Integrated knowledge management in the supply chain: Assessment of knowledge adoption solutions through a comprehensive CoCoSo method under uncertainty

Abstract

The natural dispersion of supply chain (SC) elements and the distribution of knowledge within these elements have necessitated an integrated knowledge management (KM) process in the SC. The use of KM poses many challenges for the members of the SC, and the key to the survival of the SC lies in identifying and solving these challenges. For this reason, this paper aims to appraise the challenges of knowledge adoption in the steel SC and proposes solutions to address these challenges. To determine the significance of challenges in the SC and rank the effective knowledge adoption solutions to address these challenges, a new method called Comprehensive CoCoSo (CoCoCoSo) is introduced based on the CoCoSo method. This method consists of three parts: weighting the challenges, weighting the experts, and ranking the solutions, respectively. In the first part, a new process for weighting the challenges based on CoCoSo is presented; in the second part, the CoCoSo method is enhanced by the ideal average concept for weighting the experts, and finally, a new prescription of CoCoSo is introduced in the ranking part of the knowledge adoption solutions. Also, type-2 fuzzy sets (T2FSs) are used to consider the uncertainty in experts' opinions. T2FSs are much stronger compared to classical fuzzy sets because they have fuzzy membership degrees and prepare more degrees of freedom to consider uncertainty. Then, the challenges in the steel SC are determined using experts' opinions, and then the importance of these challenges is assessed. Furthermore, solutions to face these challenges are outlined, and then using the CoCoCoSo method, the efficiency of solutions to solve the challenges is evaluated and ranked according to their efficiency. Among the proposed solutions, affirmative leadership for KM adoption in SC and creating reliable teamwork have been identified as the most effective solutions. Also, sensitivity analysis shows that the proposed method is sensitive to the weight of the challenges. Finally, the validity of the proposed method has been confirmed by comparing it with well-known methods in the literature. Additionally, using the different degree index, the superiority of the proposed method over well-known methods in the literature has been determined.