Building a data-driven factory operations management system — from invisible to visible, from cannot-do to can-do, from out-of-reach to within-reach, from hard-to-collaborate to smooth-collaboration
Data silos, business silos, geographic silos, industry silos — intertwined constraints make the transformation journey long
Factory floor problems are invisible; equipment status, production progress, and quality anomalies cannot be perceived in real time. Supplier delivery and quality information is inaccessible.
Cross-department resolution is difficult once problems are found; business functions are fragmented across separate systems. Work order flow is impeded, and problem handling lacks closed loops.
Factory remote direction is inefficient, and management metrics are inconsistent. Multiple factories are distributed across different locations, equipment is scattered across different areas, and management span is insufficient.
Data is scattered across isolated systems; ERP/MES/EAM/WMS/QMS each operate independently. Supplier production and supply chain data are not integrated.
CEO Headline stands at the top of the tower, forming the fourth layer of "Autonomous Discovery" — not waiting for problems to occur before handling them, but AI proactively discovering and alerting
CEO Headline is the "brain" of the Control Tower, scanning factory-wide OEE trends, MTBF decline, spare parts consumption anomalies, quality fluctuations, and other indicators 24/7. When hidden deterioration has not yet triggered alerts, AI provides early warning and automatically opens cases, pushing headlines to management. No longer "investigate after problems occur," but "tell you before problems occur."
L1 Real-time Visibility: What is happening now? Real-time transparency of operations management across manpower, machine, material, method, environment, and measurement dimensions — personnel turnover rate/multi-skill rate, real-time output/OTD delivery nodes, equipment utilization rate/MTBF/MTTR, inventory transparency, energy and carbon management, environment and safety monitoring. QCDSM business metrics visualization.
L2 Data Analysis: Why did it happen? What will happen next? Layer-by-layer drill-down of anomalous data, root cause analysis, trend prediction, TOP problem analysis. Complete analysis path from business goals → KPI indicators → system data → business activities.
L3 Closed-loop Management: How to improve? Anomalous indicators auto-trigger work orders → push problem causes and solutions → on-site follow-up handling → timeout escalation management → knowledge base accumulation → AI continuous optimization. Dual-wheel drive of management closed loop + knowledge closed loop.
Tear down data chimneys, integrate ERP/MES/EAM/WMS/QMS and other business systems. Lean application market is ready to use out of the box, supporting both subscription and perpetual license models. Application atomic design — "Lego-style" composition, large applications can be split into multiple sub-applications and flexibly combined. Data is automatically integrated between applications, eliminating information silos.
LeanFusion Data Fusion: Lightweight data fusion platform, data warehouse modeling, data integration, data development. Fuses MES/ERP/QMS/EAM/IoT heterogeneous data into data warehouse, ODS-DWD-DWS-ADS layered governance. 100K/sec single-node concurrency, 1 billion records real-time query, PB-level storage, 20% data volume compression.
LeanCodee Low-Code Development: Model-driven, visual forms and process orchestration. Industrial object modeling (equipment/BOM/workstation/production line) + behavior modeling (maintenance rules/alert rules/scheduling rules). 1000+ industrial component models, 30+ standard industrial APPs, 7-day standard application go-live, 80% maintenance cost reduction. Supports application independent deployment outside the platform.
LeanBI Industrial Visualization: Fully configurable data applications. 50+ industrial components (andon/SPC/safety green cross), 3D digital twin, mobile auto-adaptation. Built-in discrete industry modules and QCDSM indicator system. 10+ industry operations templates, 100+ industrial indicator systems.
From business to operations, from strategy to execution, comprehensive transparency management
Five-dimensional business cockpit: Q Quality Traceability (AUDIT/DRR/3misIPTV), C Cost Control (variable cost per unit/inventory turnover), D On-time Delivery (manufacturing HPV/order fulfillment rate), S Safety Production (liability accidents/hazard improvement), M Personnel Management (turnover rate/multi-skill rate).
Six-dimensional operations management: Personnel Management (turnover rate/multi-skill rate), Equipment Management (utilization rate/MTBF/MTTR), Material Management (inventory transparency/kit analysis), Process Management (parameter monitoring/process modeling), Environment Management (unit energy consumption/wastewater utilization rate), Measurement Management (quality data/SPC).
Vertical drill-down through hierarchy levels: General Manager → Business Unit Director → Business Unit Manager → Warehouse Manager → Transportation Manager. Horizontal display of indicator current status, recent changes, TOP issues, achievement rankings. Different roles view different dimensions of QCDSM data.
Daily/weekly/monthly meeting integration. 4M1E change points auto-presented, meeting achievement rate tracking, meeting action item follow-up. Data-driven meetings — data is prepared before the meeting, focus on improvement actions during the meeting, work order closed-loop execution after the meeting.
Core indicators: utilization rate, OEE, failure rate. Insight path: equipment monitoring analysis → locate equipment with low utilization rate → analyze failure types → identify failure causes → prevent in advance through inspection and maintenance plans. OEE-related areas can jump to secondary detail views.
Mobile auto-adaptation, view key indicators anytime anywhere. Mobile work order processing — scan-code repair request, photo evidence, anomaly reporting, work order confirmation. Managers can monitor factory dynamics on the go, operators can close-loop handle problems on-site.
From business goals to business activities, four-step penetration, layer by layer progression
Indicator anomaly → layer-by-layer drill-down → root cause analysis → trigger work order → closed-loop improvement. Transition from after-the-fact and in-process management to proactive preventive management
Anomalous indicators auto-trigger work orders → solution recommendation → review closed loop → knowledge accumulation, forming a continuous improvement flywheel
Real-time alerting on indicator anomalies, auto/manual work order trigger. Automatically carries anomaly indicator details, supports image, table, and voice attachments. Designated handler receives the work order.
After handler selects a cause, the system auto-matches recommended solutions, showing historical success rates. AI-assisted root cause analysis, linking similar historical cases.
Handler submits for review, initiator approves or rejects. Mobile work order processing, auto-escalation if not handled within timeout. Statistics refreshed after processing completion.
After work order completion, generates knowledge base entries, updates problem library and solution library. Multi-dimensional analysis of work order processing efficiency, continuously optimizing recommendation accuracy.
Five-phase progressive advancement, from pilot to global, from transparency to intelligence
The Control Tower is not a cost center, but a profit engine — quantified value, visible returns
| Dimension | Before Transformation | After Transformation | Value |
|---|---|---|---|
| Problem Discovery | After-the-fact firefighting, manual investigation after problems occur, losses already incurred | CEO Headline AI 24h proactive scanning, hidden deterioration early warning 3-7 days in advance | Proactive Alerting |
| Data Transparency | Data scattered across ERP/MES/EAM systems, cannot see all, cannot see clearly | QCDSM + Man/Machine/Material/Method/Environment/Measurement full-dimension transparency, multi-role multi-level drill-down | Global Visibility |
| Root Cause Analysis | Manual experience judgment, 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 | Precise Localization |
| Closed-Loop Management | Cross-department coordination difficult after problem discovery, no tracking of handling process | Work order auto-trigger → solution recommendation → review closed loop → knowledge accumulation, auto-escalation on timeout | Closed-Loop Execution |
| Cross-Department Collaboration | Business silos, departmental data disconnected, low meeting efficiency | Daily/weekly/monthly meeting data-driven, 4M1E change points auto-presented, action item work order follow-up | Smooth Collaboration |
| Knowledge Management | Fault experience scattered, personnel turnover = experience loss | Enterprise-level knowledge base, work orders auto-archived, AI similarity matching, new hires quickly up to speed | Knowledge Accumulation |
| Management Span | Single factory single workshop, low remote direction efficiency | Group → Factory → Workshop → Production Line → Shift → Equipment six-level penetration, mobile anytime anywhere | Penetration Management |
| Extensibility | Requirement changes need scheduled development, long cycle high cost | Low-code drag-and-drop configuration, 7-day standard application go-live, 80% maintenance cost reduction | Agile Extension |
From automotive manufacturing to electronics & semiconductors, the Control Tower has taken root in multiple lighthouse factories
Introduced lean production digital indicator system for the automotive industry, achieving transparent management of factory production. Mined and analyzed core indicators such as quality defects and production achievement rates, driving production process optimization through data. Established a production process intelligent alerting system for real-time response to anomalies. Assisted the customer in launching the first industrial internet platform for the automotive industry, achieving self-service development and delivery of business systems.
Established a digital quality management system based on ISO9001-2015. Integrated 20+ heterogeneous business systems (MES/ERP/QMS etc.), real-time collection of EAP/PLC equipment data, deployed distributed storage clusters, built hot-cold layered data architecture, supporting daily tens of millions of data entries and PB-level storage. Built a business platform through low-code for rapid collaborative application development. Deeply integrated with upstream supply chain for timely quality and delivery data traceability. Won Jiangsu Provincial Smart Manufacturing Demonstration Factory and Apple Supply Chain Lighthouse Factory honors.
Not built from scratch, but assembled Lego-style based on Leansight's three major product platforms
Lightweight data fusion platform, data warehouse modeling, data integration, data development, fusing heterogeneous data into data warehouse with layered governance
Model-driven low-code platform, industrial object modeling + behavior modeling, visual forms and process orchestration, supports independent application deployment
Fully configurable data applications, 50+ industrial components, 3D digital twin, mobile auto-adaptation, built-in QCDSM indicator system
Eight thinking models driving product design and operational practice
Everything can be decomposed. Software into components, business into blueprints, knowledge into models, design into templates
If not necessary, do not add entities. Minimization principle, derived plugin design, reduce unnecessary waste
Flow is core. Different roles enable application orchestration capability to flow, reduce waste, continuous improvement
Self-growth capability. From development to application development to partner development to customer self-development
Time thinking. Anything that cannot be reused is not worth doing; models/modules/applications/industries continuously reused
The whole is greater than the sum of its parts. The whole of industrial applications is the integration of knowledge, technology, and aesthetics
Achieving certainty in uncertainty. Not providing specific applications, but providing the "energy" to realize applications
See the effect first, then optimize. Never stop POC, continuous building capability