Application of a novel approach of production system modelling, analysis and improvement for small and medium-sized manufacturers: a case study. Issue 10 (19th May 2023)

• Record Type: Journal Article
• Authors: Sun, Yuting; Zhang, Liang
• Is Part Of: International journal of production research. Volume 61:Issue 10(2023)
• Page Start: 3279
• Page End: 3299
• Publication Date: 2023-05-19
• Subjects: Production system -- exponential machine -- parameter identification -- modelling -- smart manufacturing; Factory management -- Periodicals; 658.57
• DOI: 10.1080/00207543.2022.2079015
• Languages: English
• ISSNs: 0020-7543

Abstract

ABSTRACT: With the great opportunities created by the new advances in Industry 4.0, many manufacturers are testing and investing in new equipment and infrastructure to deploy these technologies. However, there are a huge number of small and medium-sized manufacturers (SMMs) that are lagging behind due to the lack of in-house R&D capabilities and workforce shortage and/or financial constraints to afford such investment. Additionally, application of theoretical production research in SMMs often confront challenges such as low data availability and data quality, etc. In this paper, we describe a case study at a local medium-sized manufacturer of electromechanical devices for industrial, consumer, and medical applications, who was struggling to meet ever-growing market demand, and apply a novel approach of production system modelling to overcome the challenge of unavailability of the operation up- and downtime data. Specifically, the parametric model of the production system is identified using several system performance metrics derived based on the parts flow data of the in-process buffer. With the mathematical model constructed, the system bottleneck is analysed and a number of improvement scenarios are explored that can potentially enhance the system throughput. Finally, model sensitivity is analysed by calculating the deviation of the model-predicted performance metrics to those produced by a reference nominal model. This analysis demonstrates that the model constructed using our proposed approach is robust even when the system parameters vary from the baseline ones.