Business Intelligence, Process Mining, and Lean Six Sigma for Sustainable Business Model Innovation: A Comprehensive Review
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.
*Author to whom correspondence should be addressed.
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.
Keywords: Artificial Intelligence, sustainable business models, lean six sigma, process mining, business intelligence, ESG reporting, data-driven innovation, sustainability management, digital transformation