9.3 Master Data Management

The world your business operates in is constantly changing. Supply chain disruptions, market pressures, budget constraints, regulatory changes, extreme weather, new technology, challenges retaining experienced engineers and maintenance technicians, and more can impact the operating context of your assets and equipment.Minimizing risk, and maintaining high performing assets in the face of constant change often requires organizations to accelerate the rate at which risk and criticality are re-assessed, maintenance strategies updated, people and systems enabled, and new maintenance plans put into practice; connecting the dots in a closed loop process.In this webinar, we will share practical approaches to help you connect the dots between strategy and execution, and ensure the investments you make in your reliability based maintenance program are put into action and deliver the intended results as the broader organization and asset management context continues to change.

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.

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.

An Intelligent machine can sense its environment, take a decision and apply an action or give a recommendation. How this can transform maintenance? With every Industrial Revolution, Maintenance tools and strategies grow to provide the needed service for this industrial era. Now what shall we do with the Augmented reality and other technologies that Industry 4.0 introduces in the production environment??! It is important to understand what AI -Artificial Intelligence in details is because it is currently part of our work and life even if we do not realize this. When we understand how AI works, we can use it as our ally. Otherwise, we shall resist its existence specially when it starts to give recommendations and report of what went good and what went bad. AI is a title frequently applied to the project of developing systems with the intellectual similar to those of humans, such as the ability to reason, discover meaning, generalize, or learn from past experience. The human maintenance team applies its intellectual process at every situation it encounters. This intellectual process includes the ability to reason, discover meaning, generalize, or learn from experience. How AI does this? It is all based on software and Algorithms. There are two (2) ways for the software to yield intelligent advices or actions. You either add to it all the possible solutions of a problem and the software searches through all possibilities to find a one matching to this situation, then returns the stored actions for this possibility. Alternatively, the other way is to let the algorithms of the intelligent software infer some reasoning based on the inputs then solve the problem. . Let us relate this to maintenance

The Regional Municipality of Durham is a community that makes up the east end of the Greater Toronto Area (GTA), which comprises multiple cities and townships. The region provides a multitude of services to approximately 745,000 residents and maintains $17.85 billion in assets and infrastructure. The region was using several disconnected applications and business processes to manage these services, many of which had limited functionality, reporting, and analytics, as well as a lack of integrations to other systems. In an effort to standardize and streamline these services, the region amalgamated all of the tracking of regional assets, maintenance management, and business technology processes. The region began the process of requirements gathering in 2013; at this time, a steering committee was created to govern the project, and project leads and business subject matter experts were engaged to ensure the product selected met business requirements. In 2015, the region began the procurement process: request for proposal, evaluation, vendor presentation, and negotiations. Maximo was the selected enterprise maintenance management system. Durham used a multi-phased implementation plan—including Planning, Design, Execution, and Closing—which consisted of three go-live dates. This multi-phased approach would span over the course of three years. The initial phase of the project included detailed design, organizational impact analysis, future business process design, future role modifications and development, and multiple-tiered information sessions. The organization identified current operational gaps and business process changes were required. It followed Use Case business processes with some adaptation for operational responsiveness and consistency within the To-Be roles. The business was able to retain current operational practices as much as possible but built in a structured and disciplined approach to maintaining assets. This will influence and impact the quality of analytics and reporting. Through its approach, the region was able to implement a centralized maintenance management system across multiple divisions. This implementation impacted 800 end users across 13 divisions and multiple third-party system integrations. It also performed a readiness assessment of departments, divisions, and areas and organizational, process, and technology criteria. It created a go-live and system support strategy, and monitored system, sustainability, and performance throughout the implementation process.

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.

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?  

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.

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.