This four-part blog series is intended to cover the principles and best practices around asset information management. The aim is to provide guidelines to organization who are seeking to incorporate IIoT in their asset management strategy or to acquire or optimize their CMMS or EAM. The series will be published in the order listed below so that readers will be able to logical advance through creating a management system for asset information.
The four parts in the blog series are as follows:
- Part 1 – Asset information and sources
- Part 2 – The Importance of asset information to decision-making
- Part 3 – Managing Asset Information
- Part 4 – Summary & Conclusions
Part 1 – Asset information and Life Cycle Phases
Asset information is the combined data and documents about the asset portfolio that is required to support asset management activities and related decision-making over the asset’s life cycle. Asset information is generated at all life cycle phases and begins even before the asset is acquired, with justifying the need for a new or replacement asset and the selection processes for the asset. Let’s take a familiar asset, a pump, and develop this idea using some examples of asset information that would typically be generated over its life cycle as outlined in the table (Blog – Asset Information Management – Part 1 – Table) attached.
As can be seen in the table, a significant amount of asset information is generated over the pump’s life cycle. Leading us to conclude that asset information is best managed in its digital form. In fact, in order to be useful for decision-making, the asset information should be stored in asset information systems, that are capable of complex analysis and decision-making.
Asset Information Sources
Asset information comes from several sources. Thinking again of the pump, some asset information comes from connected devices (IIoT), such as vibration analysis may originate from a connected vibration sensor. The machine protection instrumentation on the pump for example, is usually hardwired to its control system to make decisions to shut down or refuse to start it, without the need for human intervention. In this case, first line decisions are being made by the pump’s control system. However, because human intervention will be required after a failure event, some organizations opt to link local control panels to process control systems for speed of failure analysis. Asset master data for the pump would typically be a combination of asset data record (created by the organization in CMMS/EAM) and the nameplate specification data. Decisions such as those around parts and components do require human intervention as the first line decision-makers. In this case, if the specification data only exists locally on the motor nameplate, and not in a centralized system as the CMMS, it will be difficult for the planner to decide on which bearings to stock or what size flange to order. The process control system is yet another source of information that would contain run hours and other operational data. Information such as defect elimination analysis may exists as a management report on the organization’s document management system.
Impact of IIoT on Asset Information Sources
IIoT has opened up several possibilities as it relates to the sources of asset information. This includes increasing the number of data sources and increasing the quality of the data by introducing more quantitative sources such as infra-red cameras in preference to qualitative sources such as visual inspection. By employing IIoT, organizations will be able to move beyond a few connected sensors to full digital twinning of the asset, increasing information analysis capability for improved decision-making. Thinking of the pump again, the planner will be able to view the nameplate using augmented reality, while also viewing the parts catalog from an exploded view diagram, and simultaneously accessing the supplier’s contact information. Without changing software or screens, the planner would also be able to view PMs and job plans and using artificial intelligence, be advised when work should next be scheduled based on deteriorating vibration trends and benefit from automated critical spares recommendation based on usage, criticality, asset condition and supply chain capability.
Continue to Part 2 here.
About the Author
Suzane Greeman, ASQ-CMQ/OE, CAMA, CAMP, CMRP is author of the Risk-based Asset Criticality Assessment (R-b ACA©) Handbook. She is the Principal Asset Management Advisor of Greeman Asset Management Solutions Inc., a firm that specializes in asset management, maintenance and reliability advisory services and education. Suzane has over 21 years of experience across cement, power generation, and wastewater industries. Her areas of expertise include developing asset information management strategies and systems, developing and deploying asset management policy, developing asset management business processes, leading multi-functional project teams, managing capital and maintenance management programs and organizational capacity-building.