Reconfiguration Assessment for Production Volume Changes Using Discrete-Event Simulation : A Large-Size Highly-Customized Product Case Study

Globalization and mass customization are commonly translated into increased levels of complexity in manufacturing systems. One of the main reasons is the increased number of variables, parameters, and interrelations on the shop floor. This intrinsic complexity can grow exponentially when considering the manufacture of large-size products with high levels of variability and variants: the mass production of large recreational motorboats with high levels of customization and low production volumes, mass customization. With the increasing role of sustainability and concepts of Industry 5.0, focusing not just on improving production systems but also human wellbeing, quick decision making becomes essential. Data and digitalization are becoming the cornerstone for system improvement, and digital data availability and analysis can facilitate the utilization of computerized tools to support decision making and maximize the performance of complex systems. For that purpose, simulation can be a powerful analytical tool to design, maintain, and improve complex manufacturing systems. Simulation techniques usually allow handling the size and complexity commonly associated with manufacturing systems. However, in systems with highly customized and large-size products, manual processes, and limited floor space, the implementation of simulation techniques is not straightforward, especially considering the aspects of variability, data collection, model validation, and system reconfiguration. With a particular focus on large-size products and limitations of a constrained existing facility layout, this paper presents the implementation of a simulation-based reconfiguration assessment considering manual production, assembly, and internal logistics requirements. Going through an industrial case study of large recreational motorboats manufacturing, the paper analyses the system analysis, data collection, implementation, and validation of the methodology step by step. Considering different w
CC BY-NC 4.0Corresponding Author, Enrique Ruiz Zúñiga, JSPS Research Fellow, Systems Design Laboratory, Kyoto University, Kyoto, 615-8540, Japan; Department of Intelligent Automation, University of Skövde, Högskolevägen, Box 408, Skövde, 541 28, Sweden; E-mail: enrique.ruiz.zuniga@his.