Algorithmic Coordination in Supply Chains – Large Language Models (LLMS) As an Integrator of Classic Coordination Mechanisms

Abstract Book of the 11th International Conference on Management, Economics and Finance

Year: 2026

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Algorithmic Coordination in Supply Chains – Large Language Models (LLMS) As an Integrator of Classic Coordination Mechanisms

Dr. Mariusz Kmiecik

ABSTRACT:

This paper asks how large language models (LLMs) are reshaping inter-organizational coordination in supply chains – not as a separate, novel mode of coordination, but as a connective layer that ties together four classic mechanisms: market, hierarchy, social, and logistical. The argument is conceptual and synthetic. It starts by reconstructing the established typology of coordination mechanisms within the network governance tradition, and then shows that an LLM works across all of them at once: it sharpens logistical flows such as forecasting and transport planning, builds the transparency on which trust depends, and supports the formal monitoring of contracts and performance indicators. The reasoning draws, among other things, on the author’s earlier empirical work – studies of triadic structures involving 3PL operators, where an LLM integrated operational, forecasting, and environmental data, and a survey on how logistics firms perceive generative artificial intelligence and what holds back its adoption. The paper positions itself against two strands of research: work on algorithmic management, which has so far been confined to coordinating labour inside the firm, and work on LLMs in supply chains, which has concentrated on optimization and autonomous consensus-seeking. The gap it identifies is the integrative, governance-grounded role of LLMs across organizational boundaries. Its contribution is to move the LLM from an optimization tool toward a mechanism that binds existing coordination modes, and to set out the perceptual and organizational conditions on which its adoption in logistics practice depends.

Keywords: Supply Chain; Generative Artificial Intelligence; Large Language Model; Inter-Organizational Collaboration; Network Governance; Third-Party Logistics