Put machine events into context
Connect technical signals to the right asset, line and production order.
Industrial data integration
Integrate machine, ERP and MES data for production decisions
Production planning needs consistent relationships between machine events, orders, quality and material records. We design interfaces, timestamps, identifiers and validation checks that make production data usable in operational applications.
Why production reports do not always agree
Machine signals use different rates, identifiers and formats than ERP and MES records. Without consistent timestamps and job identity, reports from the same shift may show contradictory production status.
A reliable solution begins with workflow, data ownership and accountable quality criteria—not a preferred platform or algorithm.
Four production data capabilities
Machine records only become useful when they can be understood in the context of orders and operational decisions.
Understand downtime
Provide event sequences and documented status definitions for root-cause analysis.
Calculate OEE consistently
Use agreed availability, performance and quality rules with validated time periods.
Serve operational applications
Make exceptions available in dashboards and role-specific workflows.
How production data integration works
The integration layer ingests machine events and joins them with production orders from ERP or MES. Buffering, timestamp normalization, status mapping and data validation are core components.
A shared model prevents every dashboard from interpreting the same KPI differently. Data updates and exceptions must be traceable to their source, not silently corrected.
The technical implementation is part of Data & AI Engineering and Platform Engineering.
From production problem to a verifiable pilot
Prove data identity, consistency and usefulness on a bounded process before expanding.
Select an operational question
Specify which downtime, order or quality decision needs more reliable information.
Validate assets and work orders
Review identifiers, timestamps, states and available interfaces.
Develop event processing and models
Implement order context, time intervals and documented calculations.
Test with production owners
Compare KPIs with actual events and exceptions before adding more assets.
Engineering trade-offs and data boundaries
Centralizing every raw machine signal is not always useful. Appropriate granularity, latency and volume depend on the operational question. Predictive maintenance models should only be considered once a reliable event history exists.
Evaluate KPI consistency, order association, visible data gaps and usefulness for production decisions. Compare alternative methods and relevant failure cases. Keep existing operational systems where they still fulfill their role.
Requirements for dependable integration
These conditions affect the reliability of reporting and the engineering effort.
Time alignment
Clock differences and event intervals must not distort metrics.
Work-order association
Signals must map to the correct production step.
Status semantics
Distinguish production, setup, downtime and other relevant states.
Operational resilience
Handle outages, late events and controlled replay without hidden data loss.
Engineering scope and expected evidence
These workflows describe possible technical applications, not verified outcomes from a named customer. Architecture, data scope and measurable acceptance must be determined for the real production process.
Our manufacturing industry page explains the context of the five Valanor services. Data & AI Engineering covers the integration and analytical implementation.
Manufacturing context
Review industry requirements for shop-floor integration, quality and operational applications.
Frequently asked questions: production data integration
Technical decisions before connecting machines, ERP and MES.
How is this different from an MES?
We develop integrations and supporting applications; a functional MES does not need to be replaced.
Is a real-time platform required?
Only when a business decision needs very low latency. Many planning and quality analyses do not.
How is OEE calculated consistently?
By agreeing on availability, performance and quality definitions and validating time and order information.
Can the data feed AI models?
Yes, when sufficient history, quality and a defined use case justify model development.
Discuss production data
Which production decision needs better information?
Describe the production step, machine and order sources and desired outcome. Aleksandar Basara can discuss an appropriate technical starting point and data validation plan.
Aleksandar Basara
Managing Director
aleks@valanor.co
+49 (0) 711 16 44 6 83
Lindenspürstrasse 22, 70176 Stuttgart
