Business Intelligence, Process Mining, and Lean Six Sigma for Sustainable Business Model Innovation: A Comprehensive Review

Ademola Hope Adeoye, Oluwakemi Fehintola Dosunmu, Hannah Motunrayo Shobajo, Oluwatoyin Olawale Akadiri, Erinmi Isaac Adejoro and Shadiat Alimotu Oyewole
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Ademola Hope Adeoye: Department of Civil Engineering, Faculty of Engineering, Federal University Otuoke, Nigeria.
Oluwakemi Fehintola Dosunmu: Department of Social Work, faculty of Sociology Studies, Lagos State University, Nigeria.
Hannah Motunrayo Shobajo: Department of Zoology, University of Lagos, Nigeria.
Oluwatoyin Olawale Akadiri: Department of Information Sciences, School of Information Sciences and Engineering, Bay Atlantic University, USA.
Erinmi Isaac Adejoro: Department of Systems Engineering, Faculty of Engineering, University of Lagos, Nigeria.
Shadiat Alimotu Oyewole: Department of Industrial Engineering & Data Science, College of Engineering, Florida Agricultural and Mechanical University (FAMU), USA.

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Abstract: This comprehensive review synthesizes the expanding body of scholarship on how business intelligence (BI), process mining, and Lean Six Sigma (LSS) collectively enable sustainable business model innovation in modern organizations. Drawing from multidisciplinary literature across operations management, information systems, sustainability science, and industrial engineering, the study examines how BI provides the data architecture and analytical foundation for real-time visibility, how process mining operationalizes event-log–driven transparency for continuous process improvement, and how LSS offers structured methodologies for reducing waste and optimizing value streams. Using a thematic synthesis approach, the review identifies the integration mechanisms through which these three capabilities support environmental, social, and governance (ESG) objectives, accelerate digital transformation, and strengthen decision-making for sustainable value creation. Findings demonstrate that BI-driven analytics enhance sustainability reporting and performance measurement; process mining uncovers inefficiencies and compliance deviations critical to ESG outcomes; and LSS embeds disciplined, data-driven improvement cycles into organizational routines. The review concludes by outlining a conceptual integration framework, highlighting implementation challenges such as data quality, skills gaps, and technological fragmentation, and proposing a research agenda focused on unified BI–process mining–LSS architectures for next-generation sustainable business models.

Date: 2025-12-22
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Published in Journal of Global Economics, Management and Business Research, 2025, 17 (3), pp.515-532

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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-05430026

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