{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,3]],"date-time":"2026-05-03T04:32:26Z","timestamp":1777782746287,"version":"3.51.4"},"reference-count":62,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2025,12,10]],"date-time":"2025-12-10T00:00:00Z","timestamp":1765324800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Systems"],"abstract":"<jats:p>This systematic review explores how business intelligence (BI) and operations research (OR) help organizations ensure sustainable practices in supply chain management (SCM). Drawing on 56 peer-reviewed studies, this review synthesizes how BI tools support sustainability by transforming large and complex datasets into actionable insights, enhancing transparency, improving forecasting, optimizing production and inventory, reducing waste, and enabling circular economy practices. Complementarily, OR provides methodological rigor through optimization models, simulation, and multicriteria decision-making, enabling organizations to balance economic, environmental, and social objectives in supply chain design and operations. The findings reveal that BI and OR jointly contribute to 11 of the 17 United Nations Sustainable Development Goals (SDGs), demonstrating their strategic relevance for global sustainable development. This paper\u2019s contribution is twofold: it consolidates fragmented academic research through an integrative framework clarifying how BI and OR reinforce sustainability within SCM, and it provides practitioners with evidence of how these tools can generate both operational efficiency and a competitive advantage while meeting environmental and social responsibilities. Future research should focus on bridging existing gaps in the literature and advancing the practical applications of these technologies.<\/jats:p>","DOI":"10.3390\/systems13121111","type":"journal-article","created":{"date-parts":[[2025,12,10]],"date-time":"2025-12-10T12:20:57Z","timestamp":1765369257000},"page":"1111","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Integrating Business Intelligence and Operations Research for Sustainable Supply Chain Systems: A Systematic Review"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8449-0526","authenticated-orcid":false,"given":"Rui Pedro","family":"Marques","sequence":"first","affiliation":[{"name":"Research Unit on Governance, Instituto Superior de Contabilidade e Administra\u00e7\u00e3o, Competitiveness and Public Policies (GOVCOPP), University of Aveiro, 3710-193 Aveiro, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0368-2222","authenticated-orcid":false,"given":"Dorabella","family":"Santos","sequence":"additional","affiliation":[{"name":"Research Unit on Governance, Instituto Superior de Contabilidade e Administra\u00e7\u00e3o, Competitiveness and Public Policies (GOVCOPP), University of Aveiro, 3710-193 Aveiro, Portugal"}]}],"member":"1968","published-online":{"date-parts":[[2025,12,10]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1281","DOI":"10.1007\/s10479-022-05047-x","article-title":"Enablers for digital supply chain transformation in the service industry","volume":"348","author":"Alzarooni","year":"2022","journal-title":"Ann. 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