The number one challenge for CEOs of industrial organizations is quantifying risk. While more connected devices produce more data than ever before, measuring the right data and managing the massive volume of system and human outputs remains a critical obstacle to calculating risk.
According to Deloitte’s Global Risk Management Survey, data integrity is one of the most common “new” risks plaguing organizations today, with two-thirds of respondents believing their organization performed poorly in this regard. Additionally, only about 25 percent of respondents considered their institution to be effective in any aspect of risk data strategy and management, including data governance and data standards.
A single industrial organization may oversee thousands of assets, each producing continuous data streams and alarms. Poor data quality and alarm fatigue can undermine the benefit of more information and digital operations; but, when leveraged appropriately and given the proper context, this data can be used to execute intelligent asset strategies. An intelligent asset strategy results from an understanding of risk, unified data collection, predictive analytics, and machine learning to help executives and reliability engineers alike make the right decisions for asset and operational performance.
The concept of intelligent asset strategies is not new, despite the relatively recent onset of the Industrial Internet of Things (IIoT). With the expansion of industrial enterprise software solutions and continued use of buzzwords such as “big data,” “predictive analytics,” and “Internet of Things,” it is easy to assume that intelligent asset strategies and asset performance management (APM) are recent trends. However, industrial organizations have had processes and capabilities in place to manage risk and monitor asset behavior for decades through constructs such as engineering design, maintenance plans, and condition and process monitoring. The expansion of the IIoT and all the technology that supports it is not inspiring intelligent asset strategies; rather, this expansion is helping to improve and advance those strategies, allowing organizations to solve more complex problems more effectively and efficiently.
Defining intelligent asset strategies
A core component of a digitally driven, intelligent asset strategy is the ability to apply risk-based principles to the management philosophy of an asset, and then create dynamic management through the integration of operational data and analytics to detect emerging threats. In other words, by creating a real-time view of how people, systems and physical assets are performing, you can more accurately identify potential emerging threats and take prescribed action to therefore minimize safety and environmental risks in addition to ensuring high asset utilization at an optimal cost.
When developing an intelligent asset strategy, organizations should be able to answer three key questions:
- What are my high-risk assets, and what is the potential impact when a failure occurs? This question seems easier to answer than it is because it is multifaceted in many ways. First, the business needs to agree how to define high risk (i.e., what is your threshold for what constitutes medium versus high risk) and how do you in turn measure that risk – for instance, lost revenue, amount of downtime, or something else? From there, how do you accurately measure the impact of a potential failure, and how likely is that failure to occur?
- What is my strategy to mitigate the risk associated with a failure in an optimized manner? This question is truly the meat of an asset strategy. An organization must first have a process in place to identify risk, and from there develop a methodology that outlines how to reduce those identified risks and ultimately prevent failure. What can make this strategy development so difficult is that there are multiple levels of risk, and within each level there exists several different types of potential asset or system failures, each of which may require a unique approach to addressing the risk.
- How do I know when threats emerge that can impact the asset strategy, and what actions should be taken? Developing an intelligent asset strategy is not a one-and-done exercise; rather, an asset strategy needs to continuously monitor changes in the operations, conditions and performance of the asset to drive an asset-centric and smarter approach to risk management.
Developing an intelligent asset strategy
Ultimately implementing an intelligent asset strategy becomes complex quickly when there are multiple layers to consider – from individual pieces of equipment up to fleets of assets – and a broad range of stakeholders to consult with and ensure buy-in. To that end, what follows are clear steps to help guide organizations as they look to develop intelligent asset strategies.
1. Align asset performance management (APM) program with business objectives.
Depending on the industry, current business conditions, regulatory environment and corporate goals, organizations can have varied requirements for their asset and risk management programs. Organizations in a sold-out product scenario may be interested in maximizing production with no concerns regarding costs. Fluctuations in market pricing may create the need to reduce operational costs. New regulations may drive increased compliance requirements. In some cases, throughput, costs and compliance may all need to be balanced together. Defining the overall asset management goals and mapping them to specific business objectives can help you define a prioritized multigenerational plan to focus and prioritize your efforts and resources.
For instance, a major utility company aimed to improve availability by 30 percent, while simultaneously lowering average costs by 30 percent. With these goals in mind, management needed to identify where the production losses were occurring due to unreliable equipment through statistical analysis of historical data and eliminate the systemic issues that were driving the equipment downtime. To address the cost concern, the company worked to remove unnecessary time-based maintenance activities by leveraging online monitoring/diagnostics, operator rounds and asset health management to direct maintenance activity at the time necessary, based upon the equipment condition. By focusing on the key business goals, the company was able to implement two key asset performance management techniques, deep analysis of historical performance and corrective action, as well as condition-based maintenance to increase availability while also lowering average costs.
2. Assess risk and prioritize asset focus.
With potentially thousands of assets to manage, it is nearly impossible to closely monitor each individually, nor is it necessary. Rather, organizations need to understand what the criticality of an asset is in terms of the probability of failure and the potential cost or consequence of that failure. Whether driven by a particular facility, asset type, manufacturer, industry regulation or even a specific asset, risk is a key element of developing an intelligent asset strategy. Asset criticality analysis offers a way to rank the risk associated with each asset, allowing organizations to more accurately prioritize efforts and resources to higher risk assets. By understanding the level and type of potential risk across safety, environmental and production dimensions an organization can confidently tailor their mitigation plan to ensure the proper emphasis is with the most critical assets. Not all assets pose the same risk to your business and utilizing criticality analysis techniques allows you to move away from a one-size-fits-all model, which can dilute focus.
For instance, a piping segment in a process manufacturing facility handling water could be considered low in safety consequence in a loss of containment event due to its nature of being non-toxic and non-flammable, however, if that segment is supplying water to a core secondary process critical to production it could be considered high in production consequence. Alternatively, another piping segment could be high in safety consequence if it contains a toxic or flammable liquid, but low in production consequence as it could only be supplying a storage tank. Although the type of equipment in this simple scenario is identical, how risk is mitigated in each case may be different based upon the knowledge captured in the criticality analysis.
3. Implement asset strategies to mitigate risk.
After assessing the relative risk of assets, the next step is to develop a strategy for mitigating risk in a way that ensures the asset delivers to the engineered objective in an optimal approach. Organizations want to avoid performing maintenance that does not have a risk-mitigation benefit as this will unnecessarily increase costs, nor do they want high-risk failure modes to not be fully addressed through various maintenance, inspection and monitoring activities. Providing asset managers with key tools to balance risks and costs allows organizations to minimize downtime, optimize costs and ensure regulatory compliance in a structured, scalable manner.
For example, an international chemicals and energy company conducted asset criticality analyses to define the criticality for each system and related asset with respect to safety, environment, operations and financial risk consequence categories. This provided prioritization based on risk ranks to help the organization effectively manage and utilize resources on the most critical assets and systems. With the highly critical assets, company engineers conducted “what-if” scenarios of potential asset strategy options taking into account the predominant failure modes that exist for the asset, the potential consequences if the failure occurred and the appropriate activities to prevent those failures from occurring. By using a quantitative, “what-if” approach to assess the most effective activities to mitigating risk and the cost profiles to execute, the company was able to quantify and optimize the program. With the model, the company could understand the cost/risk profiles of a “minimize-risk” strategy, “minimize-cost” strategy, and many other scenarios across the risk/cost spectrum. From this process, the reliability teams were able to develop an optimized approach that best conformed to their risk/cost tolerances in a very structured, documented manner.
4. Monitor, detect and manage emerging threats, and prevent failures.
Assets in industry are complex and dynamic. Risk considerations are constantly changing, including a transitional workforce, aging assets and shifting operational and market conditions, which can have swift and significant impacts on risk curves and some situations can be “hidden” from standard maintenance and inspection methods at fixed intervals.
For instance, there are situations in petrochemical facilities in which changes in operational conditions – increased temperatures, changes in chemical composition of fluids – where a relatively “stable” damage mechanism, usually identified through regulatory, time-based inspection activities, can emerge rapidly and not be detected early enough to prevent undesired events. In these high-consequence situations, it is important to monitor the operational conditions that impact the failure potential in near real time to ensure the risk is addressed immediately through preventative measures. The key to a fully intelligent asset strategy is to connect the high-consequence failure modes with key sensing, monitoring and diagnostic techniques to detect emerging threats with as much lead time as possible to ensure an unplanned event can be prevented, thus reducing impact.
5. Create APM governance and visibility.
The last and final step in implementing an intelligent asset strategy is ensuring complete transparency across the organization in terms of what risks exist across assets, how the risks are being managed and what threats are emerging that may alter those risks and shift the strategy. An APM program can do the heavy lifting, providing industry-standard metrics, health indicators and dashboards for management to review and analyze.
Having effective strategies in place with a digital dashboard to back them up will enable industrial organizations to quickly and accurately assess the state of assets across the organization and quantify risk. Big data can easily overwhelm, but intelligent technology and asset strategies reduce the burden of maintaining data integrity and governance for industrial operators.
Joe Nichols is chief operating officer, VP Products, Asset Performance Management, GE Digital. He is responsible for building strategic partnerships worldwide with a focus on ensuring customers receive the most complete solution to optimize the performance of their assets. Nichols joined Meridium Inc. (acquired by GE Digital in 2016) in 1995 and brings extensive knowledge in the development and implementation of asset performance management methodologies and software in large enterprises through his various roles in product strategy, sales, consulting, marketing and training.