Fixing Inventory Inaccuracies At Scale
Vivek F. Farias, Andrew A. Li, Tianyi Peng

TL;DR
This paper introduces a novel, scalable anomaly detection method for inventory inaccuracies in retail, leveraging cross-sectional data and low-rank Poisson matrix models, achieving significant cost reductions over existing solutions.
Contribution
It proposes a simple, entry-wise anomaly detection approach for low-rank Poisson matrices that works under realistic noise conditions, with theoretical guarantees and practical effectiveness.
Findings
Up to 10x cost reduction compared to existing methods
Effective anomaly detection using cross-sectional store and SKU data
Theoretical guarantees for entry-wise error in sub-exponential matrices
Abstract
Inaccurate records of inventory occur frequently, and by some measures cost retailers approximately 4% in annual sales. Detecting inventory inaccuracies manually is cost-prohibitive, and existing algorithmic solutions rely almost exclusively on learning from longitudinal data, which is insufficient in the dynamic environment induced by modern retail operations. Instead, we propose a solution based on cross-sectional data over stores and SKUs, observing that detecting inventory inaccuracies can be viewed as a problem of identifying anomalies in a (low-rank) Poisson matrix. State-of-the-art approaches to anomaly detection in low-rank matrices apparently fall short. Specifically, from a theoretical perspective, recovery guarantees for these approaches require that non-anomalous entries be observed with vanishingly small noise (which is not the case in our problem, and indeed in many…
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Taxonomy
TopicsData-Driven Disease Surveillance · Anomaly Detection Techniques and Applications · Advanced Statistical Process Monitoring
