Process Control Parameters Evaluation Using Discrete Event Simulation for Business Process Optimization
The quest for manufacturing process improvement and higher levels of customer satisfaction mandates that organizations must be equipped with advanced tools and techniques in order to respond towards ever changing internal and external customer demands by maintaining the optimal process performance, lower cost and higher profit levels. A manufacturing process can be defined as a collection of activities designed to produce a specific output for a particular customer or market. To achieve internal and external objectives, significant process parameters must be identified and evaluated to optimize the process performance. This even becomes more important to deal with fierce competition and ever changing customer demands. This paper illustrates an integrated approach using design of experiments techniques and discrete event simulation (Simul8) to understand and optimize the system dynamic based on operational control parameter evaluation and their boundary conditions. Further, the proposed model is validated using a real world manufacturing process case study to optimize the manufacturing process performance. Discrete event simulation tool is used to mimic the real world scenario, which provides a flexible and powerful way to comprehensively understand the manufacturing process variations and allows controlled 'What-If´ analysis based on design of experiments approach. Finally, this paper discusses the potential applications of the proposed methodology in the cable industry in order to optimize the cable manufacturing process by regulating the operational control parameters such as dealing with various product configurations with different equipment settings, different product flows and work in process (WIP) space limitations.
Citation : Kang, P. S., Aboutaleb, A., Silva, C. U., Erhart, A., Todeschini, V. and Duffy, A. (2015) Process Control Parameters Evaluation Using Discrete Event Simulation for Business Process Optimization. Proceedings of the 64th IWCS Conference, pp. 569 - 277, Atlanta, USA, 8 Oct 2015.
Research Institute : Institute of Engineering Sciences (IES)
Peer Reviewed : Yes