SKU Level Forecasting: Understanding Sales Segements

Understanding you sales segments especially when they are available at SKU level will help us achieving better forecasts. Look for following first, instead of reverse engineer with ML/Statistical techniques and wasting time in front.

1) Sales Variation - Foremost thing one should follow when we have SKU level data is to look for each SKU variability with respect to their average sales or from with in variation sales i.e. coefficient of variation.

  1a) In my practical journey of forecasting, I tend to look for zero ratio, which is nothing but, ratio of zero observations to non-zero observations. It tells us a lot about noise which we are going to face.

  1b) Also, I look for active, new and ended products, as most of the time clients never provide product life cycles and we combine all or treat all of them as same, forgetting the important impact of life cycle.

  1c) Further, in my practical journey of forecasting, I have seen lot of people, in reality failing to understand seasonal traits. Specific example, when seasonality moves from a time point to another (e.g. from one week to other, in cases of specific holidays like Good Friday and Easter). We need to get it right and label it right for modelling.

2) Client Provided Info - exploiting demographic or geographic information that already exists in the data. For instance, city information or product line/item information which makes it unique to that category.

3) Generate Segments - In few cases, I have observed that a bare minimum data about only date and time along with sales is provided for forecasting purposes. In such scenarios, we need to generate segments using unsupervised Machine Learning techniques, however, here, as said above, foremost start from sales variation obtained and proceed for better results.

Once all above, sales segment analysis is done, then job of identifying a better model is damn easy. For instance, a low volume with zeros SKU can be even predicted using a moving average method which provides better forecasting than high end boosting technique.

Views expressed here are from author’s industry experience. Author trains on and blogs Machine (Deep) Learning applications; for further details, he will be available at mavuluri.pradeep@gmail.com for more details.
Find more about author at http://in.linkedin.com/in/pradeepmavuluri

My Two Cents as pragmatic forecaster,
Happy Forecasting!