4.00 Asset Information General

Artificial Intelligence (AI) technology is developing rapidly and is appearing more and more often in asset intensive organizations. It is both a Threat and Opportunity. To some it is a tool for making better or faster decisions; and on systems where the output is king that could be a driving factor. For others, it is another layer of fog hiding decision making processes. Evolution of this technology is moving much faster than regulation, and in an unregulated space within a capitalist economy, where AI can provide a commercial advantage it will be used and advanced. How is AI being used in asset intensive organizations today, what could tomorrow look like? What skills and knowledge should Asset Management Professionals be investing in to work in a world with fast evolving AI systems?

Asset management maximizes the value of physical assets while balancing cost, quality (levels of service), and risk. It is a complex and interdisciplinary task that requires strong leadership, multidisciplinary knowledge, and effective use of technology and resources. Asset management is especially challenging for public organizations that own a wide range of assets, such as roads, water, sanitary, stormwater infrastructure, facilities, parks, public art, information technology, and fleet. Furthermore, asset management is not merely a technical or a leadership role but a marrying of the whole organization. Hence, asset managers must have a holistic understanding of how their assets support the organization’s long-term goals and how to engage with various stakeholders. The presentation will explore skills, techniques and methodologies to help asset managers achieve better outcomes and meet their objectives. The approaches range from traditional methods to the latest techniques in industry and elsewhere. These include engineering, finance, project management, maintenance management, supply chain, process improvement, public engagement, design thinking, and journalism.

Information ontologies have been used to integrate information and clarifying the meaning of its contents in the biomedical domain for decades. More recently, the approach is seeing wider adoption in the financial services and industrial domain. In this presentation, we address three familiar problems commonly observed in all industrial sectors. The first is the undesirable state of having multiple sets of information about the same assets stored in independent silos. There are many popular solutions to this problem; we contend that they are fragile due to a second problem. The second problem is that asset records in different data sources (e.g., an engineering drawing repository, work management system, or SCADA database) representing the same asset are updated independently. This leads to inconsistencies between the data sources over time. The third problem is the most critical and perhaps the most intractable – the contents in the data contain pernicious ambiguities. As a result, we cannot find in the data the clear and definitive answers to guide asset management decisions. Ontologies, and their utility for disambiguation and semantic integration, are well suited to support these challenges of asset record management. We present an ontology for asset information integration currently being trialed at Toronto Water for the audience to assess.

You are an asset owner. What confidence should you have that those operating and maintaining your assets are capable and “doing a good job”? With organizations more and more commonly outsourcing their maintenance and operations, and even the management of assets, there must be assurance activities in place, assurance to provide confidence that third parties will deliver what is asked and that their approach is sustainable in the long term. A three-lines-of-assurance approach provides asset owners, operators, and maintainers with layers of audit to assess the effectiveness of risk and control functions. The third line assessment is aligned to the audit and risk function and is for executive or board committees. This presentation will explore case studies where an organization may have benefited or has benefited from third line assurance. The three lines of assurance can be characterized as follows: First Line—how operational managers own and manage their asset risk and maintain effective internal controls; Second Line—how management establishes compliance functions that facilitate and monitor operational practices; and Third Line—expected to provide executive management, audit and risk committees, and governing boards with a means of addressing their responsibilities and accountabilities for meeting their corporate objectives or regulatory requirements. The third line is expected to provide high-level independent assurance on organizational effectiveness. Assurance is a fundamental of asset management; it should provide confidence that assets will fulfil their required purpose. It is hypothesized that, as an industry, we would do well to focus on assurance (giving and receiving) and, more importantly, capability building. Public asset owners in particular should be investing in their organizational capability, and industry should be focused on providing enduring uplift in knowledge and management system improvement. Asset owners cannot separate themselves from their assets and the realization of organizational outcomes. Independent review and advice at the third line should provide the assurance required to give confidence that those operating and maintaining your assets are capable and are “doing a good job.”

If we were to do reliability analysis on certain assets, which assets are worth our effort and provide the greatest impact? To solve this, our team developed a focused methodology to help us identify which asset types need to be further analyzed in the coming years. Our goal is to identify the asset type that causes the highest generation of lost opportunity cost and the highest maintenance costs. Ideally, the generation of lost opportunity cost should be added to maintenance cost. Lost opportunity cost is calculated from existing operational performance data, which is collected and used for NERC GADS (North American Electric Reliability Corporation Generating Availability Data System) reporting. Maintenance cost is calculated from maintenance history in CMMS (Computerized Maintenance Management System). The operational performance and maintenance history data are illustrated using a Pareto chart. As a result, the asset type that causes the highest generation of lost opportunity cost can be identified from the chart. However, asset types with the highest maintenance costs may not indicate the asset type with the most issues. Besides, the NERC GADS codes may be related to one or more assets in CMMS and, therefore, need a relationship to be created if the costs are to be added.

Are we converting our data to decisions? What is the state of digital adoption in asset management? What has changed since the onset of Covid? What has stayed the same? Using the DIKW Pyramid as our guide, combined with the experience and insights of our panelists; we will explore best practices in data-informed decision-making. Are we now in a much different place on our digital adoption journey?  

We are in unprecedented times. Covid-19 wreaked havoc on supply chains; decreased production during times of increased demand. Labor shortages, chip shortages, long lead items turning into “maybe next year, if you’re lucky” items. The Russia Ukraine war added further stress to supply chains through sanctions, port closures, fuel shortages and much more. What once was reliable is now unreliable. So how can companies overcome an unreliable supply chain to maintain their reliability? There are several ways to mitigate unreliability; scenario planning, supplier management, and technology. There is no one size fits all and what may work for one company will not necessarily work for another. Scenario planning involves reviewing every potential situation that could occur, then working through to see how the company would be impacted. Ultimately this results in mitigation plans for each scenario. These can then be reviewed and implemented. Proper Supplier Management includes ensuring all suppliers have their scorecards reviewed on a regular basis. Their information updated and kept current. It can also include reviewing which suppliers can become substitutes for others in the event one is not able to provide the required product in time. Technology is important as it links all the information together. Algorithms can be created to let management know that certain parts are low, equipment is wearing out sooner, it also collects information on suppliers for the scorecards. Overall technology is the glue that binds and provides real time information updates. This presentation will review how to best use technology to help mitigate reliability and supply chain issues.

Reliability and Maintenance (R&M) teams at manufacturing facilities employ different maintenance strategies on their physical assets to achieve the desired reliability and maximize the availability of the assets. Most of the production downtime in manufacturing facilities is because of unexpected (or) random failures of equipment and the associated reactive maintenance work. One of the factors that affects the total time to fix failed equipment is spare parts availability. The increasing complexity to minimize production downtime with aging assets demands problem-specific decision models. In this study, a multi-criteria decision model is proposed to assist the R&M stakeholders at manufacturing facilities in making decisions on stocking the right parts. The proposed model will help facilities to stock the spare parts required to maintain the system with-in acceptable and manageable risk. Two case studies from a pulp mill will be presented to demonstrate the use of the proposed decision model. The first case study deals with “Pulp Machine Process Area” with historical data on equipment failures and spare parts usage while the second one focuses on a newly commissioned plant without failure information. The proposed decision model helped to identify the right parts to stock and minimized the risk and inventory costs in both cases.

Asset management focuses on adding value as a form of an investment in the physical preservation of the asset, financial stewardship, and data analytics. We will share experiences and best practices in evidence-based asset management that strengthens smart outcomes and strategies. Gain insights and knowledge of successful strategies to ascertain owner support

In 2020, as the COVID-19 pandemic gripped the world, the foundations of business operations shifted instantly. With ripple effects continuing today, organizations continue to strive to adapt to the changing landscape. In these times, the principles and practices of asset management are more critical than ever and can be a framework for embedding resiliency. This presentation will delve into the relationship between asset management principles and three focus areas impacted by COVID, while sharing real life examples from water and wastewater operations. First, we will explore how facility lockdowns and supply chain disruptions impacted equipment repair parts availability. We will dive into how facilities with strong critical spare parts management techniques weathered these disruptions with less panic and time spent chasing parts. And, how techniques such as predictive maintenance programs and planning scheduling better accommodated longer lead times before equipment intervention was required. Second, we will discuss what happens when only a few individuals own the asset management program and they are suddenly absent for health or family support obligations for an extended period. We will interconnect how building a strong ownership of asset management across disciplines and embedding processes and procedures ensures a lasting culture of asset management. Finally, lockdowns and travel bans forced our team to explore new methods for deploying technical support without onsite visits. Innovative approaches from virtually facilitated risk review workshops to augmented reality condition assessments have been developed and implemented over the past year. With advancements in technology, as the accumulation of data grows there has been a concentrated effort to leverage this data toward making data-justified decisions. Over the past year there has also been an increasing emphasis to leverage these analyses to bring decision teams to agreement as managers and client interact virtually. With an interdependent global economy it is only a matter of time until the next disruption hits, and implementing the core principles of asset management can enhance the resiliency of your organization.