Process-oriented risk assessment methodology for manufacturing process evaluation

A process-oriented risk assessment methodology is proposed. Risks involved in a process and the corresponding risk factors are identified through an objectives-oriented risk identification approach and evaluated qualitatively in the Process FMEA. The critical risks of the PFMEA are then incorporated in the process model for further quantitative analysis employing simulation technique. Using the proposed methodology as a decision-making tool, alternative scenarios are developed and evaluated against the developed risk measures. The risk measures values issues out of simulation are normalized and aggregated to form a global risk indicator to rank the alternative processes on the basis of desirability. The methodology is illustrated with a case study issued from parts manufacturing but is applicable to a wide range of other processes

BAYESIAN-INTEGRATED SYSTEM DYNAMICS MODELLING FOR PRODUCTION LINE RISK ASSESSMENT

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