LAUSR

Abstract Integrating quantitative and qualitative data is a challenge in global supply chain decision making. Decision support tools should be easier to use and focus on providing transparency and supporting human judgement. A strong emphasis on multi-objective models and trade-offs is needed. We investigate methods to solve strategic global supply chain network design problems and develop a solution framework, comprised of mixed-integer linear programming, the Analytical Hierarchy Process and the Pareto front. It is applied in a case study in the med-tech industry. Results show that the framework is easy to use for practitioners, accommodates qualitative as well as quantitative criteria and provides transparency over the entire range of efficient solutions. Managers can use it to discuss and select the configuration best fitting to their overall strategy. We believe the framework and application results can contribute to the development of more effective and user-friendly decision support tools for strategic global network design.