first_pagesettingsOrder Article Reprints Open AccessArticle Current Trends and Forecasts of Sustainable Supply Chains: Large-Scale Text Mining and Forecasting by Nikolay Dragomirov 1,*ORCID,Myriam Caratù 2 andLilyana Mihova 1ORCID 1 Department of Logistics and Supply Chains, University of National and World Economy—UNWE, 1700 Sofia, Bulgaria 2 Department of Economics and Business, University of Catania, 95129 Catania, Italy * Author to whom correspondence should be addressed. Sustainability 2026, 18(8), 3842; https://doi.org/10.3390/su18083842 (registering DOI) Submission received: 11 February 2026 / Revised: 3 April 2026 / Accepted: 8 April 2026 / Published: 13 April 2026 Downloadkeyboard_arrow_down Browse Figures Versions Notes Abstract This study rounds into both the historical context and future projections of sustainable supply chain research practices. It emphasizes the necessity for the advanced analyses of research articles by combining traditional analysis with modern topic modeling and forecasting techniques. This study is organized around four primary research questions. A dataset of n = 8955 indexed article keywords and abstracts for the period of 2000–2025 was analyzed in the Python (version 3.12.) environment using n-grams, top keywords by year, k-means clustering combined with dimensionality reduction, and co-occurrence networks. Time-series forecasting models were also used to project the short-term development of clusters. The dataset retrieval was performed with search string and subject-area filters to focus the analysis on managerial and economic perspectives of sustainable supply chains. The analysis identified four keyword clusters: (1) CSR and Stakeholder Engagement, (2) Circular Economy and Sustainable Production, (3) Decision-making, Resilience and Emerging Technologies, and (4) Green Supply Chain Management. These clusters were then examined to assess current research practices from a managerial and economics perspective and their near-term evolution, with results validated through the additional clustering of abstract-level topics. This study confirms a paradigm change toward the integration of circularity, digitalization, and resilience, with technology-enabled growth. Social sustainability remains underrepresented, revealing a critical gap in current research. This study contributes methodologically by updating and extending current research practices and theoretically by revealing sustainability problems trends in supply chains.

Current Trends and Forecasts of Sustainable Supply Chains: Large-Scale Text Mining and Forecasting

Myriam Caratu';
2026-01-01

Abstract

first_pagesettingsOrder Article Reprints Open AccessArticle Current Trends and Forecasts of Sustainable Supply Chains: Large-Scale Text Mining and Forecasting by Nikolay Dragomirov 1,*ORCID,Myriam Caratù 2 andLilyana Mihova 1ORCID 1 Department of Logistics and Supply Chains, University of National and World Economy—UNWE, 1700 Sofia, Bulgaria 2 Department of Economics and Business, University of Catania, 95129 Catania, Italy * Author to whom correspondence should be addressed. Sustainability 2026, 18(8), 3842; https://doi.org/10.3390/su18083842 (registering DOI) Submission received: 11 February 2026 / Revised: 3 April 2026 / Accepted: 8 April 2026 / Published: 13 April 2026 Downloadkeyboard_arrow_down Browse Figures Versions Notes Abstract This study rounds into both the historical context and future projections of sustainable supply chain research practices. It emphasizes the necessity for the advanced analyses of research articles by combining traditional analysis with modern topic modeling and forecasting techniques. This study is organized around four primary research questions. A dataset of n = 8955 indexed article keywords and abstracts for the period of 2000–2025 was analyzed in the Python (version 3.12.) environment using n-grams, top keywords by year, k-means clustering combined with dimensionality reduction, and co-occurrence networks. Time-series forecasting models were also used to project the short-term development of clusters. The dataset retrieval was performed with search string and subject-area filters to focus the analysis on managerial and economic perspectives of sustainable supply chains. The analysis identified four keyword clusters: (1) CSR and Stakeholder Engagement, (2) Circular Economy and Sustainable Production, (3) Decision-making, Resilience and Emerging Technologies, and (4) Green Supply Chain Management. These clusters were then examined to assess current research practices from a managerial and economics perspective and their near-term evolution, with results validated through the additional clustering of abstract-level topics. This study confirms a paradigm change toward the integration of circularity, digitalization, and resilience, with technology-enabled growth. Social sustainability remains underrepresented, revealing a critical gap in current research. This study contributes methodologically by updating and extending current research practices and theoretically by revealing sustainability problems trends in supply chains.
2026
sustainable supply chains, text mining, trend forecasting, automation in data analysis,
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.11769/710209
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