February 4, 2018

Equipment Reliability

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It’s All About the Bottom Line

ISO 14224 helps asset-intensive industries improve equipment availability and minimize hazards with high-quality equipment reliability data. High-quality, structured data give companies better insight into equipment reliability and performance and thus enable data-driven decision making. Data-driven decision-making will help your enterprise optimize profitability, safety and compliance and thereby improve your corporate bottom line.

Data quality starts at inception, as part of native asset and work management processes within SAP corporate software. Key elements of high-quality data are technical hierarchy, malfunction reporting, consequence accounting, preventive/inspections condition reporting, data quality assurance, and data aggregation. For optimal results, these elements should be consistent with ISO 14224 normative requirements.

With high-quality structured data, you can use standard SAP reports to generate corporate-wide equipment reliability metrics. If you can’t analyze 1000+ things at once, you’re not doing it right!

What is the Equipment Reliability Data Workshop?

The Equipment Reliability Workshop will teach you how to apply ISO 14224 methods in SAP enterprise software. You will receive pragmatic instruction on how to represent ISO 14224 content in SAP and how to apply ISO 14224 methods for technical structuring, malfunction data collection, consequence accounting, data aggregation, and generation of corporate-wide equipment reliability metrics.

Workshop Content

The workshop covers functional business requirements, associated system customization (configuration and enhancements), and business processes. Workshop delivery is via instructional slides and hands-on exercises.

  • Introduction to ISO 14224
  • Technical hierarchy: purpose, design considerations, master data objects that comprise it, and our recommendations for your enterprise
  • Master data objects: functional locations, equipment, classification, catalogs, and DMS, and measurement points
  • Work processing: logical segmentation of work into discrete processes and data sets; general maintenance, malfunction reporting, and preventive maintenance
  • Equipment reliability data quality assurance and consequence assessment
  • Equipment reliability data aggregation and metrics
  • Equipment taxonomy definition development



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