Digital transformation of logistics business processes in the agro-industrial complex: a strategic approach to sustainable development

Abstract

This article proposes a structured theoretical and analytical approach to assessing digital transformation (DT) as a strategic program for implementing digital changes focused on BPM for business processes in the field of logistics. DT is viewed as the synergy of necessary sets of digital tools. It is proposed to divide the transformation into three management levels and apply two practical implementation tools: a set of concise indicators and a phased roadmap. The findings are threefold. First, the proposed three-layer evaluation model clarifies managerial responsibilities by distinguishing strategic governance (alignment with ISO 9001/22000/14001, UN SDGs, and explicit data-governance and cybersecurity policies), tactical capabilities (deployment of WMS/TMS, IoT telemetry, AI-based forecasting for planning and routing, blockchain mechanisms for trust, and Big-Data analytics for decision support), and operational execution (PDCA-driven routines, role clarity, and partner onboarding through open APIs). Second, the indicator suite operationalizes the model across five dimensions - readiness and governance, process performance, transparency and trust, sustainability, and inclusion and capability – capturing, among others, connectivity and interoperability, MAPE, OTIF, order-to-delivery lead time, inventory turns, traceability coverage, data-integrity incidents, waste reduction, SME participation, training intensity, and digital literacy. Third, the roadmap sequences investments from foundations through core digitization and transparency at scale to full sustainability integration, thereby avoiding fragmented, technology-only rollouts. Overall, the framework translates generic managerial levers into actionable capabilities and KPIs that raise reliability, reduce losses and costs, strengthen transparency, and embed sustainability goals. It is sector-agnostic in illustration yet directly applicable to complex, multi-actor logistics networks, and it sets up empirical validation through case studies and surveys.