Distributing decision-making authority in manufacturing – review and roadmap for the factory of the future

Abstract

The question of the benefits of autonomous control is more important than ever: production managers, governments and society hope that the vision of smart and digital production systems with high flexibility and low costs may save the value adding and therefore welfare in the high wage, industrialised countries. At the same time, the discussion on the social implications of autonomous objects and decentralised control approaches is growing. Looking back on the history of production research and practice, we find that there has been a constant ply among scholars and production managers between the advantages of the two concepts of centralised and decentralised control approaches. In this article, we study the concept of autonomy in production planning and control, enabled by cyber-physical systems and the distribution of decision-making authority. Based on a profound structured literature review, we analyse the perception of autonomy, the technological requirements and the increasing complexities of modern smart manufacturing. Moreover, we find that recently several research streams suggest the advantages and benefits of autonomous control concepts compared to traditional centralised approaches based on qualitative analysis and identify a distinct lack of quantitative results.

Keywords:

• Autonomy
• autonomous production control
• decentralised control
• industry 4.0
• production planning and control

Data availability statement

The authors confirm that the data supporting the findings of this study are available within the article or its supplementary materials.

Disclosure statement

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

Additional information

Notes on contributors

Oliver Antons

Oliver Antons was born in Papenburg, Lower Saxony, Germany, in 1990. He received his B.S. and M.S. in Mathematics from the Rheinisch – Westfälische Technische Hochschule Aachen (RWTH), Aachen, Germany, in 2015 and 2018, respectively. He is currently pursuing his doctorate in Economics at RWTH, Germany. He was a research of Management of Digitalisation and Automation from 2018 to 2019. He has been a Research Associate in RWTH's Department of Management Science, since 2019. He is also a Doctoral Researcher at Fraunhofer Institute for Factory Operation and Automation (IFF) since 2020. His research interests include the dualism of centralised and distributed control, autonomous entities and machine learning approaches in production planning and control.

Julia C. Arlinghaus

Julia C. Arlinghaus received her Diploma degree in Engineering Management from the University of Bremen, Bremen, Germany, in 2007, and her doctorate degree in Business Innovation focused on supply chain design, management and performance from the University of St. Gallen, St. Gallen, Switzerland, in 2011. She is Director of the Fraunhofer Institute for Factory Operation and Automation (IFF), Magdeburg, Germany. She was Operational Excellence and Lean Management Consultant with Porsche until being appointed Associate Professor of Network Optimisation in Production and Logistics at Jacobs University Bremen in 2013, and then Professor of the Management of Industry 4.0 at RWTH Aachen in 2017. She received the DAAD Fellowship to attend the University of Tokyo. She holds Chair of Production Systems and Automation in the Department of Mechanical Engineering at Otto von Guericke University Magdeburg, Magdeburg, Germany. Together with her team, she researches in and provides businesses consulting services related to risk-optimised supply-chain design, production planning and control system implementation, digitalised and efficient manufacturing operations, and frugal innovation and its implications for the design and management of manufacturing and logistics operations in developing countries.