Asset management has been viewed over the years as a systematic process of developing, operating, maintaining, upgrading, and disposing of assets in the most cost-effective manner (including all costs, risks, and performance attributes).
According to ISO 55001 and ISO 55002, in order to successfully deliver and sustain a quality asset management system, in any industry, an asset management framework should be in place. The asset management framework should include the strategic asset management plan that defines the path for aligning organizational objectives with asset management objectives and the method of delivery and sustaining. The most important enabler to successfully implement a quality and sustained asset management framework throughout the entire lifecycle of any asset (physical and intangible assets) is a data management strategy.
According to Gartner, “Asset performance management (APM) entails the capabilities of data capture, integration, visualization and analytics tied together for the explicit purpose of improving the reliability and availability of any physical assets.”
The data management strategy can be described as the process an organization develops to create, use, store, manage and govern their asset data. The strategy should entail at minimum the governance, guidelines, procedure of the organizational business, maintenance/reliability data management activities and compliance. It should provide clarity on the creation and use of data, locations, criticality, regulatory requirements, data transfer, naming conventions, taxonomy principles and more. Also, it will serve as a baseline for the intended functional purposes of master data, transactional data, process data, monitoring data, and condition-based maintenance data such as oil analysis, vibration analysis, ultrasonic analysis, etc.
To ensure that the requirements of the strategy are fully implemented, a delivery structure must be in place to address the data fundamentals, including content, classification, and specifications, that impact the end-user interpretation of data and integration with a technology platform. Also, a quality management plan is needed to address data integrity, including data quality planning, control, assurance, and improvement, which impacts the organizational process to manage data, such as reliability and maintenance data.
Most organizations across different industries globally struggle to achieve quality asset data; spending millions of dollars every year in maintenance budgets and ad hoc data cleanup. These firefighting situations are a result of ineffective or non-existent asset management framework that is driven by a well-defined, fit for purpose data management strategy with a robust implementation plan.
Quality data is essential to any asset intensive organization and crucial to the success of that organization, especially in making informed business decisions. As organizations grow in size and complexity or the asset starts aging; it essentially affects the volume of data, the rate at which it’s generated and decision makings. The effect of these changes will require a systematic approach to manage data, guide change, and address deviations to contain the changes in the organizational or operating context.
The systematic approach starts with data collection and validation to establish the quality data building blocks, which will guarantee consistent and assured quality asset data throughout the asset lifecycle. The result of the systematic approach typically produces a quality Asset Register, which comprise a set of hierarchical (parent-child) relationships and descriptions that define:
• Functional locations
• Equipment
• Maintainable assemblies and components
• Parts/materials
• Failure modes
The asset register structure is the key reference point for all maintenance and reliability activities. It links all aspects of the numerous equipment items, maintainable assemblies, components and parts with management and technical information that is required to ensure safe and effective operation and maintenance within the organization. It also provides the basis for data collection, cost analysis, performance monitoring and continuous improvement at all levels of the organization.
A quick test or measure if your asset data meets quality requirements, is to confirm if the asset master data (asset register) is fully aligned with the asset drawings (as built, P&IDs, etc.), the physical asset on-site, and all required asset/equipment technical documentation (OEMs/etc.).
The big questions are:
Does your organization have an asset management system or process?
Do you have a well-structured asset management framework?
Do you have a well-defined, purposeful data management strategy with a robust implementation plan?
Do you have validated asset data?
Answering these big questions is no small feat. Any objectives that involve APM are going to require solutions that are comprehensive, unique, and easily integrated. If your answer to the above questions is no, or needs further thought, you are not alone. Many organizations are just beginning to take a fresh look at their data management strategy and asset management. GP Strategies can help you get started on your journey with APM OptimizeTM.
GP Strategies’ APM Optimize is designed to address these needs and more across multiple industries such as oil & gas; energy; automotive; pharmaceutical; metals; food, beverage, and consumer goods; manufacturing; and more.
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