Purpose

The objective of this research is to put forward a novel closed-loop circular economy (CE) approach to forecast the sustainability of supply chains (SCs). We provide a practical and real-world CE framework to improve and fill the current knowledge gap in evaluating sustainability of SCs. Besides, we aim to propose a real-life managerial forecasting approach to alert the decision-makers on the future unsustainability of SCs.

Design/methodology/approach

It is needed to develop an integrated mathematical model to deal with the complexity of sustainability and CE criteria. To address this necessity, for the first time, network data envelopment analysis (NDEA) is incorporated into the dynamic data envelopment analysis (DEA) and artificial neural network (ANN). In general, methodologically, the paper uses a novel hybrid decision-making approach based on a combination of dynamic and network DEA and ANN models to evaluate sustainability of supply chains using environmental, social, and economic criteria based on real life data and experiences of knowledge-based companies so that the study has a good adaptation with the scope of the journal.

Findings

A practical CE evaluation framework is proposed by incorporating recyclable undesirable outputs into the models and developing a new hybrid “dynamic NDEA” and “ANN” model. Using ANN, the sustainability trend of supply chains for future periods is forecasted, and the benchmarks are proposed. We deal with the undesirable recycling outputs, inputs, desirable outputs and carry-overs simultaneously.

Originality/value

We propose a novel hybrid dynamic NDEA and ANN approach for forecasting the sustainability of SCs. To do so, for the first time, we incorporate a practical CE concept into the NDEA. Applying the hybrid framework provides us a new ranking approach based on the sustainability trend of SCs, so that we can forecast unsustainable supply chains and recommend preventive solutions (benchmarks) to avoid future losses. A practicable case study is given to demonstrate the real-life applications of the proposed method.

中文翻译：

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在循环经济背景下预测供应链的可持续性：动态网络数据包络分析和人工神经网络方法

目的

本研究的目的是提出一种新颖的闭环循环经济 (CE) 方法来预测供应链 (SC) 的可持续性。我们提供了一个实用且真实的 CE 框架，以改进和填补当前在评估 SC 可持续性方面的知识空白。此外，我们旨在提出一种现实生活中的管理预测方法，以提醒决策者注意 SC 未来的不可持续性。

设计/方法/方法

需要开发一个综合数学模型来处理可持续性和 CE 标准的复杂性。为了解决这种必要性，首次将网络数据包络分析（NDEA）并入动态数据包络分析（DEA）和人工神经网络（ANN）。总的来说，在方法论上，本文使用基于动态和网络 DEA 和 ANN 模型组合的新型混合决策方法，使用基于现实生活数据和知识经验的环境、社会和经济标准来评估供应链的可持续性。型公司，使研究与期刊范围有很好的适应。

发现

通过将可回收的不良输出纳入模型并开发新的混合“动态 NDEA”和“ANN”模型，提出了一个实用的 CE 评估框架。使用人工神经网络预测未来时期供应链的可持续性趋势，并提出基准。我们同时处理不需要的回收输出、输入、期望输出和结转。

原创性/价值

我们提出了一种新的混合动态 NDEA 和 ANN 方法来预测 SC 的可持续性。为此，我们首次将实用的 CE 概念纳入 NDEA。应用混合框架为我们提供了一种基于供应链可持续性趋势的新排名方法，以便我们可以预测不可持续的供应链并推荐预防性解决方案（基准）以避免未来的损失。给出了一个可行的案例研究来证明所提出方法的实际应用。