Skip to main navigation menu Skip to main content Skip to site footer

Integrating Agile, Leagile, and Intelligent Supply Chain Practices: A Comprehensive Theoretical and Operational Framework

Abstract

The contemporary global business environment demands highly adaptive, responsive, and efficient supply chains that can withstand volatility, uncertainty, complexity, and ambiguity. This research article provides an extensive, theoretically grounded, and operationally relevant synthesis of agile, leagile, and intelligent supply chain paradigms. Drawing upon extensive literature spanning logistics management, agile manufacturing, digital intelligence, and sustainability, this study examines the integration of these frameworks in contemporary supply chain operations. The study identifies the critical components of agility, including flexibility, responsiveness, and collaboration, and analyzes their interaction with lean principles to form hybrid leagile models. Further, this article explores the transformative role of digital intelligence, including IoT and AI-driven systems, in enhancing decision-making, inventory management, and risk mitigation. Methodologically, this research employs a comprehensive literature synthesis approach, combining theoretical constructs, case analyses, and empirical findings from multiple international contexts. Findings underscore that firms adopting agile-leagile-intelligent supply chain models demonstrate superior performance in responsiveness, customer satisfaction, and operational efficiency while mitigating environmental and systemic risks. The discussion elaborates on operational implications, theoretical contributions, and strategic pathways for managers aiming to achieve end-to-end supply chain resilience. Limitations and future research avenues, particularly in digital intelligence integration and risk-averse air cargo systems, are also articulated. This article contributes a holistic framework that bridges classical supply chain theory with contemporary technological and operational innovations, providing actionable insights for practitioners and scholars alike.

Keywords

Agile supply chain, Leagile integration, Digital intelligence, Inventory management, Logistics optimization, Risk mitigation

PDF

References

  1. Hugo, WMJ, J.A. Badenhorst-Weiss, and E.H.B. Van Biljon. (2004), Supply chain management: logistics in perspective. 3rd edition, Pretoria: Van Schaik.
  2. Klemencic, E. (2006), Management of Supply Chain-Case of Danfoss District Heating Business Area, Faculty of Economics, Ljubljana University, February 2006.
  3. Kisperska-Moron, D and Swierczek, A. (2008), The agile capabilities of Polish companies in the supply chain: An empirical study. Int. J. Production Economics.
  4. Ismail, HS and Sharifi, H. (2006), A balanced approach to building agile supply chains, International Journal of Physical Distribution and Logistics Management, 26:6, pp.431-444.
  5. Lambert, D.L. (2006). Supply Chain Management: Processes, Partnerships, Performance, 2nd edition, The Hartley Press, USA.
  6. Leenders, M.R & Fearon, H.E., (1997), Purchasing and supply chain management Chicago: Irwin, 11th edition.
  7. Lee, W.B., Lau, H.C.W., (1999). Factory on demand: The shaping of an agile network. International Journal of Agile Manufacturing Systems 1:2, pp.83–87.
  8. Chowdhury, W. A. (2025). Agile, IoT, and AI: Revolutionizing Warehouse Tracking and Inventory Management in Supply Chain Operations. Journal of Procurement and Supply Chain Management, 4(1), 41–47. https://doi.org/10.58425/jpscm.v4i1.349
  9. Mason-Jones R & Towill DR (1999) Total cycle time compression and the agile supply chain. International Journal of Production Economics 62, pp. 61–73.
  10. Mason-Jones, R., Naylor, J.B., and Towill, D. (2000), Engineering the Leagile Supply Chain, to be published, Int. Jnl. Agile Manufacturing Systems.
  11. McKinnon A (2001), In: Brewer AM, Button KJ & Hensher DA (eds) Handbook of Logistics and Supply-Chain Management. Pergamon, pp.157–170.
  12. Tøndel, I. A., Cruzes, D. S., Jaatun, M. G., & Sindre, G. (2022). Influencing the security prioritisation of an agile software development project. Computers and Security, 118. https://doi.org/10.1016/j.cose.2022.102744
  13. Tseremoglou, I., Bombelli, A., & Santos, B. F. (2022). A combined forecasting and packing model for air cargo loading: A risk-averse framework. Transportation Research Part E: Logistics and Transportation Review, 158. https://doi.org/10.1016/j.tre.2021.102579
  14. Yang, H., Landes, H., & Chow, J. Y. J. (2023). A large-scale analytical residential parcel delivery model evaluating greenhouse gas emissions, COVID-19 impact, and cargo bikes. International Journal of Transportation Science and Technology. https://doi.org/10.1016/j.ijtst.2023.08.002
  15. Yıldız, B., Savelsbergh, M., & Dogru, A. K. (2023). Transshipment network design for express air cargo operations in China. EURO Journal on Transportation and Logistics, 12. https://doi.org/10.1016/j.ejtl.2023.100120

Downloads

Download data is not yet available.