4.01 Asset Information Strategy

PEMAC, FMC, Toronto Metropolitan University, and municipal experts across Canada have partnered on a project entitled “Leveraging Municipal Asset Master Data and Information for Maintenance and Reliability Readiness.” In this project, a survey of Canadian municipalities has been conducted to determine how asset data and information are collected, when, and how it is set up in various systems across the asset’s lifecycle stages. This presentation will highlight the survey results and make recommendations for potential improvements specifically related to setting up maintenance and reliability for success. Many municipalities have been struggling with such improvement areas for years to set up processes, procedures, and systems. The survey results will help attendees understand the current Canadian landscape and allow making recommendations to improve how and when municipalities best manage their various processes and systems towards improving asset and maintenance management across municipalities. The survey results will help develop and deliver a training course for municipal practitioners in the summer and fall of 2023. The information gathered will also aid in developing a white paper and business case that will increase the profile, understanding, benefits, and requirements for asset master data and information readiness during an asset’s acquisition phase prior to being handed over to the operations and maintenance phase.

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.

According to GFMAM Landscape, the performance of asset-intensive organizations is dependent on the quality and availability of asset data and information. So why does research indicate that 70% of plant operators report 33% to 50% of their asset and process safety information is either missing, incomplete, inconsistent, or outdated? Common complaints from maintenance planners, reliability engineers, facility engineering, process safety and compliance managers include the following: “We can’t find it,” “It’s not complete,” and “We don’t trust it.” As a result, personnel continually make safety, engineering, financial, capital, maintenance, and operational decisions without full access to complete, consistent, and up-to-date information. Such decisions are suboptimal and can cause significant loss. We call this value leakage. Have you ever wondered how much value leakage is costing your organization? Why do the underlying causes of value leakage persist, and what can you do about it? In this presentation, we examine the root causes of value leakage—from incomplete project information handover, to a lack of standards and processes. We then explore a successful framework to improve AIM, including building the business case and return on investment (ROI). Attendees of this presentation will learn how to identify value leakage and the underlying causes; how to calculate the ROI (qualitative and quantitative) of improving asset information management to reduce value leakage; and quick wins and long-term strategies for improving asset information management.

Digitalization for sustainable operations. This presentation will discuss digitalization for sustainability, discuss the hype cycle for AI/ML lead initiatives, and provide a road map for real-world actions. Actions must include a bottom-up approach to delivering sustainable value and return on investment for a tactical realization of your organization’s ESG sustainability goals. A case study will show how a digitalized reliability initiative improved asset availability by 25% and reduced flaring by more than 80% over the past five years.

Applying agile data governance and leveraging 21 century tools and methods to create an ecosystem that supports the success of asset data management strategies. This approach addresses challenges in resourcing for developing strong governance that considers strategic, tactical and operational needs while providing a unique approach to data gathering, quality and quantity of data at a program level.

Organizations are observing the change in the maintenance landscape with the use of data-driven analytics for decision-making. Underpinning these analytics is Machine Learning. The algorithms form the model that ingests data to represent the system and predict its future state. While this method has found rapid applicability in other sectors, the field of Reliability & Maintenance Engineering is still exploring ways to adapt this idea to its conventional Asset Management programs. The objective of this paper is to explain the predictive power of machine learning by wrapping it around a prevalent reliability tool: the P-F Curve. Initially proposed in the Reliability-Centered Maintenance (RCM) framework, the P-F Curve is ubiquitous and the practitioners understand its simple and elegant description of the failure behavior. In spite of its understanding, the use of the P-F Curve has been minimal in everyday analysis to estimate when and how soon the failure will occur. Predictive Maintenance (PdM) Tools such as Vibration Analysis, Ultrasound, and Thermography have made the P-F Curve more accessible. Adding Machine Learning to these PdM tools, with a real-time data stream, will amplify the value of this analysis with better detection of Potential Failure (Pf) and forewarning of Functional Failure (Ff). Having real-time data and a real-time P-F Curve plot will enable the users to capture the changing conditions. We define this new curve as the Dynamic P-F Curve™. Dynamic P-F Curve™ Machine Learning models will estimate the time available (P- F Interval) for the maintenance team to respond to an asset before catastrophic failure. This interval will change if the asset experiences an external force causing it to wear out sooner. Thus, a dynamic curve makes the maintenance plan itself changeable and agile, improving the plan's efficiency. The final part of the presentation will showcase an example of how a Dynamic P-F Curve™ is calculated and represented by using an open-source data set. The resulting change in the maintenance plan actions is also prescribed to fully explain this idea, concluding with the list of use cases.    

The successful use of the technologies associated to Building Information Management (BIM) depends on the interest and levels of investment that owners are willing to put into their projects. According to U.S. and Australian studies, the costs of poor information management in construction for each of these countries are nearly 15 billion U.S. $. The largest losses (almost two-thirds) were found among property owners. The implementation of BIM technologies for facility management focuses mostly on the technological aspect and often neglects the change management required to migrate from traditional approaches to asset management processes. BIM leverages the generation and use of digital representations of buildings and infrastructures in design, construction, and operations. The cost, efficiency and communications benefits that accrue from fostering single source of truth integrated data sets throughout infrastructure project lifecycles are forcing engineering firms, construction companies and public policy offices to rethink their processes and actions. The biggest potential opportunity for leveraging BIM processes following design and construction is for Facilities and Assets Management. Potential benefits include higher quality overall results, improved data preservation and transfer between life-cycle actors, effective predictive maintenance and energy efficiency. Leveraging the benefits of BIM technologies is easier said than done. There are few generally recognized best practices and many outstanding questions. How can we better plan the integration of BIM and FM into future projects? How can we integrate BIM into the management of existing infrastructure and real estate inventories? What best practices can we learn from existing global trends? This presentation offers some insights on how to transition towards BIM-enabled facility management. Success on this digitization path requires strong leadership from owners and operators, from project inception to operations phase. It investigates the transfer process of information technologies in place as well as changes in the business culture and organizational structure through case studies. Ultimately, a robust process to seamlessly create and transfer data across a facility lifecycle lays the ground for leveraging advanced Construction 4.0 technologies to further optimize the operations and improve the occupancy conditions for facility users.

Often organisations are conflicted on where to direct capital and resources, given constant request and competing priorities requiring resources and money, many initiatives and maintenance activities are deferred or cancelled all together in order to meet the constraints of budget and available capability expectations. How do you know if the allocation of your money/capital and resources are being directed to the areas of greatest need and also delivering greatest value? How do you evaluate value? A deep dive into capital and resource allocation may uncover wasted effort where valuable resources have been allocated to initiatives and activities that are not as value add as some activity that has been deferred or cancelled. These decisions are often made with little or no consideration of the vast amount of information being held within the company’s CMMS. Every initiative and job can be justified but is it the best use of time and money? What data do you use to decide where best to allocate your valuable money and resources? I will share the principles behind real life examples of where failure data has been sorted to demonstrate both micro and macro impacts on a business bottom line, allowing for managers to make better decisions on resource allocation, and decisions that will deliver high value outcomes for the business. These decisions were made using information freely available, but largely ignored, within the CMMS. The information was sorted into failure modes/types and overall cost of unreliability and presented in the form of a pareto chart. When organised in this simple manner the data clearly identifies areas requiring attention that have significant impact on the business performance, and also leading to reduced wasted effort on jobs that are less important. This approach also removes some of the countless debate over the order of priority when people within the organisation feel they are competing for the limited resources up for grabs, instead allowing people to focus on what is best for the overall organisation and not just their patch.

Asset management (AM) is a far-ranging topic and can be very confusing or overwhelming to anyone who is now embarking on a program or trying to take their existing program up a notch. The biggest impact in AM is on the planning side of the AM Framework. Essentially, if you plan well, then you can execute the right activities (capital project, operations, and maintenance tasks) well. Conversely, which is the case with many organizations, there is poor planning but with efficient execution of the work and attendant sub-optimal performance. Excellence in AM requires effective planning in three areas: Growth and Rationalization, Renewal & Replacement, and Operations & Maintenance. This presentation will provide best-in-class concepts for developing these three areas and the return on investment in effective planning, and will be supported by real-life examples.   Originally presented at MainTrain 2020

Previous presentations regarding the City of Toronto’s journey in Enterprise Work Management Solution deployment discussed the requirements gathering process and methodology for ensuring alignment within the service delivery group as well as across the municipality with other project partners. This presentation outlines the project’s current progress on moving from the program layer towards more specific divisional needs and the “readiness tasks,” which are required to ensure the meeting of critical milestones to realize deployment. Strategies focus on leveraging requirements documents for OCM and integration planning, with particular attention to engaging end users.