Data driven maintenance: Drinking water company Vitens shares learnings

The pilots were stand-alone initiatives with their own limited scope. They were a great way to gain more knowledge and understanding of data driven maintenance, the tools and systems and opportunities available. However, many initiatives got stuck before reaching an improvement and most forgot to share learnings with the rest of the organization. The strategy or objectives of the pilots were not always clear, nor was the alignment with the (long-term) business strategy. In addition, every pilot team tried to find their own way through the ICT infrastructure.

The available failure data did not provide enough insights to be able to pinpoint suitable asset types for data driven maintenance. This is also a challenge for the future as predictive models need to be trained based on historical failure data. Vitens is already working on solving this as they set up their asset register in a new version of their EAM system with a better setup. In a few years they will be able to have better data insights.

Vitens aims to ensure a very high availability, which is mainly achieved by having a lot of redundancy in their asset base. As all of these assets need to be maintained, their maintenance costs are relatively high. Therefore, looking from the perspective of the asset base, optimization of maintenance is most suitable for the goal of cost reduction. In the case of Vitens, reducing cost is not achieved by reducing maintenance scope, but by optimizing the moment of maintenance execution through setting up data triggers.

To give an example: reservoirs are in the top 3 of asset types with the highest costs. For these assets periodical cleaning is needed to ensure water safety and hygiene. This takes a reservoir out of commission for 24 to 48 hours which is, of course, costly. Predicting when the reservoirs need maintenance can improve planning and preparation efforts which can be more efficient and cost-effective. Hence, reducing the asset scope is not an option, but the optimization of maintenance execution is.

Vitens already knew that they were not yet ready for a full-blown data driven maintenance strategy. However, their situation included positive elements that clearly showed that the organization wanted to work towards the next step in their digital transformation journey. They formulated three important learnings from this readiness evaluation.

Vitens now has an overview of their current data driven maintenance readiness and is taking the next steps in a controlled and centralized way. As you can tell from this example, data driven asset management goes beyond the data and systems. Processes, roles and responsibilities and also culture and way of working are all influenced and play a big role in the success of Digital Transformation.

It is valuable to stress the importance of change management: the entire organization is affected and will need to change. This should be a two-way street: communicate about the project, share successes and learnings, directly involve key stakeholders and also set up feedback loops. Vitens, for example, is very much aware of the knowledge and expertise of their work floor and field technicians. The technicians are part of the project team and are invites to give feedback and share their experience. In this way, internal adoption will go more quickly as the entire organization is working on their data driven future.