Case Study

Condition-Based Asset Maintenance

We helped the client transition from time-based to condition-based and predictive maintenance through data analytics and agile software development. By using advanced analytics and machine learning algorithms, predictive maintenance can help detect potential issues before they cause breakdowns or production delays. This proactive approach can reduce maintenance costs, minimize equipment downtime, and improve overall operational efficiency, making it an indispensable tool for modern businesses.

Lack of visualization on asset performance and no real-time condition monitoring.
No data-driven decision making about asset maintenance possible.
High administrative work due to paper-based maintenance schedules.

Client Challenge

The solution was created and iteratively refined in the environment of a pilot plant following the listed steps:

Assessment of client's hypotheses regarding main issues and root causes based on data analytics.

Definition and prioritization of use cases and respective software functionalities.
Agile software development of a condition-based asset monitoring solution using Microsoft Azure Cloud and Edge components.
Integrated change management as an essential part of solution creation and implementation.

Our Approach

Application MVP as a basis to switch from time-based maintenance to condition-based maintenance and predictive maintenance.

Foundations built to generate added value through artificial intelligence and machine learning over time.
Involved and enabled client organization ready for further solution development and rollout in all plants.

The Impact