AI for Smart Supply Chain and Logistics
Jagjit Singh Srai¹
Department of Engineering, University of Cambridge, UK
E-mail: jss46@cam.ac.uk
Status
Artificial Intelligence (AI), a body of knowledge rather than a single technology, has been decades in development. Whilst we are currently at the foothills of AI technology adoption in supply chain and logistics (SC&L), it promises to be the next major ‘Industry 6.0’ transformation with the move to cognitive automation [1]. Currently, rapid advancements are being observed in demand forecasting, supply planning, with new physical and digital infrastructures [2] supporting near real-time logistics optimisation but also new business models [3] that enable autonomous operations and hyper-personalisation. These early applications and pilot developments are within a broader digital supply chain transformation that extends and integrates discrete operations across the ‘end-to-end’ supply chain. Table 1 set out current AI applications within Smart SC&L. These may be classified as ‘point-solutions’ in specific areas of the SC such as factory unit operations, and last-mile logistics [4] but also in enabling ‘infrastructure’ that supports scaling AI across business enterprises, and most exciting perhaps, multiple connected AI and digital applications that support autonomous ‘operating/business models’ [5] involving distributed decision-making through, e.g. Agentic-AI.
Table 1. Current status – Examples of AI Applications in Supply Chain and Logistics
| AI Application area | AI Technology Deployed | Enhanced SC outcome |
|---|---|---|
| Demand Forecasting | Machine learning, time-series | Forecast accuracy |
| Last-Mile Delivery | Route optimisation | Enhanced service/less stockouts |
| Warehouse Automation | Robotics-vision systems-robots | Speed, productivity, pick-accuracy |
| Inventory Management | Predictive Analytics | Reduced Inventory |
| Factory unit operations | Machine learning/ Digital Twins | Process and yield optimisation |
| Supplier Management | AI enabled digital platforms | Sourcing flexibility and reliability |
Current and future challenges
Similar to other technologies deployed in digital SC&L transformation, AI offers huge potential gains, with technology interventions enhancing both productivity and supply chain responsiveness to changing demand. However, there are substantial challenges in the adoption of AI technologies within supply chains, in terms of workforce skills and reluctance to adopt technologies that may impact job security, data quality and data integration challenges, explainability of AI models, potential system biases and governance arrangements for highly distributed systems. Figure 1 summarises AI applications in SCs, emergent challenges and future technology to address the same. In the case of skills, the WEF 2025 Jobs report [6], suggest that one-third of roles by 2030 will involve augmented systems involving human-machine interactions, with a further third fully automated, involving a 50% reduction in manual-only activities from their current levels. This will transform the nature of roles in supply/demand planning with massive reductions in labour/ entry jobs.
Figure 1. AI in SC&L-Applications, Challenges and Future Technology Advances (adapted from Srai et al [5])
Another critical challenge from the adoption of AI in SC and logistics is the issue of attribution of responsibility and accountability as agency is distributed across multiple AI Agents and human actors. This requires multi-actor SC&L collaboration on digital technology adoption [7] and governance mechanisms that limit amplification of bias taking account of interdependence, privacy and system level risks and not just those related to individual/agent decision-making.
Advances in science and technology to meet challenges
For OM [8] and OR [9] SC&L scholars, AI presents many research challenges but also opportunities to shape its future development. As firms progress beyond single function-specific AI investments, the scaling challenge will require major infrastructure development, with OT and IT professionals collaborating in the data integration activity. The primary challenges are how organisations tackle scaling AI applications across enterprises, and the accountability, data management and privacy issues related to distributed and automated decision-making. To tackle the latter, new regulatory frameworks and governance models will be required, a task most likely to be complicated by SC&L spanning multiple jurisdictions. The development of technologies badged as Industry 5 will see additional human-machine interactions for further productivity gains, that also address material and energy-use efficiency, to address scope 3 Net Zero sustainability challenges. Industry 6 technologies [1] that will underpin intelligent self-orchestrating supply ecosystems, include Agentic AI orchestration supported by data integration across IT-OT-Market platforms., with local Edge computing reducing cloud data transfers and consequent cyber-risks.
Concluding remarks
AI technologies despite major data, infrastructural and governance challenges are already driving enhanced SC&L performance from improved demand forecasting, near-real time supply re-routing, factory unit-operations productivity gains, and supplier sourcing flexibility. Future developments will start to connect these discrete systems to operate SC&L autonomously. Such developments will see major shifts in the balance between manual, augmented and automated tasks. Unintended consequences, such as changing power within supply networks, systemic risk exposure, hallucinations from model errors, and potential de- skilling through reliance on ‘black-box’ analysis may lead to trust-deficits and hold back implementation.
Acknowledgements
Funding support is acknowledged from digital supply chain transformation research projects; UKRI Resilience in Agrifood Systems: Supply Chain Configuration Analytics Lab (RASCAL) Ref BB/Z516703/1; Made Smarter Innovation Digital Medicines Manufacturing Research Centre, DM2/Ref EP/V062077/1.
References
[1] Samuels, A., 2025. Examining the integration of artificial intelligence in supply chain management from Industry 4.0 to
6.0: a systematic literature review. Frontiers in artificial intelligence, 7, p.1477044. [2] Joglekar, N., Anderson Jr, E.G., Lee, K., Parker, G., Settanni, E. and Srai, J.S., 2022. Configuration of digital and physical infrastructure platforms: Private and public perspectives. Production and Operations Management, 31(12), pp.4515-
[3] Srai, J. S., G. Parker, N. Joglekar, M. Bärring, J. Boehm, E. Enselme, M. Basso, F. Betti and B. Schönfuß, 2022. ‘Unlocking Business Model Innovation through Advanced Manufacturing’. White Paper, World Economic Forum https://www.weforum.org/whitepapers/unlocking-business-model-innovation-through-advanced-manufacturing [4] Lim, S.F.W., Jin, X. and Srai, J.S., 2018. Consumer-driven e-commerce: A literature review, design framework, and research agenda on last-mile logistics models. International Journal of Physical Distribution & Logistics Management, 48(3), pp.308-332. [5] Joglekar N., Parker, G., and Srai J.S., 2024. ‘Why Manufacturers Need a Phased Approach to Digital Transformation’, MIT Sloan Management Review, Spring 2024. 65 (3), 54-59 https://sloanreview.mit.edu/article/why-manufacturers-need-a- phased-approach-to-digital-transformation/ [6] WEF The Future of Jobs Report 2025 https://www.weforum.org/publications/the-future-of-jobs-report-2025/ [7] Srai, J.S., Balasubramaniam, P., Velastegui, S., Ni, J., Baicheng, L., Lee, J., Sankai, Y., Kim, H-N., Ma, G., 2019 Supply Chain Collaboration through Advanced Manufacturing Technologies, White Paper, World Economic Forum [8] https://www.weforum.org/whitepapers/supply-chain-collaboration-through-advanced-manufacturing-technologies [9] Shalpegin, T., Browning, T.R., Kumar, A., Shang, G., Thatcher, J., Fransoo, J.C., Holweg, M. and Lawson, B., 2025. Generative AI and Empirical Research Methods in Operations Management. Journal of Operations Management. [10] Wiberg, H., Dai, T., Lam, H. and Kulkarni, R., 2025. Synergizing Artificial Intelligence and Operations Research: Perspectives from INFORMS Fellows on the Next Frontier. INFORMS Journal on Data Science