At HubHead, we work with some of the largest companies in asset intensive industries to help them build and sustain high-quality Maintenance and Asset data. Our customers share similar processes and face similar challenges. In our experience, the foundational Maintenance and Asset data that our customers rely on can be grouped into three primary categories:
“Asset Hierarchy”, “Spares and Materials” and “Maintenance Schedules and Tasks”.
In the context of a capital project or a remediation exercise, we typically start with the asset hierarchy. The asset hierarchy, comprised of locations and equipment, is the prerequisite for criticality analysis, spare parts identification, and Reliability Centered Maintenance (RCM). Assets are typically classified based on corporate standards or variations of ISO14224 and ranked by criticality. Classification helps determine the domain expertise required for maintenance planning/execution and criticality drives priority and resource availability. The locations and equipment must also be captured with the proper attributes and characteristics that help determine cost allocation, work allocation, and overall maintenance strategy. Enforcing a standard nomenclature and descriptions for assets in the hierarchy sounds obvious, but it can be very difficult in practice without proper processes and tools. Asset information typically comes from engineering data sources including P&IDs, asset registries, and instrumentation systems. However, data from engineering systems is not created for maintenance purposes. A common challenge is normalizing the data and filling in the blanks so that it can be assembled into a hierarchy suitable for plant maintenance.
We take pride in helping our customers enforce business rules that drive consistency in building and sustaining Maintenance and Asset data. The rules are used to transform and normalize data from external sources and also to remediate and validate existing data.
Spares and Materials
Determining the types of spares and materials required for maintenance can be a challenging endeavor. The Manufacturer Recommended Spares Parts List (RSPL) often serves as a starting point. A big challenge in the industry revolves around understanding the correlation between the RSPL and materials in the warehouse. If the correlation is not properly understood, the organization may end up with duplication of spares and materials and incur unnecessary holding costs. NRX AssetHub’s Visual Asset and Parts Management solution provides intelligent SKU matching functionality, which presents suggestions for matches between the RSPL and the material master based on common properties such as manufacturer, description and part number. Our technology also allows our customers to visually identify parts for work orders, services requests, and purchase requisitions.
Maintenance Schedules and Tasks
Similar to spares and materials, manufacturer recommendations often serve as a starting point. Experienced maintenance planners will optimize maintenance strategies by tweaking the maintenance frequency and tasks. Traditionally, optimization is largely done based on historical evidence, and fine tuning maintenance procedures through observation over time. A newer camp of thought is centered upon Reliability Centered Maintenance (RCM). RCM provides a systematic way of determining the impacts and frequencies of asset failures. The impact and frequency help determine the criticality level of the asset and its risk mitigation strategy. Our solution serves as the glue between the engineering data and RCM methodologies. We aggregate data from engineering sources, providing the RCM practice a standard asset hierarchy. At the same time, we assemble criticality, maintenance schedules and tasks as master data in preparation for operation of the asset. For an organization with the desire to establish common maintenance tasks and materials based on equipment type and other variants, NRX AssetHub’s Maintenance Library serves as the vehicle to capture reusable information.
When it comes to building, repairing, or sustaining foundational Maintenance and Asset data, our primary suggestion to customers is to start by addressing the biggest pain points that will yield a tangible return on investment. In capital projects, it is important to establish clear data completion objectives at different stages of the asset life cycle. In a data cleanup exercise, it is important to identify gaps in the data and prioritize remediation activities. Regardless of where we start, the end goal is to establish a standard data practice that gets new data right and keeps existing data right.
Alfred Yang | @Alfredhubhead
Vice-President, Global Customer Service
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