Year of Publication

2018

Degree Name

Master of Science in Mechanical Engineering (MSME)

Document Type

Master's Thesis

College

Engineering

Department

Mechanical Engineering

First Advisor

Dr. Fazleena Badurdeen

Abstract

Companies, across the globe are concerned with risks that impair their ability to produce quality products at a low cost and deliver them to customers on time. Risk assessment, comprising of both external and internal elements, prepares companies to identify and manage the risks affecting them. Although both external/supply chain and internal/production line risk assessments are necessary, internal risk assessment is often ignored. Internal risk assessment helps companies recognize vulnerable sections of production operations and provide opportunities for risk mitigation.

In this research, a novel production line risk assessment methodology is proposed. Traditional simulation techniques fail to capture the complex relationship amongst risk events and the dynamic interaction between risks affecting a production line. Bayesian- integrated System Dynamics modelling can help resolve this limitation. Bayesian Belief Networks (BBN) effectively capture risk relationships and their likelihoods. Integrating BBN with System Dynamics (SD) for modelling production lines help capture the impact of risk events on a production line as well as the dynamic interaction between those risks and production line variables. The proposed methodology is applied to an industrial case study for validation and to discern research and practical implications.

Digital Object Identifier (DOI)

https://doi.org/10.13023/etd.2018.386

Available for download on Saturday, October 19, 2019

Included in

Manufacturing Commons

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