The blockchain technology selection in the logistics industry using a novel MCDM framework based on Fermatean fuzzy sets and Dombi aggregation

Logistics is a sophisticated system involving third-party logistics (3PL) providers, freight forwarders, warehousing companies, and transport service producers in various transport modes, such as road, rail, air, maritime, and multimodal transportation. Furthermore, it is possible to add customs clearance agencies, insurance companies, banks, and relevant institutions and organizations to the system. Effective logistics systems must sustainably provide customers with quality and satisfactory logistics services using data shared over advanced technologies. Nevertheless, data and information on logistics are usually challenging to collect, process, and understand, as they are primarily unstructured, unreachable, and unstandardized. Blockchain is a new and advanced technology promising to eliminate or mitigate the adverse effects of these difficulties. However, blockchain technology practices in the logistics industry are extraordinarily scarce; and a few blockchain platforms have attempted to produce solutions for a few large-scale and global logistics firms. Hence, the digital transformation process involving blockchain technology for a logistics company encounters the challenge of selecting an appropriate blockchain platform for the logistics industry's needs. Although studies have been carried out in the relevant literature to select a suitable blockchain platform for various industries, few of these studies dealt with selecting the best blockchain platform for the logistics industry. Hence, experimental studies on choosing the proper blockchain platform in the logistics industry that tries to manage outstandingly complicated relations and linkages among stakeholders are currently inadequate. The current study presents a novel, robust, practical, and powerful decision-making tool that can also overcome highly complex uncertainties to identify the most feasible blockchain technology for the logistics industry. The robustness of the study's findings is validated with comprehensive sensitivity and comparative analyses.