Automated Anomaly Detection for Sales and Inventory in Data-Driven Industries

Gaukhar Makhmetova

Citation: Gaukhar Makhmetova, "Automated Anomaly Detection for Sales and Inventory in Data-Driven Industries", Universal Library of Engineering Technology, Volume 02, Issue 01.

Copyright: This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

This study investigates automated anomaly detection systems for sales and inventory in data-driven industries, focusing on real-time detection methodologies for industries managing complex inventories. Through a systematic literature review, we analyze the implementation of machine learning algorithms and deep learning architectures for anomaly detection across retail, supply chain, and financial sectors. The research proposes a conceptual framework for evaluating anomaly detection system maturity and presents an industry-specific priority matrix for implementation strategies. Our findings reveal the critical patterns in anomaly detection system development, including technology convergence, evolution of data processing approaches, and transformation of business processes. The study contributes to both theoretical understanding and practical implementation by introducing a structured approach to anomaly detection system evaluation and development. This research addresses a significant gap in current literature by providing a comprehensive framework for understanding how different industries can effectively implement and optimize their anomaly detection capabilities. The proposed maturity model and implementation strategies offer valuable insights for organizations seeking to enhance their operational efficiency through advanced anomaly detection systems.


Keywords: Anomaly Detection, Artificial Intelligence, Machine Learning, Sales Analytics, Inventory Management, Data Analytics, Retail Operations, Supply Chain Management, Financial Analytics, Automation Systems.

Download doi https://doi.org/10.70315/uloap.ulete.2025.0201002