Modeling the blockchain readiness challenges for product recovery system

Abstract

Product Recovery System (PRS) transfers products from their typical final place to their source to arrest some value on the product. There are obstructions, such as costs, associated with the modification of accounts and assessment of products and refunds associated with the implementation of PRS. Blockchain Technology (BCT) emerged as an innovative approach to constructing trust in a trust less environment and assures the availability, traceability, and security in data management. It also presents a valuable solution to PRS. This study aims to analyze the Blockchain Readiness Challenges (BRCs) to PRS in the context of manufacturing industries. The study observes 20 readiness challenges linked with the implementation of BCT in PRS. The BRCs are identified from the literature survey and confirmed after consequent examinations with industry experts and researchers. The study employed a Multi-Criteria Decision-Making (MCDM) i.e., the Decision-Making Trial And Evaluation Laboratory (Fuzzy DEMATEL) approach to find the cause-and-effect interactions to prioritize BRCs. The Maximum Mean De-Entropy (MMDE) algorithm was adopted to establish the threshold value based on the information entropy of the interactions among the BRCs for PRS. The fuzzy set theory was adopted to tackle the uncertainty and vagueness of personnel biases and data deficiency problems. The findings from this study reveal that inadequate financing for PRS exercises, lack of governance and standards, and security challenges to BCT implementation are the most influential readiness challenges for the adoption of blockchain in PRS. The study is useful to manufacturing organizations for identifying the potential BRCs to implement PRS among all existing readiness challenges so that they can take suitable measures before proceeding to adopt blockchain in PRS. The managers are suggested to eliminate the readiness challenges and widen the blockchain technology adoption in PRS.

Key performance indicator based dynamic decision-making framework for sustainable Industry 4.0 implementation risks evaluation: reference to the Indian manufacturing industries

“…It is confirmed from the SLR that the ARAS perfectly responds to overcome rank reversal issues (Zavadskas & Turskis, 2010 ) and the extension with IVTFNs takes care of unspecific and ambiguous prejudiced experts’ judgment as compared to other MCDM methods (Dahooie et al, 2020 ). Also concluded from the SLR the FDEMATEL shows the best response to insufficient data availability, and an uncertain environment of decision making as well as to evaluate the strength of the criteria assessment as compared to conventional DEMATEL (Dwivedi et al, 2022 ) thus selection of apt MCDM methods for current study proves the robustness of the findings. Further, the integrated model developed in this study is elaborated in the next section.…”

“…The research of Lin et al ( 2019 ) used the Probit model to identify the effect of the I4.0 driving force on the performance of China’s manufacturing industries. Dwivedi et al ( 2022 ) investigated the causal relationship among the blockchain readiness challenges in product recovery systems using FDEMATEL. Braglia et al ( 2022 ) applied DEMATEL to evaluate KPIs for I4.0 and logistic 4.0, but the authors did not cover the full spectrum of KPIs influencing I4.0 advancement.…”

Blockchain technology: a catalyst for reverse logistics of the automobile industry

“…Large volumes of returns are seen due to shorter vehicle lifecycles and liberal return policies, such as warranty and service returns. Precise information of formal return policies in a blockchain's distributed ledger assists reverse logisticians in dismantling and recovering the value of preowned vehicle parts (Dwivedi et al, 2022). The authenticity of information communicated across peers eliminates duplicate versions of a document.…”

“…This caused the whole automobile industry to understand the significance of digitizing their RL activities and the need to improve SASBE 13,1 emerging technologies like blockchain to improve its operational efficiency, transparency, authenticity, traceability and data security (Samadhiya et al, 2022). Furthermore, the exceptional advantages of blockchain technology (BCT), such as security, protection, smart contracts and the ability to combat deception and extortion in transactions, are enticing organizations to coordinate it into their RL operations (Dwivedi et al, 2022). A blockchain is a distributed database that shares recorded information about successful transactions in a public ledger (Li et al, 2020).…”

“…, 2022). Furthermore, the exceptional advantages of blockchain technology (BCT), such as security, protection, smart contracts and the ability to combat deception and extortion in transactions, are enticing organizations to coordinate it into their RL operations (Dwivedi et al. , 2022).…”