Framework for Sustainable Risk Management in the Manufacturing Sector

C.F. Oduoza
2020 Procedia Manufacturing  
Risk management is a huge challenge for business managers especially in the manufacturing engineering sector, and if not proactively controlled can lead to under performance and sometimes cessation of activities for some companies. It is common knowledge that poorly managed risks can have an adverse effect on performance while proactive and systematic control of key risk variables in a business environment could generate successful outcomes. The work carried out here has developed a framework
more » ... r risk management affordable and suitable for use especially by small and medium size enterprises in the manufacturing sector. Using a combination of Bayesian Belief Network (BBN) and Analytical Hierarchical Process (AHP) search algorithms, it was possible to search and identify key risk indicators that could undermine business performance (measured in terms of cost, time, quality and safety) from a system database, and thereby manage (monitor, identify, analyse, reduce, accept or reject their impact) them. The conclusion drawn from the study is that risk management for a manufacturing process can be successfully achieved if risk factors which have a negative impact on project cost, quality of delivery, lead cycle and takt time and health and safety of workers can be identified using BBN and minimised using the framework developed in this study. Abstract Risk management is a huge challenge for business managers especially in the manufacturing engineering sector, and if not proactively controlled can lead to under performance and sometimes cessation of activities for some companies. It is common knowledge that poorly managed risks can have an adverse effect on performance while proactive and systematic control of key risk variables in a business environment could generate successful outcomes. The work carried out here has developed a framework for risk management affordable and suitable for use especially by small and medium size enterprises in the manufacturing sector. Using a combination of Bayesian Belief Network (BBN) and Analytical Hierarchical Process (AHP) search algorithms, it was possible to search and identify key risk indicators that could undermine business performance (measured in terms of cost, time, quality and safety) from a system database, and thereby manage (monitor, identify, analyse, reduce, accept or reject their impact) them. The conclusion drawn from the study is that risk management for a manufacturing process can be successfully achieved if risk factors which have a negative impact on project cost, quality of delivery, lead cycle and takt time and health and safety of workers can be identified using BBN and minimised using the framework developed in this study.
doi:10.1016/j.promfg.2020.10.180 fatcat:sgby7dbarffz5iavd6epsspux4