From equipment profiling to predictive maintenance, from spare parts management to knowledge accumulation — Leansight EAM is not just a CMMS, but an equipment intelligence engine embedded in the Smart Operations Control Tower
From precision equipment parameter control to knowledge transfer gaps, every pain point is eroding corporate profits
Multi-variety, multi-customer orders require frequent switching of packaging schemes and test programs (Recipe) on the same equipment. Manual switching is prone to parameter errors, causing batch product scrap. Minor fluctuations in precision equipment directly impact yield.
Hidden Yield LossTraditional time-based maintenance (TBM) applies a one-size-fits-all approach, unable to dynamically adjust based on actual equipment load and process variety. Over-maintenance wastes labor hours, while insufficient maintenance triggers unexpected failures. Unplanned downtime losses are high.
Frequent Unplanned DowntimePrecision equipment spare parts are expensive with long import lead times (bonding heads, ceramic nozzles, probe cards, spindles). Spare parts inventory balancing is difficult: hoarding ties up large amounts of capital, while parts shortages cause prolonged downtime. Spare parts usage cannot be precisely traced.
Capital Occupation vs. Downtime RiskEquipment fault experience is scattered among individual technicians, lacking a standardized fault knowledge base. Personnel turnover causes loss of maintenance experience. High-end imported equipment heavily depends on original manufacturer technical support, with domestic engineers lacking underlying debugging permissions.
Experience Loss · Training DependencyA wide variety of heterogeneous equipment presents challenges for unified runtime monitoring and execution. Traditional account-password usage poses security risks (leakage). Equipment operation permission control is difficult, and operators cannot be traced.
Permission Chaos · Security RisksCustomer audit requirements demand full-process equipment history traceability: maintenance records, calibration records, spare parts replacement records, and equipment parameter records must be archived long-term. Paper-based ledgers are prone to missing entries and loss. Equipment lifecycle data is scattered across various systems.
Audit Risk · Data FragmentationAll modules share interconnected data, from equipment profiling to efficiency analysis, enabling intelligent management of the full equipment lifecycle
Records equipment ID, type, department, owner, and other key information. Easily view any equipment-related information, including code, name, location, model, criticality, status, manufacturer, supplier, as well as equipment parameters, historical work orders, etc.
✓ 30% system, 70% management, 120% data — digitize paper data, connect scattered data
Real-time inventory query, full-process management of inbound/outbound/return/inventory, low-stock alerting. Collects key spare parts usage data through equipment connectivity to provide advance reminders for spare parts replacement. Precise traceability of spare parts usage provides data support for cost control.
✓ Reduce manufacturing repair costs, scientific spare parts budgeting, minimize capital occupation
Smart planning: custom inspection cycles, routes, and personnel, with automatic work order dispatch. Mobile execution: scan-code inspection, photo evidence, real-time anomaly reporting. Miss-prevention control: message reminders, electronic records, labor hour comparison analysis. Closed-loop optimization: data-driven maintenance adjustment.
✓ Reduce 30% inspection time, 100% execution rate, eliminate fake inspections
Generate rules for plan creation to reduce setup effort. Maintenance plans can be assigned to specific personnel and dispatched on demand. Timely alert reminders ensure efficient plan execution. Real-time statistical report generation provides reference for subsequent plan development. Equipment maintenance calendar visualization.
✓ Extend equipment life, reduce sudden failure rate
Multi-channel repair requests: scan code, manual entry trigger. Problem reporting: photos, videos to assist fault description. System generates work orders pushed to maintenance personnel, who confirm completion and upload repair results via APP. Reference similar fault solutions. Repair records auto-archived.
✓ Shorten 30% fault handling time, reduce coordination labor costs
Standard fault library, common fault classification and handling methods. Classic case reference guidance. Provides simple repair guidance for simple problems, reducing waiting waste. Full preparation before going to the site, understanding the cause and effect of the problem in advance. Root cause analysis can be selected when closing a problem.
✓ Reduce dependency on manual training, rapid production recovery
The EAM reporting function helps enterprises analyze data recorded in work orders, monitor all maintenance work occurring within the enterprise, and measure key maintenance indicators (MTBF mean time between failures, MTTR mean time to repair). Reasonable fault analysis through chart reports validates ideas and findings. Multiple types of reports have been accumulated in past projects for customer industrial project teams to choose from.
✓ Significant reduction in data analysis report preparation time, decisions shift from experience-driven to data-driven
Not just moving paper ledgers online — Leansight EAM is an intelligent equipment engine embedded in the Smart Operations Control Tower
| Dimension | Traditional EAM / CMMS | Leansight EAM |
|---|---|---|
| Maintenance Strategy | Time-driven (one-size-fits-all TBM): periodic maintenance, over or under | Condition-driven + AI prediction: based on equipment OEE trend degradation model, early warning, precision maintenance |
| Problem Discovery | Passive response: manual repair request after failure occurs, losses already incurred | CEO Headline proactive discovery: AI scans equipment OEE trends 24/7, auto-alerts and opens cases for anomalous degradation |
| Root Cause Analysis | Manual experience judgment: technicians recall and investigate, accuracy <40%, experience hard to transfer | AI six-step root cause analysis: multi-source aggregation → anomaly extraction → time-series alignment → cross-domain correlation → knowledge graph → RootCause |
| Data Integration | Information silos: EAM/MES/ERP/OEE each independent, data disconnected | LeanFusion data fusion: unified heterogeneous system access, ODS-DWS-ADS layered governance, real-time data interconnection |
| Visualization | Static reports: preset reports, no drill-down, no real-time, no mobile | LeanBI industrial visualization: 50+ industrial components, 3D digital twin, mobile auto-adaptation, real-time drill-down analysis |
| Extensibility | Custom development: requirement changes need scheduled development, long cycle, high cost | LeanCodee low-code: model-driven, drag-and-drop configuration, business users can extend independently |
| Equipment Security | Account/password: high security risk, leakage risk, operations cannot be traced | Card swipe + RPA Agent: card reader permission control, auto screen lock, full operation log traceability |
| Knowledge Management | Paper/Excel: fault experience scattered, personnel loss = experience loss | Standardized knowledge base: fault cases auto-archived, AI similarity matching, new hires can quickly get up to speed |
| Management Penetration | Single-layer view: only equipment level, no production line/workshop/factory level | OEE full-link penetration: Group → Factory → Workshop → Production Line → Shift → Equipment, six-level one-click drill-down |
| Platform Positioning | Standalone system: yet another information silo | Control Tower L2 layer: embedded in the four-layer Smart Operations Control Tower, data surges up and decisions flow down |
Not just another standalone system, but a core component of the Lean Collaboration Layer in the Control Tower's four-layer architecture
AI scans factory-wide equipment OEE trend degradation, proactively discovering hidden deterioration. When equipment OEE declines for 3 consecutive days but has not yet triggered an alert, CEO Headline provides early warning, pushing an "Equipment Performance Degradation Trend" headline to the Equipment Director
Factory-wide equipment status at a glance: online rate/failure rate/OEE/MTBF/MTTR real-time dashboard. Equipment OEE loss waterfall chart (theoretical capacity → planned downtime → faults → changeover → speed → quality → actual OEE). Managers can drill down to any equipment's maintenance history with one click
Asset registry, spare parts, inspection & patrol, preventive maintenance, fault repair, knowledge base, report analysis. Data interconnects between modules, work order flow forms a closed loop. Integrated with MES/ERP/OEE systems, equipment maintenance and production scheduling are linked
LeanFusion fuses EAM/MES/ERP/IoT heterogeneous data, ODS-DWS-ADS layered governance. LeanCodee model-driven low-code, EAM functional modules can be continuously extended. LeanBI industrial visualization engine, 50+ industrial components + 3D digital twin
Not waiting for equipment to fail before fixing, but AI telling you "which equipment needs attention" before failure occurs
Trend Scanning: AI scans factory-wide equipment OEE trends, MTBF decline trends, and spare parts consumption anomaly acceleration 24/7
Problem Pre-assessment: Identifies "hidden deterioration" — equipment OEE continuously declining but not yet triggering alerts, abnormal spare parts consumption growth suggesting equipment degradation
Auto Case Opening: Generates equipment maintenance headline, pushes to Equipment Director, with OEE trend charts, similar historical fault cases, and root cause analysis recommendations
Headline Push: Headline pushed to executive meetings, Equipment Director sees not just "which equipment broke" but "which equipment is about to have problems"
Hidden Deterioration Discovery: Line 3 pick-and-place machine OEE drops from 85% to 78% over 5 consecutive days, but no fault alert triggered. CEO Headline AI discovers this trend anomaly
Cross-domain Correlation: AI correlates and finds AOI detection offset defect rate on the same line rising simultaneously, spare parts consumption (nozzles) abnormally increasing → suspected nozzle wear
Headline Generation: "Line 3 pick-and-place machine nozzle suspected wear, recommend preventive replacement to avoid continued yield decline" → pushed to Equipment Director + Production Manager
EAM Closed Loop: Equipment Director creates preventive maintenance work order in EAM → replace nozzle → OEE recovers → headline effectiveness verified → knowledge base archived
Traditional EAM tells you "equipment broke" → Leansight EAM tells you "which equipment is about to have problems" → CEO Headline tells you "why it's about to have problems and how to handle it"
Equipment maintenance actions directly drive OEE changes, OEE anomalies automatically trigger EAM maintenance work orders
Every aspect of maintenance actions maps to OEE losses
OEE anomalies automatically trigger EAM maintenance processes
In the six-level OEE penetration system (Group → Factory → Workshop → Production Line → Shift → Equipment), equipment-level OEE is the direct input to EAM — MTBF/MTTR, predictive maintenance, and spare parts linkage are all driven by equipment-level OEE data
Not built from scratch, but assembled Lego-style based on Leansight's three major product platforms
Lightweight data fusion platform that integrates EAM/MES/ERP/IoT heterogeneous data into a data warehouse with layered governance through data warehouse modeling, data integration, and data development, providing data services for enterprise data governance and application
Industrial digitalization low-code platform with rich industry application templates and models. Structure modeling (equipment/BOM/workstation) + behavior modeling (maintenance rules/alert rules/scheduling rules), supporting complex business scenarios, continuously accumulating Know-How
Fully configurable, management or business users can easily connect data sources and design data applications through drag-and-drop. 50+ industrial components (andon/SPC/safety green cross), 3D digital twin, mobile auto-adaptation. Built-in discrete industry modules and QCDSM indicator system
Leansight EAM is not a cost center, but a profit engine
Gaoxin Automotive Sheet Processing Co., Ltd., 15 functional modules, 68 sub-functions, full-link from data collection to equipment management
Key equipment on blanking lines, scrap lines, boiler rooms, air compressor stations, and overhead cranes were equipped with sensors to collect vibration, temperature, pressure, and flow data. Data transmitted to monitoring stations via wired/wireless methods, enabling alarm push notifications and remote diagnosis. After periodic data accumulation, intelligent alerting and intelligent diagnosis were achieved.
15 functional modules, 68 sub-functions: equipment asset management, special equipment management, mold management, maintenance management, planned maintenance, inspection & patrol management, spare parts management, maintenance skill knowledge base, personnel scheduling, data dashboard, report engine, system settings, personal workspace, group management, mobile APP
Built production line visualization monitoring dashboard, real-time tracking of stamping force, torque peak, vibration RMS speed, and pressure parameters, with second-level anomaly alerting. Boiler room/air compressor station real-time monitoring dashboard displays circulation pump vibration, temperature, pressure, and equipment status, with second-level perception of operational anomalies.