Supply chain

Business

Pure mathematics

Scheduling (production processes)

Mathematics

Biology

Data mining

Variety (cybernetics)

Manufacturing engineering

Engineering

Marketing

Manufacturing

Paleontology

Artificial intelligence

Systems engineering

Operations management

Data science

Industry 4.0

Context (archaeology)

Computer science

Big data

Field (mathematics)

Smart manufacturing

International Journal of Production Research

The machine learning (ML) field has deeply impacted the manufacturing industry in the context of the Industry 4.0 paradigm. The industry 4.0 paradigm encourages the usage of smart sensors, devices, and machines, to enable smart factories that continuously collect data pertaining to production. ML techniques enable the generation of actionable intelligence by processing the collected data to increase manufacturing efficiency without significantly changing the required resources. Additionally, the ability of ML techniques to provide predictive insights has enabled discerning complex manufacturing patterns and offers a pathway for an intelligent decision support system in a variety of manufacturing tasks such as intelligent and continuous inspection, predictive maintenance, quality improvement, process optimisation, supply chain management, and task scheduling. 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 topics such as edge computing and cybersecurity aspects of smart manufacturing. Hence, this special issue is focused on bringing together a wide range of researchers to report the latest efforts in the fundamental theoretical as well as experimental aspects of ML and their applications in manufacturing and productionsystems.

Machine learning in manufacturing and industry 4.0 applications