In the past few years, plant maintenance has become increasingly important due to business, economic and technological influences.

Preventive maintenance (PM) data is incredibly complex, and many asset-intensive companies run into challenges in this area. Asset intensive companies have large numbers of PM plans in their EAM/CMMS systems creating a serious data management challenge. Some PMs are set up to schedule maintenance too frequently resulting in unnecessary work but changing them all efficiently can be a challenge.

Additionally, time-based PMs are often not correctly superseded by condition-based strategies resulting in duplicated maintenance activities. 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.

Companies rely on automated work orders to plan and schedule maintenance. If PMs are disorganized or unoptimized then it makes it impossible to plan and schedule effectively.

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