A Novel Approach to Modeling of Production System: A Case Study at a Small/medium-sized Manufacturer

Abstract

High-fidelity mathematical models are essential to implement model-based analysis and control in manufacturing research and practice. Currently, such models are typically conducted manually in an ad hoc manner. This approach presents several limitations, especially to small and medium-sized manufacturers, such as unavailability of equipment status data, inconvenient data collection process, non-standard and non-unique modeling rules, etc. In this paper, we describe a case study at a local small manufacturer of medical devices and apply a novel approach of production system modeling to overcome various practical challenges in collecting up- and downtime data of the operations. Specifically, the parametric model of the production system is identified based on system performance metrics derived from the parts flow data. With the model constructed, system bottleneck is analyzed and then, to enhance system throughput, potential improvement actions including operation speed-up, downtime reduction, and buffer expansion are explored. Finally, model sensitivity is analyzed by comparing the deviation of the model-predicted performance metrics to those produced by a reference nominal model.