Di-ANFIS: an integrated blockchain–IoT–big data-enabled framework for evaluating service supply chain performance
| Authors | |
|---|---|
| Year of publication | 2021 |
| Type | Article in Periodical |
| Magazine / Source | Journal of Computational Design and Engineering |
| MU Faculty or unit | |
| Citation | |
| Web | https://academic.oup.com/jcde/article/8/2/676/6141474?login=true |
| Doi | http://dx.doi.org/10.1093/jcde/qwab007 |
| Keywords | blockchain; industry 4.0; Internet of Things (IoT); big data; service supply chain; performance evaluation |
| Attached files | |
| Description | Service supply chain management is a complex process because of its intangibility, high diversity of services, trustless settings, and uncertain conditions. However, the traditional evaluating models mostly consider the historical performance data and fail to predict and diagnose the problems’ root. This paper proposes a distributed, trustworthy, tamper-proof, and learning framework for evaluating service supply chain performance based on Blockchain and Adaptive Network-based Fuzzy Inference Systems (ANFIS) techniques, named Di-ANFIS. The main objectives of this research are: 1) presenting hierarchical criteria of service supply chain performance to cope with the diagnosis of the problems’ root; 2) proposing a smart learning model to deal with the uncertainty conditions by a combination of neural network and fuzzy logic, 3) and introducing a distributed Blockchain-based framework due to the dependence of ANFIS on big data and the lack of trust and security in the supply chain. Furthermore, the proposed six-layer conceptual framework consists of the data layer, connection layer, Blockchain layer, smart layer, ANFIS layer, and application layer. This architecture creates a performance management system using the Internet of Things (IoT), smart contracts, and ANFIS based on the Blockchain platform. The Di-ANFIS model provides a performance evaluation system without needing a third party and a reliable intermediary that provides an agile and diagnostic model in a smart and learning process. It also saves computing time and speeds up information flow. |
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