Analytics & Observability
Advanced insights with machine learning and AI

New potentials through innovative technologies

Analytics & Observability

Analytics

  • By using analytics, efficiency gains in the EAM are achieved and strategic decisions are made on the basis of data
  • Gain valuable insights for optimizing asset performance using raw data analysis
  • Various analytics techniques such as descriptive, diagnostic, predictive, and prescriptive analytics are applied in EAM to look at past events, investigate root causes, predict future events, and recommend courses of action
  • Extended asset life thanks to data-driven maintenance strategies with analytics integration in EAM.

Observability

  • Observability provides external measurement and assessment of the health of enterprise asset management (EAM) assets and systems.
  • Observability solutions in EAM can identify and visualize anomalies and misbehaviors to develop an effective maintenance strategy.
  • Observability provides insight into the status and performance of assets to optimize their performance and reliability.
  • Given the increasing complexity of distributed assets and systems, observability in EAM is becoming increasingly important. This makes it all the more important to minimize downtimes and maximize the service life of assets.
  • It is a holistic approach to monitoring and surveillance of IT systems.
Gewinnung von wertvollen Erkenntnissen für die Optimierung der Asset-Leistung mithilfe der Analyse von Rohdaten

Observability

Optimization of the maintenance strategy

Improved maintenance strategy is enabled through comprehensive visibility and continuous monitoring of resources. Analysis of past maintenance information, sensor data and performance metrics allows predictive analytics. This allows optimal timing of maintenance intervals to be determined and resource wear to be managed effectively.

Identification of weaknesses and bottlenecks

Improved maintenance strategy is enabled through comprehensive visibility and continuous monitoring of resources. Analysis of past maintenance information, sensor data and performance metrics allows predictive analytics. This allows optimal timing of maintenance intervals to be determined and resource wear to be managed effectively.

Energy system monitoring

Energy assets such as wind farms or solar power plants are optimized using Observability to improve asset performance and minimize outages. Power, temperature, and emission levels are metrics that help identify potential problems early and fix them before they lead to outages.

Optimization of spare parts management and inventories

Comprehensive performance monitoring of assets and systems enables the detection of possible deviations or bottlenecks that affect the efficiency of maintenance processes. Identifying such weak points enables targeted measures to be taken to eliminate bottlenecks and significantly increase asset efficiency.

Production monitoring

On the shop floor, accurate performance monitoring of machinery and equipment is performed to ensure timely detection and resolution of potential problems. This significantly improves overall effectiveness and efficiency. Evaluation of key performance indicators such as uptime, throughput and failure rates enables targeted identification of challenges and opens up opportunities to optimize production processes.

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Optimization of the maintenance strategy

Improved maintenance strategy is enabled through comprehensive visibility and continuous monitoring of resources. Analysis of past maintenance information, sensor data and performance metrics allows predictive analytics. This allows optimal timing of maintenance intervals to be determined and resource wear to be managed effectively.

Identification of weaknesses and bottlenecks

Improved maintenance strategy is enabled through comprehensive visibility and continuous monitoring of resources. Analysis of past maintenance information, sensor data and performance metrics allows predictive analytics. This allows optimal timing of maintenance intervals to be determined and resource wear to be managed effectively.

Energy plant monitoring

Energy assets such as wind farms or solar power plants are optimized using Observability to improve asset performance and minimize outages. Power, temperature, and emission levels are metrics that help identify potential problems early and fix them before they lead to outages.

Optimization of spare parts management

Comprehensive performance monitoring of assets and systems enables the detection of possible deviations or bottlenecks that affect the efficiency of maintenance processes. Identifying such weak points enables targeted measures to be taken to eliminate bottlenecks and significantly increase asset efficiency.

Production monitoring

On the shop floor, precise performance monitoring of machines and equipment is performed to ensure timely detection and resolution of potential problems. Evaluation of key performance indicators such as uptime, throughput and failure rates enables targeted identification of challenges and opens up opportunities to optimize production processes.

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3 Pillars of Observability

Metrics

Continuous monitoring and analysis of metrics such as availability, downtime, maintenance intervals, and spare parts requirements enables evaluation of the effectiveness of asset management strategies. This detailed evaluation identifies and implements potential improvements to enhance asset performance and reliability.

Logging

Logging in EAM refers to the collection of events, error messages, and log data from assets that are normally written to databases or process systems as part of their OT. The analysis identifies possible faults, failures or anomalies in the assets and takes action accordingly to perform maintenance or repair.

Transactions

Transactions provide detailed tracking of processes and interactions in the EAM system. Analyzing transactions makes it possible to identify bottlenecks, inconsistencies, and inefficient processes related to maintenance activities, repairs, and spare parts. As a result, targeted improvements are made in asset management.

Metrics

Continuous monitoring and analysis of metrics such as availability, downtime, maintenance intervals, and spare parts requirements enables evaluation of the effectiveness of asset management strategies. This detailed evaluation identifies and implements potential improvements to enhance asset performance and reliability.

Logging

Logging in EAM refers to the collection of events, error messages, and log data from assets that are normally written to databases or process systems as part of their OT. The analysis identifies possible faults, failures or anomalies in the assets and takes action accordingly to perform maintenance or repair.

Transactions

Transactions provide detailed tracking of processes and interactions in the EAM system. Analyzing transactions makes it possible to identify bottlenecks, inconsistencies, and inefficient processes related to maintenance activities, repairs, and spare parts. As a result, targeted improvements are made in asset management.

Analytics

Predictive Maintenance

Predictive analysis of data from sensors and other sources leads to predictions and prevention of machine problems before they occur. The result is a reduction in downtime and an increase in the efficiency of maintenance processes.

Quality control

Patterns and trends in production processes are detected and quality problems are identified early by analyzing data. This capability enables faster response to problems and improvement of product quality.

Energy efficiency

Analytics play an important role in monitoring and optimizing the energy consumption of assets. They help identify inefficient processes and enable the implementation of improvements that lead to cost savings and the achievement of sustainability goals.

Supply Chain Management

To ensure accurate planning and optimization of supply chain processes, comprehensive data is collected and thoroughly analyzed throughout the supply chain. This detailed evaluation makes it possible to effectively reduce inventories and significantly improve delivery times. The resulting increased efficiency and effectiveness of the entire supply chain leads to increased customer satisfaction and optimized operational performance.

Human resource management

In human resource management, analytics are used to collect and analyze data. In this way, employee fluctuations are predicted and prevented. In addition, this data is used to identify the training and development needs of employees and to implement targeted measures for skills development.

Analytics types

Descriptive Analytics

In the context of enterprise asset management (EAM), historical data is analyzed to gain valuable insights into asset performance. By identifying patterns, trends and correlations, this analysis helps to optimize maintenance plans, increase asset availability and uncover inefficient processes.

Diagnostic Analytics

Through comprehensive data analysis and the use of statistical models, potential failures, maintenance requirements and anomalies can be identified at an early stage. This enables the derivation of proactive measures to minimize downtime and the development of effective maintenance strategies. Such data-driven decisions contribute to the continuous improvement of asset management.

Predictive Analytics

Advanced analytics provide deep insights into the root causes of asset performance issues and variances. This enables targeted problem-solving actions to increase efficiency, reduce downtime, and optimize long-term asset performance.

Prescriptive Analytics

Prescriptive analytics provide data-driven recommendations and recommended actions. They are based on the evaluation of past data and current information to optimize asset performance, minimize risks, and increase efficiency.

Our solutions and partners

We are long-standing solutions partners of market-leading EAM systems such as IBM Maximo and Hexagon EAM.

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