Developing an AI-Augmented Warehouse Resilience Framework for Real-Time Disruption Prediction and Adaptive Supply Chain Continuity

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@article{1722178,
      author = {Amruth Jutty Venkatesh},
      title = {Developing an AI-Augmented Warehouse Resilience Framework for Real-Time Disruption Prediction and Adaptive Supply Chain Continuity},
      journal = {Iconic Research And Engineering Journals},
      year = {2026},
      volume = {10},
      number = {2},
      pages = {3623-3643},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1722178.pdf},
      abstract = {Warehouse disruptions have been growing in number owing to various reasons such as globalization, political tensions, cyber-attacks, weather disturbances, worker shortages, and sudden changes in consumer preferences. These factors illustrate weaknesses in the traditional approach to warehouse management which involves a reactive approach to operations and legacy planning techniques. While progress in artificial intelligence (AI), the Internet of Things (IoT), digital twin modeling and predictive analytics has revolutionized warehouse automation, resilience strategies have tended to consider such technologies as separate entities instead of a part of an integral decision-making system. This research aims at bridging this gap by introducing a framework of AI-Augmented Warehouse Resilience (AI-AWRF).

This research seeks to create a theoretical framework through analysis of scientific literature on the topic of warehouse resilience, AI-enhanced supply chain management, predictive analytics, digital transformation and the application of Industry 4.0 technologies. Unlike traditional warehouse resilience models that emphasize post-disruption recovery, the proposed framework prioritizes proactive disruption anticipation, intelligent operational adaptation, and continuous learning.

According to the research findings, the use of AI in conjunction with real-time operational visibility would help organizations to be able to recognize any possible disruptions, before they affect their warehouse operations. This would allow organizations to allocate their inventory efficiently, coordinate better with their suppliers, utilize their resources effectively and recover from disruptions quickly. In addition, the proposed framework is able to demonstrate how through predictive intelligence and adaptive decision-making, warehouses can be turned into smart operational hubs, which will be able to maintain continuity in the supply chain despite all uncertainties in the business environment.

The research makes an important contribution to the existing literature on AI-driven supply chain resilience by providing a theoretical framework, which combines technology innovation with operational management. It also provides some implementation guidelines, new technology trends and further research directions. With the ongoing digital transformations of many organizations, the developed AI-Augmented Warehouse Resilience Framework can be used to establish resilient, adaptive and intelligent warehouse operations which will support sustainable global supply chains.},
      keywords = {artificial intelligence, warehouse resilience, supply chain continuity, predictive analytics, digital twins, internet of things, industry 4.0, adaptive decision-making.},
      month = {August},
      doi = {https://doi.org/10.64388/IREV10I2-1722178}
  }