Towards Autonomous Supply Chains: Definition, Characteristics, Conceptual Framework, and Autonomy Levels
Not on the map. Why?
Abstract (arXiv)
Recent global disruptions, such as the pandemic and geopolitical conflicts, have profoundly exposed vulnerabilities in traditional supply chains, requiring exploration of more resilient alternatives. Autonomous supply chains (ASCs) have emerged as a potential solution, offering increased visibility, flexibility, and resilience in turbulent trade environments. Despite discussions in industry and academia over several years, ASCs lack well-established theoretical foundations. This paper addresses this research gap by presenting a formal definition of ASC along with its defining characteristics and auxiliary concepts. We propose a layered conceptual framework called the MIISI model. An illustrative case study focusing on the meat supply chain demonstrates an initial ASC implementation based on this conceptual model. Additionally, we introduce a seven-level supply chain autonomy reference model, delineating a trajectory towards achieving a full supply chain autonomy. Recognising that this work represents an initial endeavour, we emphasise the need for continued exploration in this emerging domain. We anticipate that this work will stimulate further research, both theoretical and technical, and contribute to the continual evolution of ASCs.
Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 5 of its 82 references that are in the map. First posted on arXiv 13 Oct 2023.
Builds on
5 of 82 references.
References · 5
- 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
- 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,599
- 2023LLaMA: Open and Efficient Foundation Language Modelscited by 14,132
- 2021Will bots take over the supply chain? Revisiting Agent-based supply chain automationcited by 7
- 2023Implementation of Autonomous Supply Chains for Digital Twinning: a Multi-Agent Approachcited by 2
Built on by
Citing papers per year: 2025: 2; 2026: 1.
3 papers.
- 2025LLM-TabLogic: Preserving Inter-Column Logical Relationships in Synthetic Tabular Data via Prompt-Guided Latent Diffusioncited by 9
- 2025Rethinking Supply Chain Planning: A Generative Paradigmcited by 4
- 2026Agentic AI Autonomy Assessment: A Decision-Support Framework Towards Governed Supply Chain Systemscited by 0
- 2026Agentic AI Autonomy Assessment: A Decision-Support Framework Towards Governed Supply Chain Systems28 Jul 2026
- 2025Rethinking Supply Chain Planning: A Generative Paradigm4 Sep 2025
- 2025LLM-TabLogic: Preserving Inter-Column Logical Relationships in Synthetic Tabular Data via Prompt-Guided Latent Diffusion4 Mar 2025