Analyzing early-stage production through discrete event simulation: A Case study at Magna Electronics Developing a simulation model to analyze the performance of a production line and evaluating its ability for decision making at an early stage

Abstract

The highly competitive automotive industry, characterized with varying customer demands and requirements puts pressure on manufacturing organization’s ability to develop flexible and efficient production systems, particularity its early stage. Magna Electronics Vårgårda production system is currently expanding and under constant development through the implementation of new production lines. Therefore, there is a need to understand how system parameters effect performance of the production line in its early stage of development. Hence, the aim of this study is to investigate and analyze the newly implemented production line CHA1 through the development of a discrete event simulation (DES) model. More specifically, understand how DES can be used as a decision-making tool, evaluate the influence of overall equipment effectiveness and direct labor cost as well as highlighting the challenges in this context. The methodology of the study is based on the first nine stages of Banks simulation framework, which were divided into three phases. Within this framework, a literature review was conducted in combination with a mixed method approach. The qualitative data collection provided in-depth understanding of the production line through the combination of interviews, observations and informal conversations. Whereas the quantitative data were collected through shop floor measurement and from the company database, essential for input modeling and the development of the DES model. The experiments conducted through the DES model and the corresponding findings showed that DES can be a useful tool to enhance decision making and evaluate performance in an early stage of production. However, the early stage contributed to several challenges regarding input data collection and a higher degree of simplifications and estimations, effecting the ability to develop a model with the highest possible accuracy.