Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE credits
Date: 2 of June 2024
Level: Master Thesis in Product – and Process Development, 30 ECTSInstitution: School of Innovation, Design, and Engineering (IDT), at Mälardalen University
Authors: Ali Alhussain, Ahmad Obiedallah,
Title: Using Discrete-Event Simulation for Bottleneck Identification and Formulation of Future-State Scenarios: A Case Study about painting shop floor
Supervisor: André Löfqvist, Volvo CE, Stavros Vouros, Mälardalen University
Keywords: Discrete event simulation, Production system development, Bottlenecks identification, Painting shop floor.
Aim: The study aims to develop a DES model to identify and enhance bottlenecks in production systems by providing the current production scenario, enabling effective problem-solving and giving a future image of the possible improvements.
Research questions: RQ1: How can a discrete event simulation model be developed to be used in the improvement of manufacturing processes? RQ2: How can a simulation model be used for bottleneck identification, and what are the potential enhancements?
Methodology: This research utilised a deductive methodology, allowing theoretical assumptions to shape the empirical investigation and maintain a continuous dialogue between data analysis and model validation. Empirical data were obtained through methods such as observation, time studies, and documentation. Academic sources were sourced from Scopus, focusing exclusively on peer-reviewed journal articles and book chapters. The construction of the conceptual model adhered to the guidelines outlined by Banks (2005). Drawing on the model construction and subsequent findings from the conceptual model, actionable insights were derived.
Conclusion: The study showed that discrete event simulation (DES) effectively identifies and reduces bottlenecks in manufacturing, specifically at Volvo's paint shop. It developed a DES model through systematic data collection, allowing for scenario testing and process optimization. The research focused on two main questions: building a tailored DES model and applying it to address production inefficiencies. Key findings indicated that the model successfully enhanced production flow and efficiency by identifying critical bottlenecks and suggesting improvements. This confirms DES as a vital tool for enhancing manufacturing operations and continuous process improvement.
2024. , p. 45