2.03 Lifecycle Value Realisation

This presentation will provide a detailed look at how Manitoba Hydro builds and applies whole-life cost models for the purpose of projecting performance, cost, and risk for an asset class. The application of predictive analytics, through the use of these models, will be discussed as it relates to a single asset class, to mature Manitoba Hydro’s asset management strategy.

Cameco Corp. has recently deployed approximately 1,500 wireless asset condition monitoring sensors across four of its operations. This presentation will explore all aspects of this project, from initial identification of business pain, all the way through to deployment and management of the system. Condition-based monitoring of rotating assets typically involves a route that is executed at a fixed interval to collect asset condition data. This data can include vibration, temperature, acoustic emissions, and others. This data is then downloaded into software and analyzed for faults and trends. This method has many shortcomings that can be solved with remote sensing technology. This presentation will take you through Cameco’s journey of identifying the limitations of traditional data collection and why an alternative was investigated. Some of the key topics will include problems and inefficiencies with the current system, methodology used to determine which sensor company to partner with, potential cost savings and benefits, deployment strategy and execution, and some screen captures of actual asset detections. Finally, we will conclude with lessons learned and benefits realized from deploying a sensor solution.

The energy landscape is shifting with the rise in electrification of transit and the rise of renewable energy shaping a new energy era that is changing the way we think about infrastructure decision making. This presentation will articulate how electrification of transit and an increase in renewables will impact medium and long-term infrastructure planning by providing examples and a practical perspective (case study) to demonstrate how Asset Management decision-making played a vital role in a utility company’s response to this change. This utility company is a key contributor to several electrification initiatives. They recognized the challenge associated with these initiatives and the overall success of the first implementation phase with minimal disruption to current operations. They are also preparing for electrification of the government transit’s first all-electric bus garage to support future procurements of battery-electric buses (eBuses) and will be working on the design and implementation of charging systems infrastructure across the city(?). Over the past 20 years, more than 50 renewable energy systems have been installed on City buildings and properties. In 2020, the city developed recommendations for the utility to achieve greater outcomes for energy efficiency, demand management, and renewable energy. The city also mandated installation of renewable energy systems on all buildings, where feasible, by 2020. The rate of development in electrification and technology in the transit sector is faster than implementation of major infrastructure developments; changes in demand patterns impact everything from the transmission and distribution networks to generation, dispatch and peak-load system capacity design; so it is not possible to “wait and see” before committing to infrastructure investment decisions. This presentation will cover how the utility is dealing with these changes by ensuring an appropriate long-term decision-making framework is in place to assure business continuity and reduce the impact on climate because it poses a particular risk for asset owners and operators. AMCL will present best practices for long-term decision-making and how the impact of change should be taken into account during the development of long-term infrastructure planning processes, in the context of a public utility.

Is your company struggling to forecast, and have parts arrive on time to perform preventive maintenance, or know when downtime should occur that would be least disruptive? Would your company benefit from more accurate cost predictions and a decrease in last minute emergency breakdowns? Or perhaps you cannot depend on what the system has for inventory or how many staff are required on site at a given time – there are somehow too many or not enough at any given time. These are common issues faced by many large manufacturers from Marine, to Mining to Oil & Gas, and many others. This session will provide an overview of how basic Machine Learning techniques can be applied to Supply Chain, Reliability, and Asset Management to gain increased insight, and better overall performance. But what is Machine Learning? It is a subset of Artificial Intelligence, where algorithms improve through experience (new data sets). The algorithms constantly evolve as more and more data is run through it. Machine Learning is useful for finding unknown patterns and relationships in data, such as sales, plants, store, or forecasting. It is an effective tool to gain insight and efficiency in day to day operations, while also providing a future forward view.

This paper describes strategic Asset Management at an Industry level. The illustrative case study reviews the Ontario Nuclear fleet of stations', life cycle planning and then applying simple risk analysis. The situation is illustrative, not to be taken as accurate in any aspect (eg costs, schedule etc). The purpose of this paper is to illustrate a practical example of how Asset Management can be applied at a strategic level, to support strategic planning over a multi-billion dollar, multi-decade program. The situation described is very loosely based on the OPG nuclear refurbishment program, but cannot be directly connected to it since all the information is proprietary and the numbers used are hugely different. The paper outline is expected to follow: • Background • Fleet Asset planning • Project Estimate process • Risk estimate process and review . • Conclusions The above is at a "bird's eye" view, but with enough details to understand the case study and how it pertains to a broader scope of applications. Learning: 1. Asset Management is a strong strategic tool when applied at Facility level. 2. Estimates, though rough at an early stage, can provide managerial information that is adequate for decision making. 3. Risk Management can be simple, but provides huge returns for management decision making.

The cost of an error depends on where you catch it. In DfR programs, the design phase is a good place to start.          Interview with ; Marie Getsug, PMP, CAMA, CMRP, CRL, CPIP | Jacobs | Program Manager            This article was originally published on MachineDesign.com.

“In asset investment planning, research suggests the difference between best practice and what most organizations have is somewhere between 8–20% in value.” 

Around the turn of the 21st century, municipalities began using the term asset management (AM) to refer to a broad range of processes that span the continuum, from managing short-term asset maintenance to long-term asset investment planning. Over the last two decades, AM has been accepted as an umbrella term that refers to the processes that work together to answer the following: When, why, and how should we spend money on infrastructure assets to ensure they meet evolving societal expectations? But the traditional approaches to developing AM processes—extremely granular asset inventories, age-based spending need forecast models, complex level of service metrics—have frustrated organizations, cost them millions, and not practically helped to answer this fundamental question. In recent years, a number of AM champions have developed a consumer-based approach to realize value from assets in the achievement of societal objectives. Clear processes create an asset expenditure to asset performance relationship that helps set overall spending levels and enables a living management system that’s used to develop dynamic prioritized short-term spending plans. This approach enables organizations to engage with their customers/clients to balance asset performance (level of service) expectations with financial affordability. This Asset Stewardship Quality Management System resets the AM paradigm by bringing clarity to what AM processes should do and how enablers (data, people, technology) can help. The tangible impacts resulting from the new AM paradigm include a reduction in the granularity of asset registers, improved accuracy of measuring asset performance and tracking asset spending, and confident spending need forecasts to ensure infrastructure systems can effectively underwrite desired societal progress. These tangible impacts are realized through addressing all three enablers: improving data management and data collection activities, providing a training program to staff, and leveraging innovations in hardware and software technologies.

Not all asset information is equal. Asset owning organizations benefit from having the right information at the right time. Asset knowledge primacy, an imperative to deliver operational excellence throughout the asset lifecycle. What is asset knowledge primacy? As the asset is developed and matures, stakeholders must calibrate data collection and exchange while curating fit-for-purpose asset information. Consistent thought-in-action leads to precision timing, relevance, and accuracy of information delivery. Successful asset knowledge capture is rarely accomplished efficiently or effectively. This doesn’t have to be so. In this presentation Chris Murphy will share an approach to provide asynchronous asset information delivery to best serve the life of the asset and support the organization to deliver value from the assets.

Studies have shown that asset performance is negatively impacted by the lack of an integrated operational readiness approach during the asset acquisition phase (or project). The resulting delays in reaching sustainable operating performance and anticipated service levels impact start-up dates, capital costs, operational revenues, lifecycle costs, customer satisfaction, organizational morale, and overall project NPV. These impacts often require large additional cost and labour injections to correct for the lack of an integrated operational readiness approach. There are a number of operational readiness asset management objectives to be focused on in parallel with the execution of a project—a must to enable any new asset, facility, or venture to meet business lifecycle targets. These prime focuses can be summarized as follows: develop capable people to safely operate, maintain, and support the project outcomes; ensure asset capability is developed and maintained in line with business requirements; implement and configure required supporting systems and business process; seamlessly manage the transition from project to operations; and ensure the engagement and alignment of the organization and all business stakeholders. This presentation will look at a number of mining, manufacturing, and public infrastructure case studies and show the benefits of using a structured approach to operational readiness during the acquisition phase of the asset lifecycle.