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arXiv AI

Travel Time Prediction in Supply Chain Management Using Machine Learning

The study explores machine learning and deep learning techniques to predict travel time for transportation and logistics within supply chain systems. By leveraging extensive historical data, it aims to build an accurate model that estimates travel times for inventory movement. The research emphasizes the importance of precise travel time predictions for improving logistics consistency, performance, and planning across the supply chain.

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arXiv Machine Learning
Sep 11

Supply Chain Analytics: A Data-Driven Approach

The article "Supply Chain Analytics: A Data-Driven Approach" presents a mathematically rigorous framework that integrates statistical learning with robust decision-making for logistics and operations management. It covers topics from empirical demand forecasting to optimal inventory and network control under uncertainty, including sample minimization, dynamic programming for inventory replenishment, network fulfillment, and distributionally robust optimization using transport theory. The work also links predictive models with prescriptive algorithms such as column generation for vehicle routing and non‑homogeneous queueing regimes, offering both theoretical foundations and algorithmic guidance for building resilient, automated supply chain systems.

By Elioth Sanabria
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arXiv AI
1d ago

Trustworthy Data- and ML-Ops for Intelligent Transportation Systems and Logistics

The paper reviews Trustworthy Data and Machine Learning Operations (DataOps and MLOps) for Intelligent Transportation Systems and Logistics (ITS&L). It identifies gaps in current literature, discusses the complexities, key components, tools, practical insights, and case studies relevant to ITS&L, and examines methods to strengthen trustworthiness in AI applications. The authors conclude by outlining ongoing challenges and future prospects, positioning the work as a resource for researchers, industry practitioners, and policymakers.

By Antonio Emanuele Cin\`a, Giovanni Scodeller, Cecilia Caterina Pasquale, Silvia Siri, Davide Anguita, Fabio Roli, Simona Sacone, Luca Oneto
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arXiv AI
Jun 2

ChurnNet: A Optimized Modern AI for Churn Prediction

arXiv:2606. 00169v1 Announce Type: cross Abstract: Increased competition and the growing similarity of products and services offered by retailers have lowered the barriers for customers to switch to competitors.

By Syed Saad Saif, Giulio Maggiore, Paolo Russo, Damiano Distante
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arXiv AI
Sep 16

LLMs as Master Forgers: Generating Synthetic Time Series Data for Manufacturing

The paper introduces a framework that uses Large Language Models (LLMs) to generate synthetic time‑series data for manufacturing processes. By fine‑tuning pre‑trained LLMs on manufacturing instructions and applying Retrieval Augmented Generation (RAG), the method enhances data diversity and realism. Evaluation against traditional models such as ARIMA and LSTMs shows that the LLM‑driven approach produces higher‑quality synthetic data, better capturing temporal dependencies and improving downstream anomaly detection performance.

By Mantek Singh, Jeshwanth Challagundla, Prateek Karnal, Gagan Ganapathy, Vineet Shah, Ridam Arora
llmsragfine-tuning
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