The goal is to predict out-of-stock situations, either quantitatively (the gap) or qualitatively (out-of-stock likely to happen in next few weeks). Background:

  1. We have existing demand planning process, through heuristics and software packages (using time series models). Most of the time they perform well, and recorded out-of-stock is rare (~ 8% of total orders rejected due to out-of-stock). Hidden cases might exist, meaning customer did not even place the order after knowing we ran out of stock.

  2. Out-of-stock happens "in clusters". Out of a two year period, we have only 3-4 out-of-stock periods, each lasting one to two weeks. They did not happen when the demand was the highest, because we planned for that.


  1. Time series can capture the average demand, however they cannot model extreme / unexpected cases, which cause out-of-stock. Using upper confidence interval can cover some extreme cases, however this significantly increases warehouse cost.
  2. Not enough data for machine learning - essentially only 3 out-of-stock cases because they happen "in cluster".

Please suggest how I can handle this - any input welcomed...


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