Enterprise Warehouse features and workflow

The Enterprise Warehouse has these key features:

The ability to handle multi-site, aggregated data
An OLAP Star/Cube structure to manage multi-dimensional data sets
The ability to process on-premise or cloud-hosted data with SSL and encryption support
Incremental data loading and data transformation capabilities
Publish multi-site data from any Open Data (OData)-supported BI platform

An incremental Extract, Transform, Load (ETL) engine extracts and combines transactional data from multiple factories/work sites into a single Enterprise Warehouse database for processing. The FactoryLogix Reporting Service uses the ETL engine to stream data to the Enterprise Warehouse database.

Note

Some FactoryLogix users run DataMiner queries using a replicated server to produce reports and analytics so their production server isn't compromised. With the Enterprise Warehouse, this approach is no longer necessary.

Enterprise data sources

Enterprise data sources can include any of the following:

Factory, line, and individual machine throughput
Resource utilization and performance
Overall Equipment Effectiveness (OEE)
Production downtime and root cause analysis
Quality and yields by customer, product, work order, line, machine, operator
Defect rates, paretos, and analysis
Material spoilage
Material consumption by customer, product, work order
Inventory aging
On-time versus late production orders

Publish data from any OData-supported BI platform

At both the site level and enterprise level, a Business Intelligence gateway utilizes the industry standard Open Data (OData) protocol to transform data and prepare it for publishing. Using the gateway, you can publish enterprise data from DataMiner or any other BI platform that supports OData such as Power BI, Oracle BI, SAP Business Objects, DOMO, and Adobe Analytics. You can also combine DataMiner data with ERP and PLM systems’ data to create robust, full-spectrum enterprise analytics and BI reports.