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International Journal of Production Research

Call for Papers | Machine Learning in Manufacturing and Industry 4.0 Applications

Contribute an article

Deadline: 15 May, 2020

International Journal of Production Research

The International Journal of Production Research (IJPR), published since 1961, is a well-established, highly successful and leading journal reporting manufacturing, production and operations management research.

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In manufacturing, a paradigm shift is happening right now. Advances in Big data and Machine Learning (ML) is changing the traditional manufacturing era into smart manufacturing era of Industry 4.0 (I4.0). This paradigm shift is creating new opportunities. The abundance of smart sensors and the Internet of Things are the key enabler of curating and storing a remarkable amount of industrial data-rich environment related to all aspects of production.

ML techniques, a subfield of Artificial Intelligence (AI), has potential to become main driver in uncovering fine-grained intricate production patterns and offering timely decision support in a wide range of manufacturing and production applications, to name a few, predictive maintenance, process optimization, task scheduling, quality improvement, supply chain, and sustainability so on. While different ML techniques have been used in a variety of manufacturing applications in the past, many open questions and challenges remain, from Big data curation, storage, and understanding, data reasoning to enable real-time actionable intelligence to advanced topics such as edge computing, fog computing, cyber security aspects of smart manufacturing. Hence, this special issue aims to bring together a wide range of researcher to report the latest efforts in the fundamental theoretical, as well as experimental aspects of ML and their applications in manufacturing and production systems.

Topics of interest 


Potential topics include, but are not limited to: 


  • ML-based data-driven approaches for manufacturing
  • ML for diagnostics, maintenance, prognostics, and manufacturing system health management
  • ML for manufacturing process monitoring, quality control, and manufacturing process improvement and optimization
  • ML for all aspects related to supply chain management and production systems
  • ML for sustainable and eco-friendly manufacturing
  • ML for Industrial Internet of Things (IIoTs) data streams
  • ML for energy consumption reduction in manufacturing facilities
  • Digital twin model-based data-driven approaches for manufacturing problems
  • ML and data-driven design for manufacturing to enable better, faster, and high-throughput fabrication of parts.
  • ML methods that leverage material informatics for improved manufacturing
  • Novel ML algorithm for manufacturing domain
  • Innovative Deep Learning architectures to generate actionable manufacturing process and system level intelligence
  • ML based approaches for addressing cyber-security issues
  • Hybrid machine learning methods that combine data-driven and equation-based methods for manufacturing applications


  • Curation of Big-data, benchmark problems, metrics, and comprehensive case-studies that demonstrate the utility of ML approaches in manufacturing domain

Guest editors


Rahul Rai, University of Buffalo
Alexandre Dolgui, IMT Atlantique
Dmitry Ivanov, Hochschule für Wirtschaft und Recht Berlin
Manoj Kumar Tiwari, Indian Institute of Technology – Kharagpur

Submission guidelines

For guidance on how to submit your manuscript to this Special Issue, please visit our Instructions for Authors page