Advancing circular economy assessments for sustainability benchmarking in resource and manufacturing supply chains: A scalable dynamic network framework
Evaluating sustainability and establishing benchmarks in multi-stage resource and manufacturing supply chains (SCs) through circular economy (CE) principles is challenging, especially when transitioning from theoretical to actual appraisals. Real-world interactions of time-varying components and bid...
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| Veröffentlicht in: | Journal of environmental management Jg. 393; S. 127180 |
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| Format: | Journal Article |
| Sprache: | Englisch |
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England
Elsevier Ltd
01.10.2025
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| ISSN: | 0301-4797, 1095-8630, 1095-8630 |
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| Abstract | Evaluating sustainability and establishing benchmarks in multi-stage resource and manufacturing supply chains (SCs) through circular economy (CE) principles is challenging, especially when transitioning from theoretical to actual appraisals. Real-world interactions of time-varying components and bidirectional loops inside SC networks are often overlooked in existing approaches. This study proposes a novel framework to evaluate the sustainability of resource and manufacturing SCs in a CE context using dynamic network data envelopment analysis (DN-DEA). To this end, the research introduces a three-stage DN-DEA framework which integrates intra-stage bidirectional links, dual-role indices, and real-world periodic interactions, such as mid-term green investments and research and development (R&D) costs. By incorporating intra-stage and inter-period circular measures, the framework addresses the dynamic complexities of SCs, enhancing benchmarking reliability and offering actionable insights for decision-making. This method affords average efficiency and sustainability growth trends which allow for sustainability ranking of the SCs for accurate decision making. The dynamic applicability and benchmarking reliability of the framework are illustrated through a three-stage (mining, iron smelting, and distribution) case study. The framework's capacity for scalability allows different levels of resource and manufacturing configurations for both the series and series-parallel periodic approaches.
•Practical benchmarking tool developed for optimizing sustainable supply chains.•Novel dynamic network model with bidirectional restorative CE principles.•Multi-period framework captures sequential and non-sequential SSC interactions.•New ranking method assesses both efficiency levels and growth trajectories.•Applied to a three-stage supply chain case with resource and production flows. |
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| AbstractList | Evaluating sustainability and establishing benchmarks in multi-stage resource and manufacturing supply chains (SCs) through circular economy (CE) principles is challenging, especially when transitioning from theoretical to actual appraisals. Real-world interactions of time-varying components and bidirectional loops inside SC networks are often overlooked in existing approaches. This study proposes a novel framework to evaluate the sustainability of resource and manufacturing SCs in a CE context using dynamic network data envelopment analysis (DN-DEA). To this end, the research introduces a three-stage DN-DEA framework which integrates intra-stage bidirectional links, dual-role indices, and real-world periodic interactions, such as mid-term green investments and research and development (R&D) costs. By incorporating intra-stage and inter-period circular measures, the framework addresses the dynamic complexities of SCs, enhancing benchmarking reliability and offering actionable insights for decision-making. This method affords average efficiency and sustainability growth trends which allow for sustainability ranking of the SCs for accurate decision making. The dynamic applicability and benchmarking reliability of the framework are illustrated through a three-stage (mining, iron smelting, and distribution) case study. The framework's capacity for scalability allows different levels of resource and manufacturing configurations for both the series and series-parallel periodic approaches. Evaluating sustainability and establishing benchmarks in multi-stage resource and manufacturing supply chains (SCs) through circular economy (CE) principles is challenging, especially when transitioning from theoretical to actual appraisals. Real-world interactions of time-varying components and bidirectional loops inside SC networks are often overlooked in existing approaches. This study proposes a novel framework to evaluate the sustainability of resource and manufacturing SCs in a CE context using dynamic network data envelopment analysis (DN-DEA). To this end, the research introduces a three-stage DN-DEA framework which integrates intra-stage bidirectional links, dual-role indices, and real-world periodic interactions, such as mid-term green investments and research and development (R&D) costs. By incorporating intra-stage and inter-period circular measures, the framework addresses the dynamic complexities of SCs, enhancing benchmarking reliability and offering actionable insights for decision-making. This method affords average efficiency and sustainability growth trends which allow for sustainability ranking of the SCs for accurate decision making. The dynamic applicability and benchmarking reliability of the framework are illustrated through a three-stage (mining, iron smelting, and distribution) case study. The framework's capacity for scalability allows different levels of resource and manufacturing configurations for both the series and series-parallel periodic approaches.Evaluating sustainability and establishing benchmarks in multi-stage resource and manufacturing supply chains (SCs) through circular economy (CE) principles is challenging, especially when transitioning from theoretical to actual appraisals. Real-world interactions of time-varying components and bidirectional loops inside SC networks are often overlooked in existing approaches. This study proposes a novel framework to evaluate the sustainability of resource and manufacturing SCs in a CE context using dynamic network data envelopment analysis (DN-DEA). To this end, the research introduces a three-stage DN-DEA framework which integrates intra-stage bidirectional links, dual-role indices, and real-world periodic interactions, such as mid-term green investments and research and development (R&D) costs. By incorporating intra-stage and inter-period circular measures, the framework addresses the dynamic complexities of SCs, enhancing benchmarking reliability and offering actionable insights for decision-making. This method affords average efficiency and sustainability growth trends which allow for sustainability ranking of the SCs for accurate decision making. The dynamic applicability and benchmarking reliability of the framework are illustrated through a three-stage (mining, iron smelting, and distribution) case study. The framework's capacity for scalability allows different levels of resource and manufacturing configurations for both the series and series-parallel periodic approaches. Evaluating sustainability and establishing benchmarks in multi-stage resource and manufacturing supply chains (SCs) through circular economy (CE) principles is challenging, especially when transitioning from theoretical to actual appraisals. Real-world interactions of time-varying components and bidirectional loops inside SC networks are often overlooked in existing approaches. This study proposes a novel framework to evaluate the sustainability of resource and manufacturing SCs in a CE context using dynamic network data envelopment analysis (DN-DEA). To this end, the research introduces a three-stage DN-DEA framework which integrates intra-stage bidirectional links, dual-role indices, and real-world periodic interactions, such as mid-term green investments and research and development (R&D) costs. By incorporating intra-stage and inter-period circular measures, the framework addresses the dynamic complexities of SCs, enhancing benchmarking reliability and offering actionable insights for decision-making. This method affords average efficiency and sustainability growth trends which allow for sustainability ranking of the SCs for accurate decision making. The dynamic applicability and benchmarking reliability of the framework are illustrated through a three-stage (mining, iron smelting, and distribution) case study. The framework's capacity for scalability allows different levels of resource and manufacturing configurations for both the series and series-parallel periodic approaches. •Practical benchmarking tool developed for optimizing sustainable supply chains.•Novel dynamic network model with bidirectional restorative CE principles.•Multi-period framework captures sequential and non-sequential SSC interactions.•New ranking method assesses both efficiency levels and growth trajectories.•Applied to a three-stage supply chain case with resource and production flows. |
| ArticleNumber | 127180 |
| Author | Farzipoor Saen, Reza Wadley, David Shabanpour, Hadi Dargusch, Paul Lieske, Scott N. |
| Author_xml | – sequence: 1 givenname: Hadi orcidid: 0000-0002-3681-1070 surname: Shabanpour fullname: Shabanpour, Hadi email: h.shabanpour@uq.edu.au organization: School of the Environment, The University of Queensland, St Lucia, Queensland 4072, Australia – sequence: 2 givenname: Scott N. orcidid: 0000-0001-6335-6117 surname: Lieske fullname: Lieske, Scott N. email: scott.lieske@uq.edu.au organization: School of the Environment, The University of Queensland, St Lucia, Queensland 4072, Australia – sequence: 3 givenname: Paul orcidid: 0000-0001-9255-2803 surname: Dargusch fullname: Dargusch, Paul email: paul.dargusch@monash.edu organization: Faculty of Business and Economics, Monash University, Caufield East, VIC, 3145, Australia – sequence: 4 givenname: David surname: Wadley fullname: Wadley, David email: d.wadley@uq.edu.au organization: School of the Environment, The University of Queensland, St Lucia, Queensland 4072, Australia – sequence: 5 givenname: Reza orcidid: 0000-0002-0851-6509 surname: Farzipoor Saen fullname: Farzipoor Saen, Reza email: farzipour@yahoo.com organization: Department of Operations Management and Business Statistics, College of Economics and Political Science, Sultan Qaboos University, Muscat, Oman |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/40939360$$D View this record in MEDLINE/PubMed |
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| Keywords | Sustainable decision-making Circular economy Sustainability benchmarking Real-world measures Dynamic network DEA Resource and manufacturing supply chains |
| Language | English |
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