Abstract
An out-of-stock (OOS) event is referred as one of the biggest supply-chain management problem concerning retailers, distributors and consumers. We present available PCG data and discuss how to determine the importance of some features (fields), their interconnections and compare them with standard data fields used in other publicly accessible studies and recommendations from Efficient Consumer Response (ECR). We propose several models and algorithms to predict and solve Out of stock problem and at the end the computational results of these models are presented.
Highlights
An out-of-stock (OOS) event is referred as one of the biggest supply-chain management problem concerning retailers, distributors and consumers
We say that an item is out-of-stock when a customer cannot find it on its usual place
Consumer studies showed that the possible costumer reactions to the out-of-stock event ranged from "do not purchase an item" to "delay purchase" passing by "substitute-different brand", "buy item at another store" and "substitute-same brand"
Summary
An out-of-stock (OOS) event is referred as one of the biggest supply-chain management problem concerning retailers, distributors and consumers. We further examine these methods and propose several new models and assess their computational complexity as well as their precision and accuracy
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