In many plants, fault diagnosis is a time-consuming, manual process, as technicians have to laboriously correlate alarms from different control systems and historians. By integrating into central middleware, we create manufacturer-independent access to all event data. The added value lies in the immediate correlation of alarm timestamps with the actual historical process values. Technicians no longer just see that an alarm has been triggered, but immediately receive the visual context of the process conditions at the time of the event, which greatly accelerates the identification of fault causes.

Efficient alarm management often fails due to so-called “bad actors” – i.e., measuring points that constantly trigger alarms due to misconfigurations or wear and tear without signaling a real need for action. This flood of information leads to alarm fatigue among operating personnel and masks critical safety messages. We use the data aggregated in inmation to objectively identify these disruptive factors and specifically reduce the alarm load. This cleanup brings the truly process-critical parameters back into focus, which increases the team's responsiveness and minimizes the risk of unplanned downtime.

In order to use alarm management not only as a reactive tool but also as a strategic decision-making aid, we transfer the data into a consistent, plant-wide information model. All alarms and events are contextualized within a hierarchical asset structure. This means that information is no longer available as cryptic tag names but is directly assigned to the respective plant component or process section. This structure enables cross-location comparability of performance and forms the basis for automated reporting, which provides both maintenance and operations management with valid facts for investments and process optimizations.

In many plants, fault diagnosis is a time-consuming, manual process, as technicians have to laboriously correlate alarms from different control systems and historians. By integrating into central middleware, we create manufacturer-independent access to all event data. The added value lies in the immediate correlation of alarm timestamps with the actual historical process values. Technicians no longer just see that an alarm has been triggered, but immediately receive the visual context of the process conditions at the time of the event, which greatly accelerates the identification of fault causes.

Efficient alarm management often fails due to so-called “bad actors” – i.e., measuring points that constantly trigger alarms due to misconfigurations or wear and tear without signaling a real need for action. This flood of information leads to alarm fatigue among operating personnel and masks critical safety messages. We use the data aggregated in inmation to objectively identify these disruptive factors and specifically reduce the alarm load. This cleanup brings the truly process-critical parameters back into focus, which increases the team's responsiveness and minimizes the risk of unplanned downtime.

In order to use alarm management not only as a reactive tool but also as a strategic decision-making aid, we transfer the data into a consistent, plant-wide information model. All alarms and events are contextualized within a hierarchical asset structure. This means that information is no longer available as cryptic tag names but is directly assigned to the respective plant component or process section. This structure enables cross-location comparability of performance and forms the basis for automated reporting, which provides both maintenance and operations management with valid facts for investments and process optimizations.

In many plants, fault diagnosis is a time-consuming, manual process, as technicians have to laboriously correlate alarms from different control systems and historians. By integrating into central middleware, we create manufacturer-independent access to all event data. The added value lies in the immediate correlation of alarm timestamps with the actual historical process values. Technicians no longer just see that an alarm has been triggered, but immediately receive the visual context of the process conditions at the time of the event, which greatly accelerates the identification of fault causes.

Efficient alarm management often fails due to so-called “bad actors” – i.e., measuring points that constantly trigger alarms due to misconfigurations or wear and tear without signaling a real need for action. This flood of information leads to alarm fatigue among operating personnel and masks critical safety messages. We use the data aggregated in inmation to objectively identify these disruptive factors and specifically reduce the alarm load. This cleanup brings the truly process-critical parameters back into focus, which increases the team's responsiveness and minimizes the risk of unplanned downtime.

In order to use alarm management not only as a reactive tool but also as a strategic decision-making aid, we transfer the data into a consistent, plant-wide information model. All alarms and events are contextualized within a hierarchical asset structure. This means that information is no longer available as cryptic tag names but is directly assigned to the respective plant component or process section. This structure enables cross-location comparability of performance and forms the basis for automated reporting, which provides both maintenance and operations management with valid facts for investments and process optimizations.

In many regulated production processes, the time between the end of a batch and its technical release is a bottleneck. The automatic consolidation of process and laboratory data eliminates the tedious manual compilation of protocols. Deviations are immediately flagged in the context of the respective phase, which speeds up the review process and reduces throughput times.

In order to maintain consistently high production quality, retrospective analysis is often not enough. We enable real-time technical comparison of current batches with ideal reference curves (golden batch). Deviations in critical parameters such as pressure, temperature, or phase durations are immediately visible, allowing corrective action to be taken before rejects occur.

Every second that a batch takes longer than planned reduces the overall equipment effectiveness (OEE). By closely monitoring phase durations, we identify systematic delays in the process flow. This database enables operations management to precisely locate bottlenecks and optimize recipe processes in a targeted manner.

In many regulated production processes, the time between the end of a batch and its technical release is a bottleneck. The automatic consolidation of process and laboratory data eliminates the tedious manual compilation of protocols. Deviations are immediately flagged in the context of the respective phase, which speeds up the review process and reduces throughput times.

In order to maintain consistently high production quality, retrospective analysis is often not enough. We enable real-time technical comparison of current batches with ideal reference curves (golden batch). Deviations in critical parameters such as pressure, temperature, or phase durations are immediately visible, allowing corrective action to be taken before rejects occur.

Every second that a batch takes longer than planned reduces the overall equipment effectiveness (OEE). By closely monitoring phase durations, we identify systematic delays in the process flow. This database enables operations management to precisely locate bottlenecks and optimize recipe processes in a targeted manner.

In many regulated production processes, the time between the end of a batch and its technical release is a bottleneck. The automatic consolidation of process and laboratory data eliminates the tedious manual compilation of protocols. Deviations are immediately flagged in the context of the respective phase, which speeds up the review process and reduces throughput times.

In order to maintain consistently high production quality, retrospective analysis is often not enough. We enable real-time technical comparison of current batches with ideal reference curves (golden batch). Deviations in critical parameters such as pressure, temperature, or phase durations are immediately visible, allowing corrective action to be taken before rejects occur.

Every second that a batch takes longer than planned reduces the overall equipment effectiveness (OEE). By closely monitoring phase durations, we identify systematic delays in the process flow. This database enables operations management to precisely locate bottlenecks and optimize recipe processes in a targeted manner.

In many companies, key performance indicators are spread across different systems such as MES, ERP, and individual machine HMIs. This situation forces those responsible to correlate data manually, which is error-prone and costs valuable time. By consolidating all KPIs in a central information model, we create a “single source of truth.” The key advantage lies in contextualization: you not only see isolated values, but also immediately recognize how a process deviation affects the overall production goals in depth.

Not every user needs the same depth of information. While maintenance requires detailed asset condition data, operations management focuses on throughput times and OEE. We develop role-specific dashboards that are tailored precisely to these needs. Hierarchical process navigation allows users to drill down from the global site overview to the level of individual sensors as needed. This ensures that decisions at every level are made on the same valid data basis.

By connecting to live data, we no longer calculate key figures retroactively, but in real time. The spectrum ranges from technical parameters (CPPs) and batch runtimes to energy efficiency values. Integration into BI tools such as Power BI or Tableau also allows this operational data to be linked to commercial information. This reveals hidden inefficiencies that would have remained concealed in isolated systems.

Thanks to our many years of experience in traditional mechanical and plant engineering, we speak the language of your production fluently.

We are not tied to any specific hardware manufacturers and seamlessly integrate heterogeneous system landscapes – from sensors to the cloud.

Our solutions grow with your requirements—from rapid proof-of-concept to global, multi-site rollout.

We break down silo thinking by combining automation requirements with modern IT architecture standards.

Thanks to our many years of experience in traditional mechanical and plant engineering, we speak the language of your production fluently.

We are not tied to any specific hardware manufacturers and seamlessly integrate heterogeneous system landscapes – from sensors to the cloud.

Our solutions grow with your requirements—from rapid proof-of-concept to global, multi-site rollout.

We break down silo thinking by combining automation requirements with modern IT architecture standards.

Thanks to our many years of experience in traditional mechanical and plant engineering, we speak the language of your production fluently.

We are not tied to any specific hardware manufacturers and seamlessly integrate heterogeneous system landscapes – from sensors to the cloud.

Our solutions grow with your requirements—from rapid proof-of-concept to global, multi-site rollout.

We break down silo thinking by combining automation requirements with modern IT architecture standards.

Als Automatisierungsexperte übernimmt cts die volle Verantwortung – von der Konzeptvalidierung über die mechanische Integration bis hin zur fertigen ERP/MES-Anbindung.

Durch die Middleware cts AXES und offene Schnittstellen (REST/MQTT) wird das Lager zum voll integrierten Knotenpunkt Ihres digitalen Materialflusses.

Mit Kapazitäten von 400 bis über 2.000 Lagerplätzen und einer Bauhöhe von bis zu 8 Metern bieten wir Lösungen, die mit Ihren Produktionsanforderungen mitwachsen.

Das System ist nativ für das Zusammenspiel mit AMRs und Fördertechnik ausgelegt, um einen unterbrechungsfreien Materialtransport ohne manuelle Eingriffe zu garantieren.

Als Automatisierungsexperte übernimmt cts die volle Verantwortung – von der Konzeptvalidierung über die mechanische Integration bis hin zur fertigen ERP/MES-Anbindung.

Durch die Middleware cts AXES und offene Schnittstellen (REST/MQTT) wird das Lager zum voll integrierten Knotenpunkt Ihres digitalen Materialflusses.

Mit Kapazitäten von 400 bis über 2.000 Lagerplätzen und einer Bauhöhe von bis zu 8 Metern bieten wir Lösungen, die mit Ihren Produktionsanforderungen mitwachsen.

Das System ist nativ für das Zusammenspiel mit AMRs und Fördertechnik ausgelegt, um einen unterbrechungsfreien Materialtransport ohne manuelle Eingriffe zu garantieren.