Enterprise
Manufacturing
Data platforms, software and analytical services for manufacturing. Equipment-data acquisition, integration with production and business data, monitoring applications and processing deployment on-premises or in the cloud.
Equipment, production and quality
A machine measurement becomes usable when associated with an asset, a unit, a timestamp and a quality status. Production-order, product and batch references add manufacturing context. Interfaces connect this data with monitoring applications, analysis and maintenance history.
Examples of data processed in this sector
Machine measurements and events
Temperatures, vibrations, counters and operating states describe equipment observations.
Orders, products and batches
Production orders, bills of materials, batches and serial numbers connect operations with manufactured products.
Quality and maintenance
Inspection results, defects, interventions and replaced parts extend the history of a batch or asset.
Documented practices and systems in Switzerland
Public sources describing organisational practices and software interfaces. PERINGER project references appear separately below.
Engineering services
Industrial acquisition and interfaces
Connectors for equipment interfaces, including OPC UA where available, and production systems. Units, timestamps and quality states are preserved, with logging and collection-interruption handling.
APIs and systems integration →Production history and data models
Time-series databases and warehouses connecting measurements, production orders, products and batches. Data models support production-sequence comparison, inspection context and application data access.
Data platforms and warehouses →Monitoring and quality applications
Web or mobile interfaces for viewing measurements, recording inspections and linking observations with production references. Permissions, attachments and synchronisation with connected systems.
Custom software and applications →Time-series analysis and anomaly detection
Sensor-data preparation, statistical processing and API integration of models. Results are evaluated against identified historical datasets, retaining parameters, versions and execution conditions.
Predictive analytics and anomaly detection →Infrastructure and data-flow operations
Deployment of gateways, databases and analytical services in the selected environments. Configuration management, job monitoring and recovery procedures coordinated with equipment operating constraints.
Infrastructure automation →
Data protection and system security
The data-protection framework depends on the organisation’s status and processing activities. For processing subject to the revised Swiss Federal Act on Data Protection (FADP), security measures and protective defaults are defined from the design stage according to risk.
Integration distinguishes the industrial network, collection gateways and IT applications. Remote access, write permissions and maintenance operations are defined with the equipment owners. Technical records can also contain operator information subject to data-protection rules. Official source
Technical measures to define for the processing involved
- Separate IT and OT access, restricted service accounts and controlled administrative connections
- Unit and timestamp validation, collection-outage monitoring and historical-data recovery
- Interface tests and changes coordinated with equipment operating constraints
Technical guidance · FDPIC · NCSC
Legal framework, providers and incidents
Purposes, retention and rights
The design specifies the data collected, its uses, recipients and retention periods. It provides for information to individuals and handling of access or rectification requests. Record-keeping and documentation duties are assessed against the processing involved and applicable exemptions.
Hosting and processors
An organisation remains responsible when it entrusts data to a provider. The assessment covers contracts, subprocessors, processing locations and access from abroad. Hosting outside Switzerland is possible subject to the applicable transfer safeguards.
Risk assessment and incidents
A data protection impact assessment (DPIA) is required when planned processing is likely to pose a high risk to individuals. The controller reports a data security breach to the FDPIC as soon as possible when it is likely to create a high risk to their personality or fundamental rights.
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Discuss your project
A discussion of your project’s design, development and production deployment.