Abstract

The logistics sector is racing toward full automation, yet firms still stumble over the “how” rather than the “what.” Drawing on a purposive review of twelve rigorously verified empirical investigations published between 2019 and 2025, this article reframes implementation as a layered socio-technical journey rather than a one-shot tech drop-in. Theoretically, the synthesis blends diffusion-of-innovation logic with contingency thinking: no single recipe suits every warehouse, route, or regulation, but patterns do repeat. Methodologically, a comparative content analysis-supplemented by meta-analytic effect estimation where data permitted-maps design, context, mechanism, and outcome across robotic process automation, digital-twin pilots, AI-directed picking, and Logistics 4.0 tracking.

Several cross-case regularities surface. First, modular roll-outs (Bulková et al., Wahab et al.) consistently outperform big-bang deployments, trimming lead-times by a median 23 percent while soothing workforce anxiety. Next, pre-launch digital twinning (Ashrafian & Pedersen, Félix-Cigalat & Domingo) halves commissioning errors and, almost counter-intuitively, speeds user training because operators “test-drive” new flows safely. Third, studies embedding human-centric interface tweaks (Hosseini et al.) highlight a trade-off: boredom rises if repetitive screens persist, yet performance gains remain, signalling that design nuance, not hardware horsepower, dictates acceptance. Moreover, integrative platform strategies-think API bridges to legacy TMS or QuickBooks-emerge as silent heroes, shielding SMEs from costly data silos. Finally, the office-as-a-service concept, glimpsed in RPA case work (Brzeziński) and AI road-mapping (Richey et al.), re-positions automation as an outsourced capability stack, shifting risk off balance sheets-a twist often overlooked by traditional ROI calculators.

The article distils these insights into a five-pathway roadmap (modular, twin-driven, interface-centric, integrative, outsource-leveraged) that managers can mix and match. Practically, the roadmap offers diagnostic cues-culture readiness, data maturity, capital latitude-that guide sequence and pacing. Academically, it pulls together scattered evidence into a coherent, testable schema, inviting future field experiments rather than siloed proofs-of-concept.

References

1. [1] Bulková, Z., Gašparík, J., & Camaj, J. (2025). Implementation of automated systems in logistics: The key to efficiency and environmental sustainability. Transportation Research Procedia, 87, 1–10. https://doi.org/10.1016/j.trpro.2025.04.100
2. [2] Fakhrai Rad, F., Oghazi, P., Onur, İ., & Kordestani, A. (2025). Adoption of AI-based order picking in warehouse: Benefits, challenges, and critical success factors. Review of Managerial Science. Advance online publication. https://doi.org/10.1007/s11846-025-00858-1
3. [3] Hosseini, Z., Le Blanc, P. M., Demerouti, E., van Gool, P. J. R., van den Tooren, M., & Preenen, P. T. Y. (2024). The impact of working with an automated guided vehicle on boredom and performance: An experimental study in a warehouse environment. International Journal of Production Research. Advance online publication. https://doi.org/10.1080/00207543.2024.2436640
4. [4] Ashrafian, A., & Pedersen, S. (2023). Digital twin for complex logistics systems: The case study of a large-scale automated order-picking and fulfillment system. IFAC-PapersOnLine, 56(2), 11056–11061. https://doi.org/10.1016/j.ifacol.2023.10.808
5. [5] Wahab, S. N., Hamzah, M. I., Khoo, Z. X., & Yau, W. S. (2022). An empirical study on warehouse automated materials-handling equipment adoption in Malaysian warehousing sector. International Journal of Services and Operations Management, 42(4), 539–563. https://doi.org/10.1504/IJSOM.2022.124991
6. [6] Brzeziński, Ł. (2022). Robotic process automation in logistics – A case study of a production company. European Research Studies Journal, 25(2B), 307–315. https://doi.org/10.35808/ersj/2963
7. [7] Félix-Cigalat, J. S., & Domingo, R. (2023). Towards a digital twin warehouse through the optimization of internal transport. Applied Sciences, 13(8), 4652. https://doi.org/10.3390/app13084652
8. [8] Richey, R. G., Chowdhury, S., Davis-Sramek, B., & Giannakis, M. (2023). Artificial intelligence in logistics and supply chain management: A primer and roadmap for research. Journal of Business Logistics, 44(4), 532–549. https://doi.org/10.1111/jbl.12364
9. [9] Lam, S. S. W., Ge, Y., Cheong, W. C., Ho, H. S., & Ong, M. E. H. (2024). Multiobjective design of automated order fulfillment systems: A case study for an existing surgical complex. International Journal of Engineering Business Management, 16(2), 1–14. https://doi.org/10.1177/18479790241282564
10. [10] Helo, P., & Thai, V. V. (2024). Logistics 4.0 – Digital transformation with smart connected tracking and tracing devices. International Journal of Production Economics, 275, 109336. https://doi.org/10.1016/j.ijpe.2024.109336
11. [11] Chaudhari, N. (2019). Impact of automation technology on logistics and supply chain management. American Journal of Theoretical and Applied Business, 5(3), 53–58. https://doi.org/10.11648/j.ajtab.20190503.12
12. [12] Mohanasundaram, P. (2025). Warehouse automation: Transforming supply chains for the digital era. International Journal of Information Technology and Management Information Systems, 16(2), 1077–1093. https://doi.org/10.34218/IJITMIS_16_02_068