Supply chain risk modeling and management in a globally integrated enterprise

In the last few decades, there has been an increased focus on supply chain risk management due to the increasing occurrence of natural and man-made risk events and the extent of their impact on supply chains. Risk events represent a daily challenge to supply chains because they can cause disruptions that potentially have negative impacts on supply chain operations. In order to stay ahead of competitors and reduce long-term damage to their businesses, supply chains must effectively respond to the risk events and recover quickly. Supply chain risk identification and assessment are important steps in supply chain risk management.;This study proposes a comprehensive framework that utilizes simulation and optimization techniques for managing risks in supply chains. The proposed framework assists in structuring the origins of the risks in an attempt to prevent them and/or provide an effective response to address them. The framework consists of five main steps. These five steps are linked to the five steps of the DMAIC cycle, which are: define, measure, analyze, improve, and control. A fuzzy inference system is developed to identify, measure, and prioritize the risks. A simulation-optimization approach is used to assess the risks and identify the best mitigation strategies.;To demonstrate the suitability of the proposed framework for strategic and tactical planning, case studies from a high-end server manufacturing supply chain are considered.;The case studies focus on the assessment of different risks in the high-end server manufacturing environment, including supply and demand risks, new product introduction risks, and transportation risks. Risk factors are identified by the risk management team in the company and the factors' likelihoods and impacts are obtained through surveys. The factors' estimates are aggregated using bow-tie analysis to calculate the total risk likelihood and impact. A fuzzy risk assessment system is then used to calculate the total risk scores considering risk and risk management factors. The risks are then aggregated per product type. Analytical results showed that the aggregated risks for the two main products produced by the company, product 1 and product 2, are relatively low with values of 22% and 19%, respectively. However, the risk associated with product 1 may require the implementation of some mitigation strategies to further reduce the risk. Risk Mitigation Matrix (RMM), which is a two-dimensional matrix where the columns represent the risks and the rows represent the mitigation strategies, is used to identify risk correlations. The best combination of strategies in the RMM can be identified using optimization models with a single objective or multiple objectives. However, the selected mitigation strategies may affect other areas in the supply chain. For this reason, an optimization model is formulated for the whole supply chain considering risks and risks' mitigation. The optimization model is a multi-objective model that includes three objectives: total profit, total lead time, and total risk reduction. The model also considers the deterministic features of the supply chain, and a simulation model is then developed to represent the stochastic features. Both models communicate to achieve the optimal risk reduction, profit, lead time, mitigation strategies, and order and inventory allocation. Analytical results show that for the data considered in this study the models can converge after 5 iterations. Furthermore, it is shown that changing the risk reduction goal value will affect the total profit and lead time. Decision makers can identify the best value for a risk reduction that results in the optimal values for both total profit and lead time.