Last week we looked at an oil and gas company who use SAP as their EAM/CMMS solution. This week we will look at an IBM Maximo example from the Oil & Gas industry.

Preventive maintenance initiatives in Maximo are set up with the goal to keep assets running efficiently and to help manage a smooth maintenance schedule. A predictive maintenance strategy helps planners schedule regular work activities with the goal of reducing downtime. The work scheduled will include details for what labor, goods and services will be needed to complete the maintenance work.

Let’s look at our IBM Maximo example. The company had a PM optimization program implemented to standardize the level in the asset hierarchy that preventative maintenance is performed against.

What data issues did this oil and gas company have when using IBM Maximo?

  • The current state of their Maximo system had preventative maintenance being performed at all different levels of their hierarchy including some branches with multiple plans
  • One at the incorrect level and another created at the proper level without the initial plan being removed.
  • They decided to rework their PMs to ensure that all the maintenance is being performed at the correct level and decommission the PMs at the incorrect levels.
  • Without correcting the PM issue, it was impossible to have consistent accurate reporting for regulatory and operational purposes

These issues can’t be solved in spreadsheets or directly in Maximo because the data model for maintenance objects in Maximo are very complex, time consuming and prone to more errors. Without being able to visualize the hierarchy and the associated PMs in one place, it is hard to determine if the maintenance is being performed at the correct level or if any duplicate PMs are present.

How NRX AssetHub helped them?

  • Automation programs were used to make all the required changes in the correct order
  • At the end of each step the appropriate Maximo load file was created to ensure the data was being changed in Maximo properly
  • Data was first loaded into their Quality Maximo environment for testing and approval
  • Once all steps had been performed in Quality the steps were performed in the Production environment

Due to the complexity and the inter relationships within PM data, it is not easy to cleanse or fix PMs. Editing or creating PMs requires several complex, and tedious steps. To learn more about preventive maintenance optimization book a demo today or check out our website!

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