Chat with us, powered by LiveChat

The Impact of Poor CMMS and EAM Data Quality on Maintenance Optimization

Maintenance technicians rely on automated work orders to plan and schedule maintenance. Some PMs are time-based while others are condition-based. Time and cost can be reduced by switching from time-based PMs to condition-based PMs. Work should occur on regular schedules, not too often but not too infrequently. 

Business Challenges

  • Reducing costs by eliminating unnecessary work
  • Replacing time-based PMs with condition-based maintenance
  • Minimizing the effort spent maintaining aging or obsolete assets
  • Transitioning maintenance programs when you acquire or divest assets
  • Implementing better maintenance strategies like Reliability Centered Maintenance (RCM), Safety Integrity Level (SIL), Risk Based Inspection (RBI) etc.
  • Consolidating PMs with overlapping activities generated from multiple different strategies or regulatory requirements
  • Implementing IIoT effectively

Planning and Maintenance Challenges:

Planning and maintenance (PM) data is incredibly complex, and many asset-intensive companies run into challenges in this area. PM data is vast, providing workers with the uncertainty of what to do with obsolete PMs; whether they can be deleted without causing issue. Some PMs are set up to schedule maintenance too frequently, which is time consuming for skilled workers and lowers uptime. Time-based PMs are often superseded by condition-based strategies. Within an asset hierarchy, PMs can be located at inconsistent hierarchy levels or at the wrong level all together.

With this much PM disorganization it becomes difficult to accurately measure resources such as manpower needed for work required. Work prioritization is inefficient, and managers struggle to measure and report on the work backlog or whether work is completed efficiently. Regulations add another layer to these issues, and disorganization makes it difficult to comply with mandatory work.

Data Management Challenges:

For asset intensive companies, optimizing maintenance without a productivity tool can be a nightmare.

SAP EAM incorporates a large number of maintenance strategies and with that, complex data structures that can overlap, combine, and override each other. These data structures can be mandatory or optional in order for the chosen maintenance strategy to run smoothly.

A Maintenance Strategy is composed of any maintenance activities required. It does not include detail on the activity, the object or the date. A strategy can be either time-based, meaning that the maintenance is scheduled based on how much time has passed, or performance based, which means that maintenance is scheduled based on equipment usage.

A Maintenance Task List is necessary for strategy-based maintenance because it includes information on maintenance activities, materials required and specific deadlines. They may be object specific or neutral and are otherwise optional for other types of maintenance strategies. Maintenance Items include specific activities, objects, and organizational data. Maintenance Plans contain maintenance items and determine maintenance dates as well as a maintenance call object.

Deadline Monitoring is automatic and prepares batch jobs. This ensures that maintenance call objects are generated on their required due date.

These data structures are not only complex, they also overlap and have inter relationships. Some are compulsory and some are optional. A lot of the required data structures are dependent on the maintenance strategy chosen. If Preventive Maintenance is the strategy that is chosen, it will require several of the functions explained, and demands high-acceptance among users.

Within preventive maintenance strategy, there are many types of maintenance schedules. The most basic one being, a Single Cycle where maintenance is scheduled on regular intervals, with repetitive tasks. Maintenance Strategies get more complex when they are based upon multiple strategies combined, or where one strategy supersedes another. For example, when time-based and condition-based maintenance are both implemented and must combine smoothly to optimize maintenance schedules. This optimization will require a mandatory task list. A Multiple Counter Plan will determine the date based on how much time has passed, or perhaps the condition of the equipment. Here a maintenance task list is option but may be helpful.

When scheduling maintenance, it is important to be thorough so that repairs and work is done when necessary. To really Optimize Maintenance scheduling though, you need to make sure that it is not overscheduled. Too much maintenance can increase costs to a point where they are no longer beneficial to decreasing the cost of reactive maintenance such as repairs or unplanned downtime.

Since maintenance strategies and data structures can overlap, sometimes it is beneficial to implement a Maintenance Package Hierarchy. This prioritizes maintenance packages that are scheduled for the same day, so that work is prioritized, and unnecessary maintenance can be avoided. If maintenance plans are Performance-Based, scheduling will require constant input and supervision. Time-based plans have automatic scheduling and so whenever possible, they should be prioritized over performance-based maintenance.

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.

Within the Master PM Application users can create, edit, and view master preventive maintenance records. This is where planners can access Master PMs, used as templates that govern any associated PMs. These will automatically generate preventive maintenance schedules which will determine how often work orders need to be generated. Work Orders can be time-based, condition-based, or both. Job Plans describe the tasks to be completed including the length of the job and the type of work required (mechanical/electrical, etc.).

The Preventive Maintenance Application allows users to create and manage preventive maintenance plans for assets and locations. PMs and PM records are created here that will generate scheduled preventive maintenance work orders. The work orders are assigned based on job plans tasks assigned to PMs with the intent to provide a detailed description of work to be done. The PM specifies the preventive maintenance schedule for asset or location in order to generate work orders from PMs on a planned and regular schedule (time or condition based or both).

For useful and smooth scheduling, these PMs need to overlap, and therefore Master PM Relationships will specify the relationship between two preventive maintenance records. For example, the completion of the primary PM may activate, complete, or deactivate its related PM. PM Relationship Updates allow for creation or updates on Master PM Relationships based on the current master PM. Changes will update the associated PMs without changing the PM master records.

A PM Hierarchy exists to aid in the scheduling of a group of related work orders for an asset or location. These Related PMs will allow for automatic updates in the status of future work orders when related PM work is completed. For example, the completion of maintenance work can activate another PM, can trigger a change in the maintenance phase, or can initiate claiming related maintenance events. PM History provides a view of all past scheduled work orders.

Historical failure data will compile over time and can help to generate the next predicted failure on an asset. The Next predicted failure date can be displayed using query attributes in the Reliability Engineering Work Centre.

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. For example, when one PM is assigned to multiple assets, one must consider how each change to the PM could affect the asset data. Historical information may not be necessary on a day to day basis, but it is important to preserve. This is complicated because the data may no longer be accurate but needs to remain for records.

Challenges Associated with Conventional Approaches

Conventional approaches to optimize preventive maintenance are not without flaws. Making changes to a live production system is extremely risky. A large amount of changes may only make sense if implemented at the same time. Therefore, it would be easier to make changes in a staging platform and then update the live system. On the other hand, uploading changes to the EAM is difficult too because the data must comply to the EAM standardization.

Each change made within the EAM is time-consuming to complete. This is even more tedious and time-consuming when first making the change in a test system and then in production. However, without a full test EAM environment, it is difficult to validate the data. All of these edits are at risk for human error. While some try to compensate with using spreadsheets, they can’t model complex data relationships effectively. Spreadsheets provide poor visualization and they do not support complex relational data.

Real World Examples:

Data Issue:

  • The company had a PM optimization program implemented that required the SAP Strategy to be changed for ALL maintenance plans and task lists.
  • This was due to over the years of using the PM module in SAP, the company had created many Maintenance strategies due to one strategy not having either the correct time package or time unit (DAYS, MONTHS, YEARS, etc.) available.
  • They decided to incorporate ALL strategies into one using a common time UOM and having ALL possible packages available.

Why can’t this be solved in spreadsheets or directly in SAP?

  • As the data model for plant maintenance objects in SAP are very complex it is very time consuming and prone to errors.
  • The process to change a strategy on a maintenance plan and task list is very labor intensive involving the following steps:
    • Remove all strategy packages from task list operations
    • Remove task list from maintenance item
    • Flag maintenance plan for deletion
    • Change the strategy on the task list
    • Re-assign the new correct strategy package to the operations
    • Create new maintenance plan and item and associate the old task list
  • For the company to ensure all the above steps are performed correctly in excel or SAP it would have taken over 12 months of man time
  • This was completed in AssetHub in around 2 months with ~90% of the work being automated and the only involvement of the business was for sign off.

How AssetHub helped change the data and load it into SAP:

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

Data loaded into SAP Production:

  • For this project HubHead and the company were able to successfully change 767 plans, 2034 maintenance items and 2034 task lists with 30,878 operations.
  • 767 new maintenance plans and 2034 new maintenance items were created.

Data Issue:

  • The company had a PM optimization program implemented to standardize the level in the hierarchy that preventative maintenance is performed at
  • 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

Why can’t this be solved in spreadsheets or directly in Maximo?

  • As the data model for maintenance objects in Maximo is very complex it is very time consuming and error prone.
  • 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

The process to determine the level at which the maintenance is being performed is very labor intensive involving the following steps:

  • As you are unable in Maximo to visualize both the hierarchy and the associated PMs at the same time, the Asset/Location and PM data would need to be extracted into excel
  • Custom macros in Excel would have to be created to determine the following:
    • The level at which the PM is associated
    • If there are any other PMs within the same branch to perform the same or similar work.
    • Validate to ensure all critical assets have an appropriate PM associated

    For the company to ensure all the above steps are performed correctly in Excel or Maximo it would have taken over 12 months of man time. This was completed in AssetHub in around 2 months with ~90% of the work being automated and the only involvement of the business was for sign off.

    How AssetHub helped change the data and load it into Maximo?

    • 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