Abstract

Food supply chains are inherently complex, dynamically adaptive, and constantly subject to variability. However, the immense importance of a safe, secure, and efficient food supply system has never been more apparent than in recent years, as catastrophic shortages, waste, and rising prices have prompted a surge of interest from policymakers and researchers. To address these challenges, new technologies have the potential to transform food supply chains from fat and frisky to fit and fine-tuned by facilitating decision-making and instilling more automation. Recently, several advances in AI-based solutions have arisen, to the point where some very sophisticated and very powerful solutions capable of very sophisticated strategic, tactical, and operational decision-making processes are becoming available. At the same time, their commercial ecosystem is maturing, and commercialization landscapes supporting everything from verticals to incubators are emerging. Much like how a rising tidal current can float all boats, this flourishing new generation of decision-making AI systems and technologies holds the potential to transform food supply chains.

This paper argues that deployable higher cognitive capability AI systems, or Agentic AI, can be leveraged to embed multiple layer decision-making capabilities along the food supply chain to address its various challenges, particularly at the operational and tactical levels. The ability to integrate the physical and cyber systems with autonomous decision-making capabilities also holds the potential to seamlessly fuse human operator and control tower insights with those of increasingly capable IT systems, enabling meaningful opportunities for operator-awareness, oversight, and collaboration. We outline an operational framework for modeling food supply chain decision-making challenges and then layer Agentic AI capability enhancements on top to demonstrate how the framework can leverage Agentic AI to overcome the challenges faced at the operation-tactical level of food supply chain logistics.

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Keywords

  • automated tests
  • machine learning
  • quality metrics
  • Predictive Test Selection
  • code coverage
  • PR-AUC
  • C_FP
  • C_FN
  • CI/CD
  • build stability.

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