Abstract

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 as follows: (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; and (3) 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, 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.

中文翻译：

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Di-ANFIS：集成的区块链–物联网–支持大数据的框架，用于评估服务供应链绩效

摘要

服务供应链管理由于其无形性，服务的高度多样性，不信任的环境以及不确定的条件，因此是一个复杂的过程。但是，传统的评估模型通常会考虑历史绩效数据，而无法预测和诊断问题的根源。本文提出了一种基于区块链和基于自适应网络的模糊推理系统（ANFIS）技术评估服务供应链绩效的分布式，可信赖，防篡改和学习框架，称为Di-ANFIS。本研究的主要目标如下：（1）提出服务供应链绩效的等级标准，以解决问题的根源。（2）提出了一种智能学习模型，通过神经网络和模糊逻辑相结合来处理不确定性条件；（3）由于ANFIS对大数据的依赖以及供应链中缺乏信任和安全性，因此引入了基于分布式区块链的框架。此外，提出的六层概念框架由数据层，连接层，区块链层，智能层，ANFIS层和应用程序层组成。该架构使用基于区块链平台的物联网，智能合约和ANFIS创建绩效管理系统。Di-ANFIS模型提供了性能评估系统，而无需第三方和可靠的中介机构，可以在智能和学习过程中提供敏捷和诊断模型。它还节省了计算时间并加快了信息流。