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Smart Lean Manufacturing in the Context of Industry 4.0: A Case Study

Authors: M. Ramadan, B. Salah


This paper introduces a framework to digitalize lean manufacturing tools to enhance smart lean-based manufacturing environments or Lean 4.0 manufacturing systems. The paper discusses the integration between lean tools and the powerful features of recent real-time data capturing systems with the help of Information and Communication Technologies (ICT) to develop an intelligent real-time monitoring and controlling system of production operations concerning lean targets. This integration is represented in the Lean 4.0 system called Dynamic Value Stream Mapping (DVSM). Moreover, the paper introduces the practice of Radio Frequency Identification (RFID) and ICT to smartly support lean tools and practices during daily production runs to keep the lean system alive and effective. This work introduces a practical description of how the lean method tools 5S, standardized work, and poka-yoke can be digitalized and smartly monitored and controlled through DVSM. A framework of the three tools has been discussed and put into practice in a German switchgear manufacturer.

Keywords: Lean manufacturing, Industry 4.0, radio frequency identification, value stream mapping.

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[1] R. Y. Zhong, Q. Y. Dai, T. Qu, G. J. Hu, and G. Q. Huang, “RFID-enabled real-time manufacturing execution system for mass-customization production,” Robotics and Computer-Integrated Manufacturing, vol 29(2), pp. 283-292, 2013.
[2] D. Kolberg, J. Knobloch, and D. Zühlke, “Towards a lean automation interface for workstations,” International Journal of Production Research, vol 55 (10), pp. 2845 – 2856, 2017.
[3] E. Vermillon, “Facilitating lean manufacturing with manufacturing process management in the medical device industry,” Conference: Medical Electronics Symposium, May 2004.
[4] T. Netland, “Critical success factors for implementing lean production: The effect of contingencies” International Journal of Production Research, vol. 54 (8), pp. 2433-2448, 2016.
[5] D. Metz, S. Karadgi, U. Müller, and M. Grauer, “Self-learning monitoring and control of manufacturing processes based on rule induction and event processing”. In eKNOW 2012, the fourth international conference on information, process, and knowledge management, pp. 88-92, 2012.
[6] K. Zhang, et al., "IoT-enabled dynamic lean control mechanism for typical production systems," Journal of Ambient Intelligence and Humanized Computing, pp. 1-15, 2018.
[7] M. Malavasi and G. Schenetti, “Lean manufacturing and Industry 4.0: An empirical analysis between sustaining and disruptive change”, Dissertation, School of Industrial and Information Engineering/politecnico di milano, 2017.
[8] S. Buer, J. Strandhagen, and F.Chan, “The link between Industry 4.0 and lean manufacturing: mapping current research and establishing a research agenda,” International Journal of Production Research, vol.56(8), pp.2924-2940, 2018.
[9] A. Mayr, M. Weigelt, A. Kühl, S. Grimm, A. Erll, M. Potzel, and J. Franke, "Lean 4.0-A conceptual conjunction of lean management and Industry 4.0". Procedia CIRP 72, no. 1, pp.622-628, 2018.
[10] S. Satoglu, A. Ustundag, E. Cevikcan, & M. B. Durmusoglu, “Lean production systems for industry 4.0,” In industry 4.0: Managing the digital transformation, springer, cham, pp. 43-59, 2018.
[11] A. Moeuf, R. Pellerin, S. Lamouri, S. Tamayo-Giraldo, and R. Barbaray, “The industrial management of SMES in the era of industry 4.0,” International Journal of Production Research, vol 92, pp. 1 – 19, 2017.
[12] M. Ramadan, "RFID-Enabled Dynamic Value Stream Mapping for Smart Real-Time Lean-Based Manufacturing System" PhD diss., Dissertation. University Duisburg-Essen, 2016.
[13] H. Kang, et al., “Smart Manufacturing: past research, present findings, and future directions,” International Journal of Precision Engineering and Manufacturing-Green Technology, vol. 3 (1), pp. 111 – 128, 2016.
[14] H. Karre, M. Hammer, M. Kleindienst, and C. Ramsauer, “Transition towards an Industry 4.0 state of the lean Lab at Graz University of Technology,” Procedia Manufacturing 9, pp.206-213, 2017.
[15] T. F. Aydos, and J. C. E. Ferreira, “RFID-based system for lean manufacturing in the context of internet of things,” IEEE International conference on automation science and engineering, CASE 2016, Fort Worth, TX, pp. 1140-1145, 2016.
[16] T. Wagner, C. Herrmann, and S. Thiede, “Industry 4.0 impacts on lean production systems,” Procedia CIRP, vol 63, pp. 125-131, 2017.
[17] A. Sanders, C. Elangeswaran, and J. Wulfsberg, “Industry 4.0 implies lean manufacturing: research activities in industry 4.0 function as enablers for lean manufacturing,” Journal of Industrial Engineering and Management, vol. 9 (3), pp. 811-833, 2016.
[18] Q. Ma, Wang, and Z. Zhao, “SLAE-CPS: Smart lean automation engine enabled by cyber-physical systems technologies,” Sensors 17, vol. (7):22, pp. 1500, 2017.
[19] J. C. Chen, and K. M. Chen, “Application of ORFPM system for lean implementation: an industrial case study,” International Journal of Advanced Manufacturing Technology, vol. 72 (5-8), pp. 839-852, 2014.
[20] T. Meudt, J. Metternich, and E. Abele, “Value stream mapping 4.0: holistic examination of value stream and information logistics in production,” CIRP annals manufacturing technology, vol. 66 (1), pp. 413-416, 2017.
[21] D. Kolberg, J. Knobloch, and D. Zühlke, “Towards a lean automation interface for workstations”, International Journal of Production Research, vol 55 (10), pp. 2845 – 2856, 2017.
[22] R. Chen, "An intelligent value stream-based approach to collaboration of food traceability cyber physical system by fog computing,” Food Control, vol.71, pp.124-136, 2017.
[23] T. Bortolotti, S. Boscari, and P. Danese, “Successful lean implementation: Organizational culture and soft lean practices,” International Journal of Production Economics, 160, pp. 182-201, 2015.