A cyber-physical system architecture based on lean principles for managing industry 4.0 setups

ABSTRACT

Cyber-Physical Systems improve productivity and competitiveness of enterprises by connecting manufacturers with the entire supply chain using information and communications technology. Lean principles are commonly applied to improve processes and create flawless material and information flows. However, integrating lean principles into Industry 4.0 has not been well elaborated in previous studies to address dynamic changes and complex material and information flow in processes. In this study, a comprehensive framework for integrating lean principles and industry 4.0 is proposed for addressing the issues mentioned above. A lean-based architecture is developed in an Industry 4.0 context to enhance connectivity between Industry 4.0 elements and enable better information exchange efficiency with improved decision-making accordingly. Besides, a smart value stream mapping VSM 4.0 is proposed to improve materials and information flow and allow real-time self-decision-making throughout production processes by utilizing the Industrial Internet of Things (IIoT) technologies. The suggested Lean-based Industry 4.0 Architecture integrated with VSM 4.0 allows better system efficiency and responsiveness, controllability, and robustness against random failures.

KEYWORDS:

• Cyber physical systems
• industry 4.0
• lean-based architecture
• decision-making
• VSM 4.0
• lean 4.0

Disclosure statement

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

Correction Statement

This article has been republished with minor changes. These changes do not impact the academic content of the article.

Additional information

Funding

This research work is not funded by any organization.

Notes on contributors

Amr Nounou

Amr Nounou obtained Dr.-Ing. in Mechanical Engineering as well as M.Sc. in Textile and Clothing Engineering (at the faculty of Mechanical Engineering) from The Technische Universität Dresden and B.Sc. in Mechanical Engineering from The American University in Cairo. His research interests are in the areas of lean manufacturing, Six Sigma, thermal ergonomics, facilities planning and strategic Management. He has more than ten years of experience in the industry in the fields of production management, product development, project management, change management and strategic planning.

Hadi Jaber

Hadi Jaber is currently an Assistant Professor of Industrial Engineering and Management at Abu Dhabi University, UAE. He served previously as an Assistant Professor of Industrial Engineering and Management at the American University of Beirut, the American University of the Middle East, and Paris-Saclay University, France. He accomplished all his undergraduate and graduate studies in France, where he obtained with top honors his Ph.D. in Industrial Engineering from Paris-Saclay University in February 2016, his Master’s degree in systems engineering in 2012, and his M.Sc. in industrial systems and projects in 2012. His professional experience includes roles as Systems Engineer at the French Atomic Energy Commission; Quality Engineer at RENAULT within the Strategy of Quality Management Department; and Quality Engineer at TOTAL within “Managing Procurement Risks in Complex Projects” unit. His research focuses mainly on industrial organization and Management. He has sixteen articles indexed in Scopus, including several in top-tier journals.

Ridvan Aydin

Ridvan Aydin received the Ph.D. degree in Industrial and Systems Engineering from the Hong Kong Polytechnic University in 2016. He completed a postdoctoral research project funded by a federal agency in the Institute for Sustainable Manufacturing, University of Kentucky in 2018. He is currently an Assistant Professor in the Department of Industrial Engineering and Engineering Management at the University of Sharjah, UAE. His research interests include multi-lifecycle product design, new product development, remanufacturing, closed-loop supply chain design, managing product returns and multi-objective optimization modelling.