Approaches to digitalization and optimization of logistics - Marcenko, Valentyn (Management and Entrepreneurship: Trends of Development, 2025)
The pace of digital transformation in freight transport is no longer set by hardware cycles or cloud pricing, it is limited by the lenses through which managers view their own processes. This study therefore asks a deceptively simple question: what combination of design choices actually converts advanced logistics technology into measurable performance gains for small- and mid-sized carriers? A purposive meta-synthesis of twelve peer-reviewed empirical investigations published from 2019 to 2025 is combined with a multi-case analysis covering forty United States trucking firms that adopted the OnLogix and Excel Logistics platforms. The sequential mixed-method design first extracts effect sizes for key technologies-digital twins, IoT sensing, blockchain registries, generative-AI routing-then traces how those tools behaved once nested inside real organisations. Quantitative evidence shows an average forty-five per cent improvement in dispatch accuracy and a sixty per cent cut in back-office cost when automation displaced repetitive clerical work rather than merely supporting it. Qualitative pattern-matching, meanwhile, uncovers a layered blueprint that privileges modular roll-outs, zero-touch data capture, and “office-as-a-service” outsourcing, all scaffolded by hybrid technical–consulting support, organisations that engaged all layers reached full payback in under eleven months, twice as fast as partial adopters. By re-framing implementation as an iterative socio-technical journey rather than a one-shot system drop-in, the article advances logistics-4.0 theory and supplies practitioners with diagnostic cues-data maturity, capital latitude, cultural readiness-that can be used to sequence future investments. The roadmap is portable beyond the United States and is already being piloted by early movers in Eastern Europe, suggesting its relevance for global supply-chain resilience. In sum, the research links scattered technological promises to an actionable, evidence-based pathway toward lean, autonomous, and scalable logistics operations. Future work will extend the dataset longitudinally, tracking how learning curves, workforce redeployment, and carbon metrics co-evolve as additional automation layers come online in practice.JEL Classification: R41 O33 L86