Delivery
Predictive Maintenance
Industry
Painting, Production and Maintenance
Area
Distribute maintenance insights worldwide
Impact
Real-time AI-models integrated in an App
Introduction
Optimizing maintenance in a global and complex operation.
Hempel is a global supplier of paints and surface protection solutions across a wide range of industries. The company is particularly known for its solutions in the maritime sector, where its products contribute to increased durability, lower fuel consumption, and reduced operational costs for ships. With operations in more than 80 countries, Hempel continuously works to develop new solutions that both strengthen the business and improve customer operations.
Challanges
Predictive Maintenance based on data-insights.
Ship maintenance is a constant balancing act. Surfaces need to be maintained to prevent wear, inefficiency, and failures, while at the same time, taking a vessel out of service too early is both costly and disruptive. In addition to the direct costs, poorly timed maintenance can result in significant revenue loss. The challenge is further compounded by the maritime environment, where conditions such as weather, operational loads, and sailing patterns are constantly changing. As a result, the actual condition of a vessel’s surfaces can be difficult to assess and is often partially hidden. This made it challenging to plan maintenance based on actual condition rather than fixed intervals or assumptions.
The Solution
Data and AI as the foundation for more precise and predictable maintenance.
Hempel’s goal was to use data and AI to shift maintenance from a reactive cost to a more predictable and value-creating discipline. In collaboration with Hempel, Codellent designed, developed, and implemented a data and AI platform in Azure to support this transformation. The platform was built using domain-specific data models and automated data pipelines from relevant sources, ensuring that information could be continuously updated and used in analysis. Based on this, Codellent developed AI models using neural networks to identify patterns and predict maintenance needs. To ensure long-term stability, an MLOps setup was established with continuous retraining, along with integrations into relevant applications and business systems.
Final thoughts
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PROJECTS