A state-of-the-art on production planning in Industry 4.0

Abstract

The Industry 4.0 revolution is changing the manufacturing landscape. A broad set of new technologies emerged (including software and connected equipment) that digitise manufacturing systems. These technologies bring new vitality and opportunities to the manufacturing industry, but they also bring new challenges. This paper focuses on the impact of Industry 4.0 on production planning approaches and software. We first propose a digital twin framework that integrates production planning systems and frontier technologies. The frontier technologies that may impact production planning software are the internet of things, cloud manufacturing, blockchain, and big data analytics. Second, we provide a state-of-the-art on the application of each technology in the production planning, as well as a detailed analysis of the benefit and application status. Finally, this paper discusses the future research and application directions in the production planning. We conclude that Industry 4.0 will lead to the construction of data-driven models for production planning software. These tools will include models built accurately from data, account for uncertainty, and partially actuate the decision autonomously.

Keywords:

• Production planning
• industry 4.0
• digital twin
• cloud manufacturing
• blockchain
• big data analytics

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

Data sharing is not applicable to this article as no new data were created or analysed in this study.

Additional information

Funding

The present work was conducted in the project ASSISTANT (https://assistant-project.eu/) that is funded by the European Commission, under grant agreement number 101000165, H2020 – ICT-38-2020, Artificial intelligence for manufacturing.

Notes on contributors

Dan Luo

Dan Luo is a Ph.D. student in Automation, Production, and Computer Science Department at the IMT Atlantique, France. She received the B.S. degree in Logistics Engineering from the school of Logistics Engineering, Wuhan University of Technology, Wuhan, China, in 2015 and M.S. degree in industrial engineering from the school of Mechanical Science and Engineering, Huazhong University of Science and Technology (HUST), Wuhan, China, in 2017. Her research interests include modelling and simulation of manufacturing system and logistics analysis systems, big data-driven logistics and production management, cloud simulation platform, and automated flexible production line of smart factories.

Simon Thevenin

Simon Thevenin is an assistant professor in the Automation, Production, and Computer Sciences Department at the IMT Atlantique, France. He received a Ph.D. from the University of Geneva in 2015 for his work on metaheuristics to solve scheduling problems in production systems. His current research interests focus on optimisation methods for production management, including production scheduling, production planning, and manufacturing line design.

Alexandre Dolgui

Alexandre Dolgui is a Fellow of IISE, a Distinguished Professor of Industrial Engineering, and the Head of the Automation, Production, and Computer Sciences Department at the IMT Atlantique, campus in Nantes, France. His research focuses on manufacturing line design, production planning and scheduling, and supply chain engineering. His main results are based on exact mathematical programming methods and their intelligent coupling with heuristics and metaheuristics algorithms. He has contributed to the theory of assembly line balancing, combinatorial design of machining lines, process planning, supply chain scheduling, lot sizing and replenishment under uncertainties. He is the author of over 700 publications and communications, numerous books and articles, the editor-in-chief of the International Journal of Production Research, an area editor of Computers & Industrial Engineering, a member of the editorial boards for several other journals, including the International Journal of Production Economics, Fellow of the European Academy for Industrial Management, Member of the Board of the International Foundation for Production Research, former Chair of IFAC TC 5.2 Manufacturing Modelling for Management and Control, and Member of IFIP WG 5.7 Advances in Production Management Systems.