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About DMP
The DMP big data platform brings data and AI together. High performance, intelligence-driven, elastically scalable and easy to adopt, it helps enterprises optimise processes, maintain equipment and cut energy use — turning data into productive capacity and powering intelligent decisions.
Performance Strengths
- A purpose-built engine for manufacturing
Deeply combines the full know-how of discrete heavy industry with AI algorithms to match the intelligence needs of production, logistics and other core scenarios, adapting to industry workflows without custom development.
- Lightweight architecture, easy to run
A distributed MPP architecture keeps the technology stack simple and intuitive, lowering both the barrier to deployment and the cost of running it.
- High performance, elastic scaling
Stores, computes and analyses data at scale with a horizontally scalable design that reallocates resources dynamically, raising utilisation and response speed.
- One-stop closed-loop data service
Integrates collection, storage, governance, analysis, visualisation and intelligent applications into one chain, closing the loop from data to results without complex integration.
Functional Value
- 01
Multi-source intelligent data integration
Connects seamlessly to ERP, MES, PLM and other business systems as well as heavy production equipment, supporting fast ingestion and real-time sync of many data formats and breaking down data barriers without complex configuration.
- 02
Drag-and-drop data scheduling
Graphical task orchestration builds workflows quickly, while built-in scheduling strategies dispatch jobs dynamically by data-source condition and task priority, keeping processing efficient and stable.
- 03
End-to-end intelligent data governance
Automates cleaning, standardisation and deduplication and adds human review, safeguarding production data quality as a reliable basis for analysis and decisions.
- 04
Drag-and-drop visual analysis
A rich set of chart components and custom dashboards lets users build visual reports by dragging and dropping, showing equipment status, capacity and efficiency at a glance.
- 05
Flexible report management
Highly customisable report management makes it easy to configure any kind of analytical report and get full value from the data.
- 06
A dedicated analytics framework for discrete heavy industry
Built-in industry-standard analytical models and metric systems provide out-of-the-box analytics for discrete heavy industry.
- 07
AI-native natural-language queries
Built-in NL2SQL turns a single sentence into a data insight.
- 08
Automated analysis reports
AI consolidates multi-dimensional data into analysis reports with no manual work.
Applications
01 Production Process Optimisation and Monitoring
- A. Real-time production monitoring: business data from every production stage is consolidated for live monitoring, exposing bottlenecks and waste early so they can be optimised.
- B. Production plan optimisation: data analysis improves demand forecasts and resource allocation, raising production efficiency.
02 Supply Chain Management and Inventory Control
- A. Supply chain optimisation: data from every link is consolidated to optimise the network and make the supply chain faster and more flexible.
- B. Precise inventory control: stock status is monitored in real time and demand forecast, helping prevent both overstock and stockouts, lower inventory costs and raise turnover.
03 Energy Management and Emission Reduction
- A. Energy data analysis: electricity, water, gas and other production energy data are collected and analysed to identify waste and propose energy-saving and emission-reduction measures.
- B. Intelligent energy management: the big data platform manages and dispatches energy intelligently, improving efficiency and reducing consumption and emissions.
04 Cost Management
- A. Cost accounting: the platform automatically collects raw-material, labour, depreciation and other production cost data for accounting and analysis.
- B. Cost control: big data analysis and forecasting expose weak points and recommend improvements that lower production costs.
05 Quality Management and Compliance
- A. Quality management optimisation: inspection records, defect rates and other quality data are analysed with statistics and machine learning to identify root causes, guide improvement and raise product quality and customer satisfaction.
- B. Compliance monitoring: the platform helps collect, organise and store quality standards, safety rules, regulatory requirements and other compliance data.
06 Predictive Equipment Maintenance
- A. Real-time operating data combined with machine learning enables accurate equipment health assessment and predictive maintenance.
- B. Alerts support targeted plans covering preventive maintenance, scheduled servicing and fault repair.