Combining Simulation with Reliability Analysis in Supply Chain Project Management under Uncertainty: A Case Study in Healthcare

Many projects involving supply networks can be logically represented by multiple processing paths. When the supply chain is working under deterministic conditions, computing the total time requested by each path is a trivial task. However, this computation becomes troublesome when processing times in each stage are subject to uncertainty. In this paper, we assume the existence of historical data that allow us to model each stage's processing time as a random variable. Then, we propose a methodology combining Monte Carlo simulation with reliability analysis in order to (i) estimate the project survival function and (ii) the most likely 'bottleneck' path. Identifying these critical paths facilitates reducing the project makespan by investing the available budget in improving the performance of some stages along the path, e.g., by modifying the transportation mode at one particular stage in order to speed up the process. A numerical example is employed to illustrate these concepts.