Abstract
<title>Abstract</title> <p>Smart factories accumulate large volumes of process data, yet the software governing them remains a set of loosely federated applications: an execution system for order release, a supervisory layer for equipment, isolated condition monitoring services, and offline simulation environments. Decisions on scheduling, maintenance, quality, and energy are therefore taken by different tools, on different time bases, from inconsistent representations of the same asset. This paper proposes an AI native Manufacturing Operating System (M-OS) that acts as the intelligent operating layer of an autonomous factory. The system comprises six layers and four proposed mechanisms. A Digital Twin Kernel maintains a single authoritative asset state, enforces synchronization contracts with bounded staleness, residual, and uncertainty, and allocates twin refresh by utility maximization rather than fixed polling. An AI Decision Engine couples scheduling, predictive maintenance, and quality control through a shared risk field, so that a decision in one domain is priced in the objectives of the others. A Physics Informed Learning Module embeds cutting mechanics, thermal balance, and wear laws as differentiable residuals and exposes a physics consistency score used as admission control on learned models. A Multi Agent Coordination Framework represents every machine, robot, vehicle, inspection station, storage location, and energy zone as an agent that negotiates in a market with a physics feasibility filter, while a Reinforcement Learning Optimization Engine adapts the parameters of that market rather than replacing it. A manufacturing knowledge graph links product features, process plans, machines, tools, defect modes, and corrective actions as a runtime control dependency, and an explainability service produces attribution, counterfactual, and rule level justification for every autonomous action. The coordination problem is formalized in forty five equations, five algorithms are given in pseudocode, and an industrial case study on five axis machining of Ti-6Al-4V aerospace components is specified for implementation with Python, MATLAB, Siemens NX, and ANSYS. Five baselines are defined for comparison: dispatching rules, rolling horizon mathematical programming, digital twin monitoring, a data driven deep reinforcement learning dispatcher, and a classical contract net architecture. No validation is reported; the contribution is architectural, mathematical, and methodological, and the experimental section defines the protocol under which the stated hypotheses will be tested.</p>