An inverse optimization approach for studying sustainability preferences in sourcing decisions

Throughout many societies around the globe, there is growing awareness of the urgent need for the transition towards a sustainable economy. Research shows that buying firms have substantial leverage to initiate sustainable development by controlling the sustainable performance of their suppliers. In that context, this article presents a novel methodology based on inverse optimization to derive the implicit preferences of decision-makers in the trade-off between traditional sourcing objectives and sustainability in the supplier selection and order allocation process. The derived implicit preferences can then be used for further analyses to gain a better understanding of the characteristics of purchasing managers and sourcing situations that come with particularly high/low preferences placed on sustainability. Since the inverse optimization approach is computationally resource-intensive and consumes a significant amount of time, we present a scalable state-of-the-art cloud architecture that allows solving an arbitrary number of optimization programs in an acceptable amount of time. We demonstrate the feasibility of the proposed methodology in a real-world case. In doing so, we test how important sustainability aspects are in the supplier selection and order allocation decisions of one of the world's largest automotive parts manufacturers. • An inverse optimization approach for studying sourcing preferences is presented. • The derived implicit preferences can be used for further analyses. • A cloud architecture solving an arbitrary number of optimization programs is shown. • The feasibility of the proposed methodology is demonstrated in a real-world case. • We test the importance of sustainability in a real-world supplier selection case.