May 1 2013
Supply chain consultant Hadas Gur asked the following question:
I have data of demands for 13000 SKUs (consumptions from the last 5 years). 6000 of the observations are zeros. I can’t recognize the distribution of the data . I have tried the q-q plot to find a match to any known distribution. What can I do in this case if I want to find the reorder point? Is it ok to use the reorder point formula which is in your post “Safety Stocks : Beware of Formulas” even though the distribution is not normal?
You do not give a context. Are those SKUs components supplied to a manufacturing company or retail items on supermarket shelves? The demand patterns may be radically different. In retail, for example, the demand for milk is the sum of the quantities bought by a large number of individual consumers acting independently, and the normal distribution is a likely fit. On the other hand, if you are supplying a model-specific part to a car manufacturer, it is unlikely to fit.
Do not try to apply the same approach to all 13,000 SKUs! For example, reorder point makes no senses for the 6,000 items that have had no demand in the past 5 years. You would want to investigate whether they should still be in the catalog and, if so, they are strangers and you need to organize to make or buy them when an order arrives.
For the others, I would suggest you explore the data rather than focus on fitting a distribution, starting with a Runner/Repeater/Stranger analysis. Then, starting with runners, investigate trends and seasonal variations. For repeaters, I would investigate ways to group them into families that make sense for what you are trying to do.
Do not use only the data. In order to understand what is possible, you need to visit the warehouses or distribution centers and understand how physical distribution distribution is organized, and the people involved.
Then consider a range of approaches for different items and item families, including just-in-sequence, Kanban, two-bin, reorder point, vendor-managed inventory, consignment, etc. Examine how these approaches would have performed with the consumption pattern of the last 5 years. You can also simulate future demand.