Simulation Analysis of Risk of Job Tardiness as a Measure of Manufacturing System Resilience

Resilience in the context of a manufacturing system is the ability to adjust, bounce back, and continue when challenged by unexpected occurrences, uncertainties, and disturbances. Manufacturing systems face a range of challenges: such as equipment malfunctions, supply chain disruption, changes in demand, and external events like natural catastrophes. The interrelationship between risks and system resilience is of dynamic nature, successful risk mitigation will result in increased system resilience, promoting sustainability and adaptability in challenging situations. In this paper, a simulation model of a manufacturing system was developed using Simio software and Simio's scheduling features were utilized to quantify the risk of job tardiness. Different case studies were performed on the simulation model with established due dates of job orders. Alternative routes from process planning tasks for jobs were used, containing scenarios as best, medium, and worst routes. The results obtained from the simulation model were analyzed and risks associated with different job routings corresponding to their due dates were quantified. The resilience of the manufacturing systems was assessed from two perspectives: using better routes to decrease risk and using alternate routes to reduce the risk of equipment malfunction and obtain balanced machine loads.