Data analytics and last-mile delivery in urban areas: challenges and opportunities

Purpose This research explores how data analytics can address last-mile delivery (LMD) challenges in urban areas. LMD is the most expensive, least efficient, and most environmentally damaging supply chain phase, yet analytics adoption remains uneven. Design/methodology/approach A scoping review of 36 studies (2018-2025) across five continents was integrated with eight semi-structured interviews with logistics managers, policymakers, SME representatives, and technology providers from the UK, Brazil, Australia, India, Northern Ireland, and Dubai, offering practitioner perspectives across Europe and four other continents. Findings Analytics yields incremental not transformative gains, reducing delivery times and enabling emissions reporting and predictive demand intelligence. However, adoption is hindered by fragmented governance, infrastructure deficits, high costs, and cultural resistance. Critically, analytics delivers only marginal improvements unless governance and organisational conditions change in parallel. Theoretical implications The study provides preliminary support for socio-technical perspectives on logistics innovation, demonstrating that LMD challenges extend beyond operational optimisation to encompass governance, cultural, and organisational dimensions. It highlights the relevance of Technology-Organisation-Environment (TOE) frameworks by showing how regulatory fragmentation and data-sharing reluctance impede adoption. The findings are consistent with socio-technical perspectives on logistics innovation, highlighting that last-mile delivery challenges extend beyond operational optimisation to encompass governance, organisational, and cultural dimensions. Likewise, the Technology–Organisation–Environment (TOE) framework provides a useful interpretive lens for understanding how technological, organisational, and environmental factors influence data analytics adoption in urban last-mile delivery. Originality/value Bridging optimisation-focused quantitative research and implementation realities, this study integrates practitioner perspectives with global evidence synthesis. The key takeaway is that data analytics alone cannot transform urban LMD; systemic changes in governance, infrastructure, and organisational culture are the essential enablers without which even sophisticated analytics will deliver only incremental gains.