IntelliChain Automotive: Embedding Machine Cognition into Dispersed Supply Logistics Networks within Industry 4.0 Operational Boundaries
DOI:
https://doi.org/10.5281/zenodo.20443383Keywords:
Artificial Intelligence, Predictive Logistics, Supply Chain Management, Distributed Automotive Networks, Industry 4.0, Edge Computing.Abstract
Digital transformation in automotive supply networks—typically characterized by Just-in-Time principles and high-risk vulnerability to shocks—enables predictive logistics: a predictive-prescriptive-optimization-enhanced real-time decision-support paradigm spanning demand forecasting, inventory optimization, transportation planning, and capacity network design. Supply networks supporting Industry 4.0 detect, analyze, and respond rapidly to changes, disruptions, and stresses, in the process minimizing total cost of ownership while improving lead-time and service-level targets predicted with a digital twin. Data ecosystems capture, aggregate, analyze, and disseminate data streams generated by a network’s partners employing standardized discrete-event, agent-based, and machine-learning models, deployed within ultra-responsive edge cloud and fog architectures.
Despite long-standing research interest, relatively few predictive-logistics applications exist, signaling a gap that demands investigation. Evidence presented here addresses an objective defined through literature synthesis: Explore Industry 4.0 drivers and constraints while assessing the impact of emerging Industry 4.0 capabilities on predictive logistics for distributed automotive supply networks. Key findings illustrate the performance benefits of predictive-logistics in two empirical cases describing automotive semiconductor supply chains and electric vehicle battery and component logistics.
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