The Industry Standard Solution for Plant Maintenance (ISPM) is an engineering solution for asset management that has been tried, tested, and refined through application in industry. It is compliant with international and industry engineering data standards, consistent with good engineering practice, and built with leading data architecture practices. It is delivered as functionality and content within corporate ERP systems.
Technical hierarchy is the foundation for an equipment reliability and maintenance program. A good design represents corporate assets completely, logically, and consistently; provides a master data infrastructure to collect, merge, and analyze equipment reliability data; and defines facilities and equipment interrelationships in a manner that is both intuitive for users and can be interpreted by the system. A good technical hierarchy design is also easy to maintain.
Equipment failure reporting consists of data collection and data analysis steps:
Equipment Malfunction Reports are used to collect failure data from maintenance personnel
Data analysis includes data merging and assessment steps
Merging is the process of choosing a select dataset of failures events based on common parameters defined by the analyst
Assessment is generating failure statistics for the select dataset
Equipment taxonomy and master data provide the infrastructure for failure data collection, merging, and assessment
Multi-Level Failure Reporting
Multi-Level Failure Reporting (MLFR®) is used to aggregate all failure events within an equipment boundary to its parent equipment level.
When combined with ISPM reliability data collection and merging processes, standard SAP reporting is an effective tool for generating equipment failure metrics. The SAP Plant Maintenance Information System (PMIS) is a flexible tool for aggregating and analyzing equipment failure data. PMIS is summary information stored in data structures. For example, summary-level equipment failure data are stored in data structure S070 “Breakdown Statistics.” PMIS is part of SAP’s Logistics Information System (LIS), which is standard delivered with SAP software.
Risk management is used to ensure safe and productive facilities operation in asset-intensive industries. Activities include risk assessment and risk mitigation.
ISPM Risk Assessment
ISPM uses an SAP Risk Assessment notification for developing and documenting risk scenarios. The Risk Assessment notification is similar to the Malfunction Report and uses the same code sets, which is logical given a risk scenario is simply a hypothetical failure event.
With ISPM, all preventive maintenance and inspection (PM) routines are treated as safeguards used to mitigate risk. Each PM task is ascribed an incremental amount of risk reduction or safeguard priority rating for each equipment item upon which it is performed. Safeguard priority ratings are validated periodically against field inspection results and equipment reliability data for said equipment and the same equipment type in similar service.
Reliability Dynamics' ISPM is a proven rapid-deployment toolset for collect and utilizing high-quality equipment failure data. Typical System Integrator/Solutions do not support failure metrics at any level: corporate, regional, business unit, or single equipment item.
Corporate ERP systems are used to integrate equipment failure and maintenance data with many other corporate data repositories, including risk assessment data, materials management, engineering and construction, accounting, HR, and financials. 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. Failure data merging and assessment remain a data mining exercise for most asset-intensive 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 designs, and software selection being driven by software company sales targets versus manufacturing company requirements. Equipment failure data in particular suffer as a consequence of these issues.