Statistical analysis of multi-criteria inventory classification models in the presence of forecast upsides

Companies may receive forecast upsides that can undermine their ability to support customer demand on time. Therefore, it is critical to include forecast upsides when performing comparative analysis of inventory classification models. This study focuses on this subject. The study includes statistical methods and sensitivity analysis to determine the performance of which MCIC model is statistically significant with respect to inventory and customer orders fill rate. Results show that the PBB-model outperforms other models when forecast upside is present and the result is statistically significant. On the other hand, when no forecast upside is present, the R-model, which does not use descending ranking criteria, outperforms other models, and the difference is statistically significant. We also find that adding descending ranking criteria to the R-model and ZF-model does not improve their Service-Cost Performance Index. © 2017 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.