Using Deep Learning to Improve Inventory Record Accuracy: Concept and Application

Maintaining accurate inventory records remains a central problem for managing retail operations. Discrepancies between the physical and recorded stock lead to poor reordering decisions and the resulting over or understocking of products, which in turn increases waste and lost sales, respectively. In this study, we revisit this classic inventory management problem by investigating whether novel machine learning algorithms provide an improvement over established practices. Specifically, we explore the application of deep learning as a work routine to identify and correct 'impactful' inventory record errors - those that affect future reordering decisions - by leveraging product level, store level, and inventory quality data.