The Impact of Poor EAM and CMMS Data Quality on Engineering & Planning

Asset-intensive organizations rely heavily on their Engineering and Planning teams to help facilitate large project roll-outs. These groups must capture, organize, and manage a vast amount of maintenance, engineering, and design data for the assets involved in the projects. They capture data for their assets, such as operating specifications, EAM attributes, engineering documents, recommended spare parts list, parts catalogs, maintenance guides, recommended maintenance procedures, and more. Ensuring that the data meet the requirements of the organization is a huge task that can be virtually unmanageable without tools to simplify and aid the process.

Capital Projects and Information Handover

A capital build project for an asset-intensive business typically requires a long time to complete, with many groups contributing data, including vendors, suppliers, and maintenance planners. Data comes in many formats: from books, files, spreadsheets, and databases. Asset-intensive organizations may input data for hundreds of thousands of assets from spreadsheets into an EAM/CMMS system such as IBM Maximo, SAP PM, Oracle eAM, Infor EAM, and others as part of a capital build. CMMS data management of these spreadsheets manually can result in duplication of data, data being overwritten or omitted, and so on. Yet, it is this asset and maintenance data upon which corporate maintenance strategies, maintenance personnel planning, and spare parts inventories are based. If the data is not accurate or complete, neither are any of the resulting plans and strategies.

Poor data quality impact on business strategy:

  • An inability to correctly staff a project with maintenance technicians
  • An inability to optimize spare parts inventories
  • An inability to estimate the actual time to complete the “data build” project
  • An inability to plan for full production as early as possible
  • An inability to trust the reliability, asset performance, or safety guards in place during early operations


New EAM Implementations

A new EAM implementation experiences the same issues with incomplete and inaccurate data as a capital build project. Organizations may attempt to move their existing data into a new EAM system such as IBM Maximo, SAP PM, Oracle eAM, Infor EAM and others, and fail to sanitize the data as part of the process. At the same time, they may be loading data for new assets. Without the ability to visualize the data, they may create duplicate assets, overwrite assets, and compromise other important business goals in the process. They also find it very difficult to assess the completeness of their data.

Challenges in implementing a new EAM system:

  • Poor performance of the new EAM system
  • Sub-optimal maintenance performance
  • Increased time required to complete the implementation
  • An inability to correctly update staffing
  • An inability to optimize spare parts inventories
  • An inability to ensure assets can be operated safely and effectively
  • An inability to estimate the actual time to complete the project


Operational Changes to Assets

Assets in an asset-intensive organization are seldom static. New assets are added, assets are decommissioned and retired, or moved to a new location. Assets are upgraded, engineering changes are made, and parts are replaced.

Asset modification is complex, requiring a high degree of coordination between various activities, including project design, cost estimates, procurement, project fulfillment, and documentation. The need to continue to support ongoing production capacity while changing, all the while maintaining safety and cost control, can be extremely challenging. Information produced from many sources must be available, at the same time as data is changing.

If operational changes are not well documented, issues such as these can result:

  • Even greater degradation of the data over time
  • Major cost overruns
  • Unplanned production outages
  • A high level of frustration for project team members


Standardization Across Facilities

Today’s multinationals operate similar facilities in many different locations and countries. But to make the most of this scale they need to standardize their engineering and planning across multiple facilities.

Inconsistent organization of asset data across facilities makes it difficult to:

  • Reuse engineering and designs globally
  • Track which versions of asset designs are deployed at different facilities
  • Roll out design and engineering improvements on a global scale efficiently
  • Analyze the performance of different designs across the enterprise efficiently
  • Roll out headquarters driven operational excellence initiatives across the organization
  • Roll out strategic corporate initiatives like the industrial internet-of-things (IIoT) efficiently
  • Leverage big data across the enterprise to improve engineering, design and operations