An Integrated Spherical Fuzzy Approach for Global Supplier Selection

Political-economic liberalization, modern communication technologies, and low-cost transportation have prompted companies to outsource an increasing amount of commercial activities to other countries. This exporting has often resulted in complex supply chain configurations. Considering that human and ecological regulation in these countries is often insufficient or poorly enforced, stakeholders place the responsibility of ensuring socially and environmentally sustainable product specifications across their supply chains on firms with particular emphasis. This research presents a combined fuzzy spherical AHP-WASPAS method basis for environmental global procurement planning that takes subsupplier sustainability considerations into account. From the present study, standards (containing risk considerations) were devised and then improved with the assistance of experts in the field and case decision-makers in order to avoid literature bias. Next, providers and semi were evaluated collectively using the agreed sustainability criteria. In previous studies, this methodology proved unsatisfactory. The problem is rectified in two stages, as shown below. In the first phase, fuzzy AHP is used to define parameter weights for inclusive sustainable supplier evaluation, so in the second phase, fuzzy WASPAS is employed to assess supplier performance in relation to evaluation criteria. Economic factors held the highest weight among some of the five ecological sustainability (financial, health, environment, and social), whereas worldwide risk assessments carried the least. This research suggests that global threats are not yet important selection factors for suppliers. Moreover, the proposed framework may help pave the way for the creation of managerial decision-making tools to aid firms in tackling sustainability risks that occur farther downstream in their supply chains. When major organizations are under intense scrutiny from their stakeholders, these vital tools are extremely important.