Integrated approach for optimizing quality control in international manufacturing networks

Abstract

This article aims at providing an integrated approach for optimizing quality control in International Manufacturing Networks (IMN) which can be characterized by consisting of numerous plants acting autonomously according to an individual target system. A key challenge is to ensure the overall process quality despite distributed value creation processes and differing target systems in dynamic environments. Hence, the developed approach allows for identifying potentials in the quality control strategy using a value stream-based method to visualize quality characteristics and procedures in the production process chain. Furthermore, the approach contains a framework for identifying possible improvement measures and a simulation-based evaluation concept to evaluate the effects of different measures with respect to individual target systems. The simulation combines elements of a discrete-event simulation in order to depict the value stream with agent-based modelling for realizing different target systems by considering distinctive plant roles. The article concludes with a case study of a globally operating automotive supplier to apply the approach. A forward research agenda is proposed that evaluates the approach in multiple cases, deriving patters across companies or industries.

Keywords:

• Quality control
• international manufacturing network
• plant roles
• simulation-based evaluation

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work was supported by the Deutsche Forschungsgemeinschaft (German Research Foundation) under Grant LA 2351/28-1.

Notes on contributors

Tobias Arndt

Tobias Arndt serves as the General Manager of the Global Advanced Manufacturing Institute in Suzhou since 2017. He completed his industrial engineering studies at RWTH Aachen University (Germany) with a diploma in 2012. During that time, he also received his Master of Science at the Tsinghua University in Beijing. Between 2012 and 2017, he has been working as research associate and project manager at the Institute of Production Science (wbk) at Karlsruhe Institute of Technology (KIT). His research topics focus the area of quality management strategies in global production networks, in which he also led several research and consulting projects.

Mukesh Kumar

Mukesh Kumar is the Head of Industrial Resilience Research Group and a lecturer in Industrial Engineering and Operations Management, Institute for Manufacturing, University of Cambridge. His main research and practice interests are in the areas of sustainability, risk and resilience in emerging and developed industrial systems. He has developed risk management processes for global manufacturing investment decisions and supply networks. Before joining the University of Cambridge, his previous roles were in the financial sector as a senior analyst and corporate finance consultant. He holds a PhD from the University of Cambridge in the area of Manufacturing Investment Risk.

Gisela Lanza

Gisela Lanza is a member of the management board of wbk Institute for Production Technology of Karlsruhe Institute of Technology (KIT). She heads the division Production Systems, which focuses on global production strategies, production system planning and quality assurance. She is an active member of the scientific advisory board of the German Academy of Engineering Sciences (acatech) and the national platform Industrie 4.0, as well as the Steering Committee of the Allianz Industrie 4.0 Baden-Württemberg. Ms Lanza deals with the development of analytical methods for identifying and improving given weak points of a production system.

Manoj Kumar Tiwari

Prof. Manoj Kumar Tiwari is working in the domain of Manufacturing System and Supply Chain Management. His research and teaching interests are in modelling the Manufacturing Processes and Operations analysis in Supply Chain Networks. Optimization, Simulation and Computational Intelligence are the main techniques adopted by Prof. Tiwari to automate the decision support system for complex and large-scale problems in Manufacturing and Logistics System. His research interests have been supported by several industries, national and international research funding agencies. He is selected as Fellow of IISE USA. He is also fellow of Indian National Academy of Engineering.