Managing Asset Information for Value – The conversation before CMMS, EAM and IoT!

Part 2: The Importance of Information to Decision-Making

Series Overview:

This four-part blog series is intended to cover the principles and best practices around the management of asset information. 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 logically advance through creating a management system for asset information.

The four parts in the blog series are as follows:

  1. Part 1 – Asset information and sources
  2. Part 2 – The Importance of asset information to decision-making
  3. Part 3 – Managing Asset Information
  4. Part 4 – Summary & Conclusions

Part 2 – The Importance of asset information to decision-making

Part 2 of this blog follows on from Part 1, which defined asset information and linked it to common life cycle phases. Part 2 will focus on common information pitfalls and organizations can avoid them by deliberately integrating asset information into the asset management value chain.

The Grim Reality of Asset Information and Decision-making in Industry

A recent report by Protiviti and North Carolina State University’s ERM Initiative (2019), identified the top 10 risks that companies should expect to face this year. Of the 10, four are directly related to asset information management. These are cyber threats, rapid speed of disruptive technologies, analytics and big data and the ability of legacy IT infrastructure to compete with born-digital firms. Findings like these have created an urgency for proper management of asset information. Zooming in on industrial organizations, these four risk factors are created through these common activities:

  1. Uncoordinated and misaligned collection and analysis of data that is not linked to specific asset management objectives and decision-making. This makes everyone tired, particularly those that collect it and destroys buy-in for the real data need.
  2. Not investing time and effort to standardize information systems, and processes to manage asset data. By not developing internal standards for data from field devices, those who acquire assets are left to make selections in a vacuum. This results in assets being bought without standardized sensor types and output data, data collection devices and monitoring and analysis software. After all, how many times have we had to call the instrumentation department during installation, in a rush, because we did not realize that the new ultrasonic level sensor did not come with the required air temperature sensor to compensate for temperature variations? In some instances, this deficiency is noted on installation and corrected. In the worst cases, the deficiency is not discovered for years and the pump operates on erroneous level information, leading perhaps to manual work arounds or even catastrophic failures.
  3. Allowing each department and group to manage asset information in silos. This is called consideration for the piece and not the whole. This gap frequently occurs between maintenance work systems and project management systems. Usually, project management processes to acquire assets do not benefit from or incorporate asset work and failure history. This can be due to tribalism or a genuine lack of quality information to accurately determine risks and life cycle costs in order to select the most appropriate asset. Either way, it sets the stage for conflicts between engineering (who need to make the selection) and operations/maintenance (who know the failure and work history), over which asset is the better choice. In this case, the lack of accurate asset information could lead to inappropriate asset or device selection and future expensive field modifications. Sometimes, even where asset history is available, it sits in silos instead of providing value another department. For example, it is not uncommon to see companies in which condition monitoring data is only known to the condition monitoring technician. This situation is amplified when condition monitoring is outsourced.
  4. Lack of ownership and governance over data. Asset information contains configuration, cost, history and organizational information that is often allowed to change without proper change management information. This applies even to history not being updated. Think about all the times spent rummaging through Stores for parts that were taken out and never replenished because no pick list was created to issue the item from the system.
  5. Yet another cause of poor decision-making, is the lack of automated data collection and analysis. This stems from the expectation that someone will consistently go to the sensor or the panel in the field to take the readings and then enter it into another system. This is a mistake as data collection and analysis will be viewed as non-value adding and will be sacrificed at the first opportunity. Not only do companies not have the resources to devote to manual collection and analysis of data, it is a huge source of risk for organizations.

Asset Information is the Basis for Asset Management Decision-making

The assets that an organization owns (or has custody of) exist only to provide value. It is the operational use of the asset that creates value. As the asset progresses through its life cycle, from acquisition to retirement, its value generating capabilities change. Managing this value is therefore a continuous process that must be more than a mere transactional event.

Organizational resources are limited and as such must be carefully prioritized. In order to prioritize resources, decision-makers in organizations are constantly balancing asset performance against risks and the cost to both deliver performance and mitigate the risks. This balancing act is called asset management decision-making. In this way, we see that the asset’s life cycle is in fact a series of asset management decisions. Value is therefore created and preserved through optimized decision-making and decision-making is achieved through analysis of asset information (Figure 1.0).

Figure 1.0 – How Asset Information Creates & Preserves Stakeholder Value

The pursuit of value therefore makes decision-making the single most important asset management activity and the most persistent over the asset’s lifecycle, beginning before the asset is created and continuing even after the operational use has ended. In fact, deriving value or lack of value from assets is a direct result of how optimized asset management decisions were made over the asset’s lifecycle. Let’s apply this value chain to the operate and maintain phase of a pump’s life cycle and imagine what stakeholder value could look like. In this phase, operations want uptime (availability), throughput (rate), compliant product quality and reliable asset performance. Top management wants environmentally and socially responsible production, least cost of operating and occupational safety assurance. Value is only as good as the decisions that the stakeholders make. Each participant along the value chain must contribute to value generation and preservation. This means that both maintenance and operations need to make decisions that will ultimately result in the stakeholder value. Maintenance’s contribution, for example, would include, optimal maintenance costs, components in good working order, and efficient maintenance processes through decisions such as acceptable deterioration levels, PMs tasks and intervals, asset prioritization for break in work, critical spares and training investments.

Asset Information Analysis

Performance and failure analysis are by far the most common form of analysis that would be expected on a common asset such as a pump, during the operate and maintain life cycle phase. These forms of analysis are also well suited to being supported by IIoT. To make optimized decision, the organization needs useful asset information and reliable information analysis capability (Figure 2.0).

Figure 2.0 – Connection Between Asset Information, Information Analysis Capability and Asset Management Decision

Useful information is information that is fit for purpose (aligns with asset management objectives and decision-making), meets the requirements of information standards, can be triangulated with other data sources, digitized (paper-based information does not facilitate easy analysis), and of measurable quality. Asset information analysis is the process of evaluating information in order to identify cause and effect relationships, and in so doing providing basis for problem solving and decision-making.

Think back to the activities identified previously as contributing to risk factors. Randomly collecting and analyzing data that is not aligned to asset management objectives and decision-making were identified as two of the activities that contribute to the risk factors. By implementing asset information strategy, including digitalization strategy and appropriate information standards, companies will ensure that information is useful and will be effective for decision-making.

Impact of IIoT on Asset Information Analysis

Another pitfall identified is the reliance on people to analyse the vast amounts of asset information. By our very nature, human beings are inconsistent, a factor which is represented in our decisions. In addition, as identified in the Protiviti’s 2019 risk report, rapid technology changes are the order of the day. This means that the assets themselves are becoming more complex, with more sources of data. Manual collection, monitoring and analysis of the vast amount of data being produced is no longer a feasible option for staff who must also manage the other affairs of the organization.

Augmented Intelligence (AI), machine learning, Augmented Reality (AR), and Industrial Internet of Things (IIoT) have significantly improved the analysis capabilities of CMMS’, Process Control Systems (PCS) and other asset information systems. AI for example, can monitor the data from our fictitious pump, look for relationships and variations to predict failures, identify when a machine is running outside of normal operating parameters and generate a call to action via notifications, alarms or work requests. With this support, no longer is our fictitious pump merely a device to convey product. Operators and maintainers would expect the pump to be equipped with decision-making capabilities of its own to optimize its operation and protect itself from catastrophic failure and relieve them of monitoring, analysis and decision-making responsibilities. As a further step, effective supply chain partnership could also allow some types of asset information such as run hours and operational parameters to be monitored by the OEM so that they can provide input into overhaul planning, spare part requirements and failure analysis.

Whilst not new technology, AR has also opened doors to improve competence management, where real-life operational scenarios can be simulated from actual asset information to train operators and maintainers.

Are we ready to re-evaluate organizational roles to complement the emerging AI, machine learning, AR, and IIoT roles in a meaningful way?

Join us in Part 3, where I will look at what asset information and digitalization strategies, information standards and data management that would be useful to organizations looking to better manage their asset information.

References:

Protiviti and North Carolina State University’s ERM Initiative. Executive Perspectives on Top Risks 2019. Retrieved from: https://www.protiviti.com/CA-en/insights/protiviti-top-risks-survey.

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. Suzane is a member of MC/ISO/TC 251 – Asset Management, Canada’s mirror committee for ISO 5500x.

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