The Top Causes of Poor Quality EAM Data
With the rise of international competition and market globalization, enormous pressure has been placed on corporations to improve efficiency and reliability while reducing operating costs. As a result, asset-intensive organizations have invested billions of dollars worldwide in ERP, EAM, and CMMS systems such as IBM Maximo, SAP PM, Oracle eAM, Infor EAM, and others to meet their maintenance and reliability goals. Unfortunately, many companies load legacy data into the new system, failing to ensure that their data is complete and accurate.
Without an adequate data foundation, EAM systems will simply fail to deliver their expected value. It is not possible to be strategic and continuously improve maintenance operations without assurance that existing data is valid and fit for purpose. Poor maintenance strategies directly impact day-to-day operations and are usually a result of inaccurate or incomplete EAM data.
So, what are the top causes of poor quality data? Let’s take a closer look.
1. Starting Too Late
Most Maintenance Engineering teams begin the process of building Maintenance Master Data (MMD) too late.
- If the process of building MMD carries past start-up, the business is exposed to critical risks
- To achieve CMMS/EAM Data Readiness, Maintenance Engineering Teams are advised to begin the process of building MMD during the Feed stage of the Asset Lifecycle
2. Using the Wrong Tool
Traditional methods for building Maintenance Master Data are no longer working.
- Maintenance Engineering teams can no longer rely on complex sets of spreadsheets or homegrown software programs to build, enrich, validate, and handover MMD for new projects
- Maintenance Contractors (who are an essential part of any complex data build) are rarely able to provide Owner/Operators with a transparent view into their progress and are not able to re-purpose past work on similar projects
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