line and more rapid signaling when demand has shifted.  In the absence of Demand Sensing functionality, you get an update to your forecast once every week, rather than once every month.  Thus increasing the accuracy of forecasts by reacting more quickly, both as an algorithm and as a business function.

As you can see, there are advantages of utilizing both monthly and weekly forecasting models.  Next week, I’ll discuss how you might be able to get the best of both worlds…

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.