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

Assessing Production Line Risk Using Bayesian Belief Networks and System Dynamics

Increased complexity in product design, strict regulations and a changing market make risk assessment critical for successful operations. Failure in responding quickly to raw material shortages, downtimes, deteriorating equipment conditions or other operational issues can prove to be an expensive affair. A company-wide risk assessment includes both external and internal operations. However, external/supplier risk assessment has been of major interest. Even though the scope of risk assessment at the production line level is not as broad as it is at the supply chain level, assessing risk would help recognize vulnerable areas of the production line, which would in turn help reduce damage caused when risk events occur. In this research, a method for production line risk assessment is proposed by considering operational risks affecting the line. Operational risks and their causal relationships are represented using Bayesian Belief Networks (BBN). The impact of these risks is observed using a simulation model of the production line using System Dynamics (SD) approach. The combination of BBN and SD assists in developing a versatile methodology, which can capture the dynamic causal mechanisms in a complex system, the uncertainties amongst risk events and the long-term impact of operational risks on the production line