Technical Library

SAP Plant Maintenance is a powerful tool for facilities asset management.  With proper architecture and methods, companies can query hundreds of thousands of records simultaneously for high-quality information with which to make operational decisions.  Unfortunately issues associated with software selection, implementation, and usage greatly diminish the effectiveness of the new ERP tools, in many cases rendering those systems into electronic equivalents of manual filing systems.  Data merging and assessment remain a data mining exercise for many companies.  The quality of corporate decision-making suffers as a result, which in turn can lead to less than optimal operational performance, and safety and environmental incidents.  Causes of these issues include lack of standard data practices, lack of engineering science and practical experience in solution design.

Enterprise Asset Management in the processing industries requires a focus on safety, reliability, and maintainability of equipment. Maintenance information is used to define and evaluate compliance with equipment reliability and maintainability requirements. Quality information comes from quality, structured data. Logic to store and classify data associated with technical objects is necessary to make the data readily accessible, retrievable, and comparable between similar technical objects. Such logic, called taxonomy, is structured hierarchically and based on the characteristics of technical objects.  Taxonomy greatly improves maintenance analyses and decision-making by improving information quality and availability. ISO 14224, Collection and exchange of reliability and maintenance data for equipment, promotes a standardized approach to taxonomy that maximizes the utility of data and facilitates data exchange.  This presentation describes how to configure SAP PM in a manner that rigorously captures the principles of ISO 14224 for standardized data collection and structuring of reliability and maintenance data.

The purpose of an equipment criticality ranking (ECR) study is to identify and address potential risk associated with the operation of processing facilities.  Failure scenarios are identified, risk-ranked, and risk quantified with consideration given to safeguards that protect against the scenarios.  Equipment criticality rankings are a sum of risk from applicable scenarios.  Safeguards are identified and prioritized based on the amount of risk reduction they provide.

ECR study data facilitate safeguard analysis, a technique used to evaluate benefit-to-cost (BTC) ratios for risk reduction programs and to monitor safeguard effectiveness.  Safeguards should be maintained or implemented only where safeguard costs give commensurate returns in the way of reduced facility exposure.  An enterprise should reduce costs by using BTC analysis to identify and eliminate support programs with low BTC ratios.

Safeguard effectiveness is a measure of actual versus perceived risk reduction for safeguards credited with reducing risks.  Automated exception reporting can be used to monitor safeguard effectiveness and to notify personnel when intervention is required to intercept a potential loss before it happens

When information is organized and accessible, more people will use it and thus be better informed.  Better information means better decisions, which results in safer facilities and more efficient operations.

Here is a systematic approach to compliance and optimization of process capacity and quality.  The premise in prioritizing maintenance based on projected consequences of equipment failure.