Equipment failures are detected late
Late equipment failure detection leads to unplanned downtime.
Industry Pain Points
Process experience is hard to replicate
Production parameters rely on veteran workers' experience for tuning and cannot be replicated at scale; process knowledge is lost as people move.
Quality root-cause tracing is difficult
Quality defects are difficult to trace to root causes and often recur.
Scheduling response is not timely
Production scheduling cannot dynamically respond to real-time changes in orders and materials.
Energy management pressure is rising
Energy consumption and carbon emissions lack fine-grained control, creating rising compliance and cost pressure under dual-carbon goals.
Supply-chain resilience is insufficient
Supply-chain volatility reaches the workshop without enough resilient response capability.
Solution
-
Build a manufacturing business fact base
Build a continuously updated manufacturing business fact base on the enterprise multi-agent R&D management platform and enterprise AI decision hub, integrating equipment status, process parameters, quality inspection, material flow, energy consumption, orders, costs, and other data.
-
Deploy a manufacturing multi-agent system
Use predictive maintenance, quality root-cause analysis, dynamic scheduling, energy and carbon optimization, and supply-chain resilience Agents to identify production anomalies in real time, call operations research, failure prediction, and statistical process control tools, and run root-cause analysis and solution simulations.
-
Connect systems to form a decision loop
Connect MES, SCADA, PLC, AGV, and warehouse systems to form a perception-judgment-execution-feedback decision loop.
-
Built-in AI Retrospective Mechanism
Through execution-feedback-learning-evolution, make manufacturing experience codifiable, reusable, and iterative.
Expected Outcomes
From repair after failure to predictive maintenance
Reduce the risk of unplanned downtime.
From post-event investigation to real-time localization
Attribute quality losses faster.
From experience-based scheduling to optimization plus simulation
Optimize scheduling globally.
From rough energy use to precise control
Continuously reduce energy consumption and carbon emissions.
From knowledge loss when people leave to systems that get smarter with use
Continuously improve flexible production, energy management, and scaled customization capability.