In the machinery assembly industry, the basic functions of machinery assembly MES systems and equipment assembly MES systems are the foundation for achieving transparency, standardization, and traceability in the production process. The introduction of AI, however, builds upon these basic functions, endowing the AI-powered intelligent MES system with intelligent capabilities of “perception—analysis—decision-optimization,” thereby achieving a transition from “digitalization” to “intelligentization.”
Basic Functions of MES in the Machinery Assembly Industry
1. Work Order Management and Production Plan Execution: Receives production orders from ERP and breaks them down into workshop-level work orders; manages work order status (pending scheduling, in progress, completed, abnormal pause, etc.); supports binding non-standard/customized work orders’ BOMs with process routes.
2. Process Flow and Work Instruction Management (eSOP): Converts paper or PDF process documents into structured electronic work instructions; automatically pushes assembly steps, drawings, torque parameters, and bills of materials to workstation terminals according to the process; supports version control to prevent the use of outdated processes.
3. Material Kitting and Error Prevention Management: Verifies material kitting based on BOM; mandates scanning to verify material batch/serial number before process start; prevents incorrect, missing, or mixed materials.
4. Production Process Tracking and Reporting: Records the operator, start/end time, equipment, and parameters for each process; supports manual/automatic reporting (scanning, PLC triggering, etc.); updates work order progress in real time and displays it on a visual dashboard.
5. Quality Inspection and Process Control (SPC): Sets critical quality gates; products that fail to pass cannot proceed; supports first inspection, patrol inspection, and final inspection processes; records information on non-conforming products, triggering rework or scrap processes. 6. Full Lifecycle Traceability: Generates a unique ID (e.g., QR code/RFID) for each piece of equipment; links purchased parts batches, self-made parts serial numbers, assembly records, test data, and quality inspection reports; supports forward (from raw materials to finished products) and reverse (from defective products to raw materials) traceability.
7. Equipment and Tool Management: Monitors the operating status of key equipment (tightening guns, press machines, test benches); manages tool calibration cycles and usage counts; collects equipment OEE (Overall Equipment Effectiveness) data.
8. Anomaly Management and Andon Alarms: Workers can report anomalies (material shortages, equipment malfunctions, quality issues) with one click; the system automatically notifies the responsible person and records the processing process and duration.
How AI is intelligently applied to the basic functions of MES
1. In terms of work order management and production plan execution, traditional MES is mainly responsible for receiving ERP instructions and tracking work order status. After introducing AI, the system can comprehensively consider multi-dimensional dynamic factors such as order delivery date, real-time equipment load, personnel skill matching degree, material arrival prediction, and historical failure rate to build an intelligent scheduling model. When sudden order insertion, equipment downtime, or material delays occur, AI can generate multiple feasible rescheduling solutions within seconds and recommend the optimal solution, greatly reducing manual intervention and significantly improving planning flexibility and on-time delivery rate.
2. In terms of process flow and electronic work instruction (eSOP) management, MES originally only provided static step guidance.
AI, by mining massive amounts of historical assembly data, identifies key process parameter combinations affecting quality and efficiency, such as optimal tightening sequence, pressing speed, or glue quantity control strategies, and dynamically embeds these “best practices” into work instructions. 3. In the material kitting and error prevention management stage, traditional MES relies on BOM comparison for post-event verification. AI, however, can combine supply chain data, logistics information, and inventory dynamics to predict material availability risks in the next few hours or days. Once a potential delay in the arrival of a critical component is identified, the system automatically freezes the initiation of related work orders, preventing production line interruptions due to material shortages and truly achieving “preventative” material management. 4. In production process tracking and reporting, AI further expands the value of data. Besides recording “who did what and when,” it can also combine workstation cameras and computer vision technology to identify in real time whether operators are performing key actions according to standard procedures, achieving automatic supervision of behavioral compliance and reducing the risk of human error. 5. In terms of quality inspection and process control, by deploying industrial cameras and deep learning models, the system can automatically detect defects such as missing parts, incorrect parts, misalignments, and scratches during assembly, with an accuracy of 0.1 mm, far exceeding the capabilities of the human eye. Simultaneously, AI can analyze process parameters collected by the equipment in real time (such as torque curves and voltage waveforms). Once abnormal trends are detected, it immediately issues warnings about potential quality problems, achieving “quality is built in the process” rather than relying on final inspection for rejection. 6. In equipment and tool management, AI drives the maintenance model from “periodic maintenance” or “fault-based repair” to “predictive maintenance.” By continuously monitoring signals such as vibration, current, and temperature of key equipment, the AI model can predict potential faults such as bearing wear and motor aging, issuing warnings 72 hours in advance, automatically generating maintenance work orders, and scheduling them for processing during non-production periods, effectively reducing unplanned downtime and improving OEE (Overall Equipment Effectiveness). 7. In the anomaly management and Andon alarm mechanism, AI can intelligently classify reported anomalies (such as material shortages, equipment failures, process deviations, etc.) and automatically set processing priorities based on their impact on the production flow. Typical Scenario Example Scenario: Assembly of a large pump and valve assembly – Traditional MES: Records who installed which part when and whether the test passed. – AI+MES: Before assembly, AI determines whether the materials are complete and the versions match; during assembly, AI visually confirms that the sealing rings are correctly installed; during tightening, AI compares whether the torque curve meets the “health mode”; if leakage exceeds the standard during the testing phase, AI immediately associates the sealing ring supplier and assembler of that batch and suggests isolating products from the same batch; if the gantry crane has recently experienced abnormal vibration, AI arranges weekend maintenance in advance to avoid interruption of the final assembly line. Implementation Recommendations 1. First, solidify the MES foundation: Ensure data collection (scanning/PLC), process standardization, and BOM accuracy. 2. Focus on high-value pain points: SMEs can prioritize deploying AI-powered visual quality inspection or predictive maintenance for critical equipment.
3. Choose a scalable platform: MES systems that support flexible integration of AI modules, such as the Wanjiexingkong low-code platform.
4. Continuous iteration: AI models need continuous training and optimization as production data accumulates.
