A sensor-driven operations and maintenance planning approach for large-scale leased manufacturing systems

Abstract

The rise of mass customisation drives manufacturers to adopt leasing agreements for their machinery rather than outright their ownership. This industrial trend results in large-scale leased manufacturing systems, where the asset conditions and maintenance requirements for a fleet of machines and/or production lines from several manufacturers are continuously monitored and managed by a lessor. In this paper, we propose an operations and maintenance planning model that explicitly models (i) dynamic real-time failure rate predictions for machines based on sensor-driven degradation data, (ii) optimal routes for maintenance teams across a number of geographically-distributed manufacturing sites, and (iii) operational outcomes. The model can handle complex maintenance and operational outcome interdependencies between multiple machines, and incorporate demand for different products, heterogeneous machine capacities and capabilities, factory topology, and the resulting production flow capacities of products. To demonstrate the model’s practicality, it is applied to three experimental case studies with various numbers of machines, operating schedules, product types, and flow capacities. The results show that the proposed model significantly outperforms the traditional reliability-based maintenance model in key metrics such as the number of unexpected failures, the cost of preventive maintenance actions, machine downtime, the percentage of unsatisfied demand, and total cost.

KEYWORDS:

• leased machines
• prognostics
• sensor-driven prescriptive analytics
• operations and maintenance
• team routing
• asset management

Disclosure statement

No potential conflict of interest was reported by the authors.

Data availability statement

Due to the nature of the research, the commercial supporting data used in this research is not available.

Additional information

Notes on contributors

Şakir Karakaya

Şakir Karakaya received his Ph. D. (2018) and MSc (2008) degrees in the Industrial Engineering Department from the Middle East Technical University (Türkiye). He worked as a postdoctoral fellow at Wayne State University (Department of Systems and Industrial Engineering) in 2022-2023. He also completed an MSc programme on Public Management and Governance at the London School of Economics and Political Science (LSE) in 2016. He has been working for the Ministry of Industry and Technology of Türkiye since 2004 and currently is the Head of Market Surveillance and Product Safety Department. His research interests are quality and product management, sensor-driven operations and maintenance management, and public management.

Murat Yildirim

Murat Yildirim is an assistant professor in the Department of Industrial and Systems Engineering, and the Director of Cyber Physical Systems Laboratory at Wayne State University. He obtained a PhD degree in industrial engineering, MSc degree in operations research, and BSc degrees in electrical and industrial engineering from Georgia Institute of Technology. Dr. Yildirim’s research interest lies in advancing the integration of mathematical programming and data analytics in various application domains. Specifically, he focuses on the modelling and the computational challenges arising from the integration of real-time inferences generated by advanced data analytics and simulation into large-scale mathematical programming models used for optimising and controlling networked systems. His research has been supported through multiple projects funded by NSF, DoE, MTRAC and Ford.

Nagi Gebraeel

Nagi Gebraeel is the Georgia Power Early Career Professor in the Stewart School of Industrial and Systems Engineering at Georgia Tech. His research interests lie at the intersection of predictive analytics and machine learning for maintenance, repairs, and service logistics. He is the director of the Predictive Analytics and Intelligent Systems (PAIS) research laboratory. He is a Fellow of the Institute of Industrial and Systems Engineers and a member of the Institute for Operations Research and the Management Sciences (INFORMS).

Tangbin Xia

Tangbin Xia received a Ph.D. degree in Mechanical Engineering (Industrial Engineering) from Shanghai Jiao Tong University, Shanghai, China, in 2014. He was a Postdoctoral with the H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, GA, USA, and a Joint Ph.D. Student with the S.M. Wu Manufacturing Research Centre, University of Michigan, Ann Arbor, MI, USA. He is currently an Associate Professor and the Vice Dean at the School of Mechanical Engineering, Shanghai Jiao Tong University. His research interests include intelligent maintenance systems, prognostics and health management, and advanced manufacturing. Dr. Xia is a Member of IISE, IEEE, ASME, and INFORMS.