Connect machine events, orders and operational status over time.
Manufacturing software and data engineering
Manufacturing software, ERP and MES data integration
Connect machine events, jobs and quality records into an operational data model.
Operational constraints and integration boundaries
Start with the real workflow and accountable business decision.
Disconnected machine signals, ERP records and MES status values lead to conflicting production reports. Timestamp normalization and stable order identifiers are essential.
Digital capabilities in Manufacturing
Six possible applications. The correct approach depends on available data and systems.
Understand OEE and downtime
Apply documented availability, performance and quality definitions.
Integrate quality findings
Associate inspection results with orders and machine conditions.
Develop internal production apps
Provide workflow screens for status, approval and exceptions.
Investigate anomalies
Contextualize unusual sensor patterns with production records.
Modernize production software
Add interfaces and functions without replacing stable equipment workflows.
Five engineering services for your requirements
We choose the relevant disciplines based on the business process rather than applying a predefined technology package.
Assess machine data, target metrics and system boundaries.
Develop shop-floor applications and ERP/MES integrations.
Implement pipelines, quality metrics and evaluated ML where justified.
Measure relevant customer-facing industrial portals.
Explain industrial capabilities with clear technical examples.
Architecture and practical trade-offs
A production KPI depends on consistent definitions, clock synchronization and reliable order mapping. AI is not useful until the underlying data and reaction paths are established.
We develop new software where it creates a needed capability and retain existing applications where they remain fit for purpose. We evaluate data quality, interfaces, access and operations together.
AI is one possible approach, not a default. We compare rules, analytics, machine learning and generative AI against the actual requirements.
From operational need to a working system
Start with one production line, asset or KPI and confirm relevant data sources and exception handling.
Define the outcome
Specify the workflow, users and evidence that would demonstrate improvement.
Inspect systems and data
Assess source records, integrations, exceptions and ownership.
Choose the architecture
Compare packaged software, integration and custom development.
Implement and verify
Develop interfaces, data and workflows incrementally with user tests.
Prepare operational use
Set permissions, monitoring, error handling and acceptance criteria.
Scope, evidence and accountability
These examples represent potential engineering use cases, not a claim that Valanor has already delivered each project in Manufacturing. Actual work and outcomes require project-specific evidence.
Regulatory and professional requirements are established with the responsible client specialists. We do not claim unverified certification or quantified results.
Frequently asked questions: Manufacturing
Important questions before investment in technical systems.
What is the first step to better production reporting?
Identify one operational decision and validate its machine and work-order inputs.
Must the MES be replaced?
Often no. Integrations or additional reporting may be sufficient.
When is edge AI appropriate?
When local inference, reaction time or connectivity create specific requirements.
How is manufacturing different from automotive?
Manufacturing addresses broad production data integration, while automotive additionally covers component traceability and supplier quality chains.
Related application
Read the detailed technical use case.
Engineering discussion
What are you trying to improve?
Share the process, existing systems and intended result. Start with one production line, asset or KPI and confirm relevant data sources and exception handling. Aleksandar Basara can review a realistic technical first step.
Aleksandar Basara
Managing Director
aleks@valanor.co
+49 (0) 711 16 44 6 83
Lindenspürstrasse 22, 70176 Stuttgart
