Phantom Inventory Detection in Retail Supply Chains using Federated Learning

DOI

10.2991/978-94-6239-616-6_14How to use a DOI?

Keywords

Phantom Inventory; Federated Learning; Data Privacy

Abstract

Phantom inventory is the difference between what a retailer has recorded in their inventory system and the stock that is physically available in the store. This difference leads to loss of sales, customer dissatisfaction and supply chain waste. The proposed approach implements a system based on Federated Learning where each store trains a local model for inventory mismatch detection and only shares model updates in order to preserve privacy. The use of anomaly detection algorithm such as Autoencoders enhances data privacy and system accuracy in detection. The approach is also useful and ideal for small retail stores in urban and rural settings as it is economical and highly scalable.

Copyright

© 2026 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - P. Praveenkumar
AU  - I. Mithra
AU  - E. Sanjana
AU  - K. Preethi
AU  - Puspita Dash
PY  - 2026
DA  - 2026/03/31
TI  - Phantom Inventory Detection in Retail Supply Chains using Federated Learning
BT  - Proceedings of the International Conference on Artificial Intelligence and Secure Data Analytics (ICAISDA 2025)
PB  - Atlantis Press
SP  - 165
EP  - 173
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6239-616-6_14
DO  - 10.2991/978-94-6239-616-6_14
ID  - Praveenkumar2026
ER  -