Asset health is crucial for water authorities to continually meet their primary goals (water safety, water quality and water quantity). However, are the use, maintenance and long-term investments in-line with the current health of the assets? Unfortunately, important knowledge is being lost due to an ageing workforce, the scarcity of technical staff and the fact that through centralization and remote management we are also less physically in contact with our assets. On the other hand, there are important technological developments that can make a positive contribution. In this article, we offer five tips to boost the health of your assets.

Asset knowledge resides across three dimensions: human cognition (observe, memory, interpret) , degradation models (understanding how things break down) and data. As a result of the aforementioned trends, human cognition has decreased without being compensated with more knowledge of degradation or more data use. As a result, asset knowledge has decreased. Water authorities are less ‘in control’ – resulting in an increasing risk. So the challenge is to make the triangle bigger again.

1. Know the health of your assets

Asset health is not only about condition, but also the extent to which an asset delivers the required performance now and in the future. This applies to the RAMS preconditions: reliable, available, maintainable and safe (RAMS). Gaining insight into asset health, therefore, means getting insight into these RAMS conditions. If we relate this to the overall asset portfolio, it is also important to add criticality. In other words, understanding asset health is more important for critical assets than for non-critical assets. A workable model for asset health is:

  1. Technical condition: where is the asset on the degradation curve and to what extent can the asset deliver the requested performance?
  2. Performance: How certain is it that the asset can deliver its performance? What are availability and reliability?
  3. Compliance: To what extent are internal and external regulations met?
  4. Life cycle: In what stage of the life cycle does the asset reside? Are spare parts still available? Is lifetime extension required? How does the asset family (similar assets) perform?
  5. Asset criticality: What is the effect of asset failure? It is important to minimize the risk of occurrence for assets with a large effect of the failure? For this reason, understanding asset health is crucial.

2. Smart use of data

As the above figure of the asset knowledge triangle shows, we can make the triangle larger by making better use of available data to better understand our assets (Reliability Engineering). As far as the data is concerned, we often see that not all available data is used. Typical systems managing asset data include maintenance management systems (CMMS ,EAM), and systems used by other departments (ERP, PLC, Historian, alarm databases, GIS, planning, etc.). Modern technologies enable us to bring all data from those systems together on a platform; creating a comprehensive overview. Our main tip is to focus on the foundation first before taking a next step; keep your data organized as data accuracy and completeness are essential.

3. Work together leads to results

While it sounds easy to collect and clean up data, it is not an easy process. But how do we effectively collect data?

  • Work together: All asset-related departments should play their part. Asset Management is the connecting discipline and an Asset Manager should play this connecting role, as the visual shows.
  • Acknowledge that more and more decisions are ‘data-driven’. The paradox is that as more data is needed to gain insight into the assets, the more the data becomes a digital asset in and of itself which needs to be managed as well. Data also goes through a lifecycle from creation to archiving. Asset Information Management becomes a strategic business function; business operations depends more on data.
  • Set up a technology architecture (an asset management platform) where applications can run to provide insight into asset health.
  • Ideally, digital and physical asset management is represented at board-level to emphasize the strategic importance. This also makes it necessary to make success measurables (KPIs).
  • Create a visualized overview, e.g. a dashboard. The dashboard metrics are not an end in themselves; they support processes and decision making. Think of further examining the asset condition and performance in consultation with colleagues or providing input to long-term asset planning. Such a dashboard works optimally when it is sufficiently embedded in the organizational processes.

4. Do a first assessment

No idea where to begin? Start by understanding your organization’s current situation. Collect information with this checklist:

  • Is asset knowledge decreasing? And is this a problem? In other words, what is your goal?

  • Collaboration: To what extent is there a cooperation between asset-related departments (Development, Projects (or Realization), Operations and Maintenance); is data and information exchanged?

  • Assets: Is there a sufficient overview of the life cycle of assets with respect to renovation or replacement? Which value or parameter could offer more insight into asset health?

  • Asset criticality: Where are the biggest problems?

  • Data: Which departments provide specific data? And which systems contain data and what is the quality of the data? What is the current status of the assets based on the above-mentioned five dimensions of asset health?

Next step: proof of concept

Once you have a good overview, it is time for a Proof of Concept (PoC). During a PoC, you have the opportunity to demonstrate on a small scale that a better understanding of asset health contributes to the goals of your organization. An important side effect of a PoC is the promotion of collaboration and the uncovering of possible knowledge gaps in your organization. Important tips for a successful Proof of Concept:

  • Define quantifiable success factors.
  • Start with the low-hanging fruit for quick wins, this helps create and maintain support.
  • Visualize asset health: for whom? Every time, the added value must be clear.
  • Formulate a vision on data ownership: internal/ external? In the cloud or locally?
  • Appoint a Product Owner as a representative for future users.
  • Identify and build up the necessary competencies.
  • Fit asset health in maintenance processes or portfolio planning.
  • Which technology platform do you use for collecting data?
  • Make conscious selections in algorithms to gain insight: when to choose Machine Learning or when to choose conventional Reliability Engineering?
  • Dare to let go of conventional maintenance (and rely more on technology). Think about how you take the involved (stakeholders) along in this process.
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