Getting full value from your maintenance data

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Driving continuous improvement using maintenance data is an emerging opportunity to unlock additional value from integrity and condition management.

Large, multi-dimensional data sets can now be collated and analysed using business intelligence tools, illuminating opportunities to improve availability while reducing maintenance cost.

The potential return from monitoring the condition and integrity of production assets is accepted in industry. Operating companies willingly spend millions of dollars annually on condition monitoring and inspection. In return, they receive assurance that their equipment will function as intended and data that they can use to demonstrate compliance. 

The challenge facing operators is how to enable their people to maximise the value derived from these activities.

This is important because the value is often not as high as it could be. Condition data is rarely used to optimise future inspection cycles, which means that the full benefit of risk-based inspection planning is not realised. Recommendations to repair assets are not consistently acted on, so the activity of reviewing condition data does not deliver the desired restoration of function and performance.

Re-engineered business processes can enhance continuous improvement loops, reducing unnecessary maintenance over time. For example, an SVT study identified that bearing failures at a gas plant were caused by the same sub-optimal lubrication practice. Changing the lubrication practices resulted in avoidance of $2 million per annum in unplanned maintenance.

The challenges in achieving such improvements should not be underestimated. These can be technical in nature, for example finding cost-effective ways to clean and verify data, or find ways to automatically access data while meeting security and other corporate IT standards. 

Driving improvement may also necessitate training maintainers and operations in new work practices. We have helped schedulers to link work orders with damage mechanisms and failure modes in their Computerised Maintenance Management System (CMMS). This helped identify root causes of persistent failures across the plant, enabling the operator to take cost-effective action.

Closing the loop

Cyclical processes are a proven way to deliver continuous improvement. In asset performance management, inaction on recommendations is a common point of process failure. Such oversight can reappear down the track as non-compliances detected by regulators, breakdowns that stop production or reportable hydrocarbon releases.

Engineering work practices should include translation of recommendations to a work order in the company CMMS, triggering action.


Using data to optimise future monitoring

Optimising future inspection plans using actual plant degradation data is a basic principle of Risk Based Inspection. 

Companies that have an Inspection Data Management System (IDMS) in place will be able to identify opportunities to safely increase inspection intervals. Faster than predicted degradation rates can also flag that equipment needs to be inspected more regularly than planned. 

Identifying improvement opportunities

Even greater value can be obtained by mining maintenance data for improvement opportunities. 

In our experience, the issues that warrant action fall into two distinct subsets: multiple failures that have a low individual consequence, but could be solved with a single solution; and unique issues that have a high consequence. The latter are generally already known and mitigated.

The former are harder to identify, however the return is likely to be high. For example, deferring planned maintenance where the machine condition data shows that it is not needed often yields significant improvements in cost. This strategy can yield quick maintenance cost wins for balance-of-plant equipment.

Implementation of new or modified business processes are often necessary for organisations to realise the benefits of maintenance improvement. Deferring unnecessary planned maintenance, for example, would require a new condition based process that would interrupt the existing planned maintenance process. Key decision makers and doers have to be engaged with and supportive of the change.

Finding the right problems to focus on is also a challenge. This usually requires aggregating and interrogating disparate data sets that are not clearly connected and not readily accessible by one person. 

This is where business intelligence (BI) software can help. 

Designed to be intuitive to use, these tools place a useable ‘Single Version of the Truth’ in the hands of people who can interpret and act on it without imposing long hours of manual data handling every time. We see BI as a necessity for organisation’s that are seeking to consistently drive continuous improvement in asset management.

Effective engagement and process supported by BI are necessities for organisation’s seeking to consistently drive continuous improvement in asset management.

What to do

Companies seeking to get full value should:

  • Engage: make sure key people are prepared to help make improvements.
  • Close the loop: Ensure that recommended actions are correctly captured in their systems.
  • Optimise using data: Optimise inspection cycles using actual equipment degradation data.
  • Automate data visualisation: Implement dashboards driven by business intelligence tools that make it easy to identify ways to improve availability, risk and maintenance cost.