Digital Replication of Product Flow in a Serial Production Line

Abstract

In the wake of the Covid-19 pandemic, North American Electronics Manufacturing Service (EMS) providers hope to “re-shore” potential business from companies currently reliant on offshore manufacturing. To capitalize on this opportunity, EMS providers must maximize efficiency to offer competitive rates. The Surface Mount Technology (SMT) assembly line is one area where increased efficiency can significantly impact plant capacity. SMT machines generate data describing their operating conditions, which can be leveraged to improve the efficiency of the production process. Discrete Event Simulation (DES) has been used across many industries to model complex systems and determine optimal operating parameters. In this research, a framework is proposed to evaluate the impact of “what-if” scenarios by digitally replicating the flow of product on an SMT assembly line at a Canadian EMS provider. The framework includes a flexible data management system for collecting, processing, and storing production data from multiple sources, a technique for addressing gaps in machine data using work in process (WIP) information, and a novel approach to DES where historical data is input directly to the model. The framework was applied to two industrial applications where the first saw potential cost savings of $270,000, and the second saw a projected throughput gain of 7%.