Robotics, Automation, and Process Management for Industrial Operations : Review and Results from Real-World Use Cases Applying Machine Learning and Alternative Approaches

Lindner, Fabian and Varlamis, Iraklis and Viltrake, Katrīna and Reith, Karl-Benedikt and Rank, Sebastian and Affek, Michał and Schneider, Germar and Leschly, Rosa and Ramadasu, Venugopal and Reiner, Gerald and Herfort, Pascal and Strazds, Guntis V. and Tatara, Marek (2026) Robotics, Automation, and Process Management for Industrial Operations : Review and Results from Real-World Use Cases Applying Machine Learning and Alternative Approaches. INFOCOMMUNICATIONS JOURNAL, 18 (Specia). pp. 119-134. ISSN 2061-2079

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Abstract

As industrial systems become increasingly complex, dynamic, and data-rich, organizations face growing pressure to enhance efficiency, flexibility, and decision-making through intelligent automation. This work examines how robotics, automation, and process management enable these transformations in modern industrial operations through data-driven and machine learning–based approaches. It first reviews the current state of research and industrial practice to establish a foundation for understanding technological and methodological developments in highly automated manufacturing environments. Building on this foundation, the article presents real-world industry use cases demonstrating how advanced analytics and machine learning improve material handling, robotics, intra-logistics automation, fleet management, and production operations. Particular attention is given to the operational deployment of machine learning, including machine learning operations (MLOps), and to the integration of engineering and management perspectives required to achieve robust and efficient manufacturing systems. The presented applications highlight improvements in system performance, operational efficiency, and decision support in complex industrial settings. By synthesizing practical insights and methodological advances, the article shows how these approaches can be transferred and extended across industries, contributing to more adaptive, intelligent, and resilient production systems.

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