Assessment of Supply Chain Designs Considering Uncertainty via a Monte Carlo Simulation

In today’s volatile and uncertain environment, designing resilient and costeffective supply chains is increasingly complex. This paper presents a Monte Carlo simulation-based approach for supplier selection and order allocation (SSOA) under uncertainty. We propose a dual-criteria decision-making model that evaluates both procurement-related costs and service level, measured by days without stock. The model incorporates probabilistic inputs for demand fluctuations, delivery delays, and supplier stockout risks. For the development of our modelling approach, we define seven sourcing scenarios for which we simulate a 12-month period using real-world inspired data. Risk measures such as Value-at-Risk (VaR) and Conditional VaR (CVaR) are used to assess worst-case outcomes. The study highlights the value of simulation in capturing complex uncertainties and supporting strategic sourcing decisions.