PurposeDue to the fierce market competition, many organizations seek global suppliers because of lower procurement costs and better product quality. However, selecting suitable global suppliers is one of the complicated decision-making tasks for decision-makers due to the involvement of various qualitative and quantitative factors. The primary purpose of this research is to design an integrated approach for global supplier selection and order allocation in the context of developing an environment-friendly supply chain under data uncertainty.Design/methodology/approachInitially, the fuzzy analytical hierarchy process (FAHP) is used to calculate the selected criteria weights. After that, the weights obtained from FAHP are inserted into the fuzzy technique for order preference by similarity to ideal solution (FTOPSIS) to examine the performance of selected suppliers and determine their final ranks. Finally, the obtained results from FTOPSIS are incorporated into the multi-choice goal programming (MCGP) model, which involves multi-aspiration levels to allocate the optimal order quantity to the selected global suppliers.FindingsA real-time case study of the automotive industry is presented to demonstrate the efficiency and practicality of the suggested approach. The case study and sensitivity analysis results show that the proposed model effectively tackles suppliers' evaluation and order allocation data uncertainty.Originality/valueIncorporation of risks, environmental management and economic factors during global supplier selection in the automotive sector has not been given much attention in the past literature. So, this research aims to fulfill the gap by developing an integrated approach that can tackle data uncertainty effectively.
The emergence of the underlying blockchain technology of bitcoin has gained extensive attention from researchers and practitioners. As distributed ledger technology, blockchain widely finds its applications in the supply chain to mitigate issues related to transparency, information sharing, process efficiency, and traceability. This study employed a knowledge-based visualization technique to create a vision beyond other review studies on the blockchain-based supply chain. We used bibliometric and network analysis to synthesize the previous literature. In total, 431 articles in the timespan of 2017 to April 2022 from Scopus and Web of Science (WOS) databases were analyzed after applying search string, inclusion, and exclusion criteria. Basic information was extracted from initial data screening; then, data was analyzed on the grounds of co-occurrence, bibliographic coupling, citation, co-authorship, and co-citation analysis. In addition, thematic analysis was performed to analyze the content of the previous studies, adopted research methods, and dynamic industries in the literature. Besides all these, we identified various research gaps and proposed research directions for future study. We believe that this study provides adequate knowledge to academic scholars and supply chain practitioners to fast-track the current research in the supply chain domain using blockchain technology.
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