Optimizing Retail with AIOps: A New Era of Demand Forecasting and Customer Personalization

Authors: Shally Garg

DOI: https://doi.org/10.5281/zenodo.15054601

Short DOI: https://doi.org/g88372

Country: USA

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Abstract: The growing complexity of retail and e-commerce operations necessitates advanced AI-driven solutions to enhance efficiency, scalability, and decision-making. Artificial Intelligence for IT Operations (AIOps) leverages deep learning (DL) to automate IT processes, optimize supply chains, and improve customer experience. This survey reviews existing DL techniques in AIOps for retail, categorizing key challenges and exploring solutions for demand forecasting, fraud detection, and personalized recommendations. We analyze modular versus end-to-end DL architectures, offering guidelines for model selection and training strategies. Additionally, we examine advancements in explainable AI, federated learning, and real-time anomaly detection, highlighting their role in improving AI-driven retail operations. Furthermore, we discuss uncertainty estimation techniques in neural networks, crucial for reliable decision-making in e-commerce environments, evaluating frameworks such as Bayesian networks and Monte Carlo sampling. Lastly, we explore transparency-enhancing efforts in AIOps, integrating logical reasoning with DL to ensure interpretable AI-driven automation. This survey aims to provide a structured overview of current research and guide future advancements in AIOps for retail and e-commerce.

Keywords: Retail AI, E-Commerce Operations, Anomaly Detection, Predictive Analytics, Neural Networks, Recurrent Neural Networks (RNNs), Long Short-Term Memory (LSTMs), Convolutional Neural Networks (CNNs), Autoencoders, Transformer Models, Customer Experience Optimization, Fraud Detection, Supply Chain Intelligence, Scalability Challenges, Data Privacy, Explainable AI, Federated Learning, Real-Time Processing, Cloud Computing, Edge AI, AI-Driven IT Management

Paper Id: 232258

Published On: 2022-10-06

Published In: Volume 10, Issue 5, September-October 2022

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