Application of a novel approach of production system modelling, analysis and improvement for small and medium-sized manufacturers: a case study

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.

KEYWORDS:

• Production system
• exponential machine
• parameter identification
• modelling
• smart manufacturing

Disclosure statement

Dr. Liang Zhang have financial interests and/or other relationships with Smart Production Systems LLC, Ann Arbor, MI, USA.

Data availability statement

The authors confirm that the data supporting the findings of this study are available within the article [and/or] its supplementary materials.

Additional information

Funding

This work is supported in part by the U.S. National Science Foundation (NSF), [grant number FM-2134367].

Notes on contributors

Yuting Sun

Yuting Sun received the B.S. and M.S. degrees in Statistics, and the M.S. degree in Industrial Engineering from University of Minnesota Twin Cities, Minneapolis, MN, USA, in 2014 and 2018, respectively. She is currently pursuing the Ph.D. degree with the Smart Production Systems Lab, Department of Electrical and Computer Engineering, University of Connecticut, Storrs, CT, USA. Her research interests include modelling, analysis, improvement and visualisation of smart manufacturing systems, and the application of machine learning, numerical computation and engineering optimisation methods.

Liang Zhang

Liang Zhang received the B.E. and M.E. degrees from the Center for Intelligent and Networked Systems, Department of Automation, Tsinghua University, Beijing, China, in 2002 and 2004, respectively, and the Ph.D. degree in Electrical Engineering-Systems from the University of Michigan, Ann Arbor, MI, USA, in 2009. He was with the Department of Industrial and Manufacturing Engineering, University of Wisconsin–Milwaukee from 2009 to 2013. He is currently an Associate Professor with the Department of Electrical and Computer Engineering, University of Connecticut, Storrs, CT, USA. His research interests include modelling, analysis, improvement, design, control, and smart operations of manufacturing systems. He is a co-founder of Smart Production Systems LLC.