Case Study

Establishing a Digital Framework for Effective Predictive Maintenance

Predictive maintenance is essential for companies that rely on complex machinery and equipment to operate. 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.

Without a way to visualize asset performance or monitor conditions in real-time, our customer was unable to make data-driven decisions about asset maintenance. As a consequence, our client was burdened with a significant amount of administrative work stemming from reliance on paper-based maintenance schedules.

Client Challenge

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

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

2. Definition and prioritization of use cases and respective software functionalities.

3. Agile software development of a condition-based asset monitoring solution using Microsoft Azure Cloud and Edge components.

4. Integrated change management as an essential part of solution creation and implementation to ensure that engineering managers, as well as end-users, realize the full potential of the new applications.

Our Approach

We delivered an application MVP as a basis to switch from time-based maintenance to condition-based maintenance and predictive maintenance. This helped build the foundations to generate added value through artificial intelligence and machine learning over time. Through our "integrated change management," we involved and enabled our client's organization, making them ready for further solution development and rollout in all plants.

The Impact