Leveraging Business Intelligence for Sustainable Operations: An Operations Research Perspective in Logistics 4.0

Maria De Lurdes Gomes Neves ()
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Maria De Lurdes Gomes Neves: Instituto Superior de Gestão, Business & Economics School, 1649-026 Lisbon, Portugal

Sustainability, 2025, vol. 17, issue 18, 1-17

Abstract: This study explores the integration of Business Intelligence (BI) and Operations Research (OR) as a driver of sustainability within the evolving framework of Logistics 4.0. As logistics systems face pressures from environmental regulations, digital transformation, and stakeholder expectations, the intersection of data analytics and optimization emerges as a critical lever for sustainable operations. Grounded in a Delphi study conducted in a Portuguese logistics firm, this research captures expert consensus across five dimensions of BI implementation: data infrastructure, real-time decision-making, operational transparency, stakeholder coordination, and sustainability performance monitoring. Methodologically, this study employed two iterative Delphi rounds with 61 cross-functional professionals directly engaged with the organization’s BI systems, particularly Microsoft Power BI. Findings indicate that integrating BI with OR models enhances organizational capacity for proactive scenario planning, carbon footprint reduction, and ESG-aligned decision-making. The results also underscore the importance of cross-departmental collaboration, data governance maturity, and user training in fully leveraging BI for sustainable value creation. By providing both theoretical insights and practical guidance, this study advances the emerging discourse on data-driven sustainability in logistics. It offers actionable insights for logistics managers, sustainability strategists, and policymakers seeking to operationalize digital sustainability and embed intelligence-driven approaches into resilient, low-carbon supply chains.

Keywords: business intelligence; sustainable operations; research Logistics 4.0 (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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