Digital twins and blockchain technology in the industrial Internet of Things (IIoT) using an extended decision support system model: Industry 4.0 barriers perspective

Yi Li, Da An Su and Abbas Mardani

Technological Forecasting and Social Change, 2023, vol. 195, issue C

Abstract: Blockchain technology (BT) can be considered a great help to the concept of digital twins (DT) as it can make sure of transparency, the immutability of data, decentralization of data storage, and peer-to-peer communication in industrial sectors. DT refers to the integrated multiphysics, multiscale, and probabilistic representation of a real-world physical component. The current study attempts to well envisage the ways blockchain can restructure and transform DTs to provide secure manufacturing, assuring compliance, traceability, quality, authenticity, and safety. However, to the adoption of DTs and BT in the industrial Internet of Things (IIoT), there are several barriers. To identify the key barriers, the present study discusses a survey approach through comprehensive literature and interviews with experts. To do so, this study introduced a novel approach using decision-making theory under the q-rung orthopair fuzzy set (q-ROFS) to analyze the identified barriers. This study developed a new methodology called q-ROF-subjective and objective weight integrated approach (SOWIA)-weighted aggregated sum product assessment (WASPAS). An empirical case study to assess the barriers to employing BT into DTs for IIoT implementation in the era of Industry 4.0 is taken. Also, comparative work and sensitivity analysis are discussed to demonstrate the advantage of the presented approach.

Keywords: Blockchain technology; Industry 4.0; Digital twins; Industrial internet of things (IIoT); Fuzzy sets; Decision making; Multi-criteria decision-making (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (3)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:tefoso:v:195:y:2023:i:c:s0040162523004791

DOI: 10.1016/j.techfore.2023.122794

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