Industrial Intelligence Methodology
Grow industrial data into industrial intelligence
Jiuze defines a phased, deliverable, and sustainable methodology for manufacturers—from data connectivity to intelligent applications.
Industrial intelligence growth path
Build evolvable capabilities in stages: connect, then operate, then intelligence.
- 01
Connect
- Device networking and data acquisition
- Data standardization and governance
- Equipment and data assetization
- Build equipment profiles
- 02
Operate
- Unified operations view
- Metrics and analytics
- Process collaboration and optimization
- Data-driven decisions
- 03
Intelligence
- Knowledge retention and sharing
- Industrial agent applications
- Predictive insights and recommendations
- Autonomous optimization and closed loops
Industrial intelligence maturity model
Five levels to assess the present, define targets, and support phased construction.
- Level 1
Device connectivity
Connect equipment and collect data; know online status in real time.
- Level 2
Data visualization
Centralized data management with key metrics visible and operations perceivable.
- Level 3
Operations optimization
Digitized business processes with data-driven operations and collaboration.
- Level 4
Knowledge retention
Industry knowledge and experience retained to drive continuous improvement.
- Level 5
Industrial intelligence
Intelligent applications in production—prediction, recommendations, and autonomous decision loops.
Build methodology
Six steps from assessment and planning through delivery and continuous iteration.
- 01Current-state assessmentInventory needs; clarify problems and goals.
- 02Top-level planningDefine blueprint and phased targets.
- 03Data connectivityConnect equipment and break data silos.
- 04Capability buildingBuild data and operations capabilities that create value.
- 05Intelligent applicationsIntroduce intelligent apps to improve decision efficiency.
- 06Continuous iterationKeep optimizing to drive sustained value growth.
Build value
Measure investment with quantifiable outcomes that support sustainable evolution.
Cost reduction
Lower equipment maintenance costs
15–30%Efficiency gains
Higher production efficiency
10–25%Quality improvement
Better product quality
10–20%Risk reduction
Lower equipment failure rates
20–40%Sustainability
Data assets that keep appreciating
Continuous growth