05 Maintenance & Reliability Engineering

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.

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?

SIGGA Technologies deployment with Votorantim Cimentos SAP PM

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.

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.

Reliability and maintenance teams in all manufacturing plants have a common goal; that is, to plan and execute initiatives that will help sustain the inherent reliability of the physical assets and increase the availability of the plant. Productivity and profitability of the manufacturing plant and overall organization are highly dependent on the reliability and availability of the plant. A thorough understanding of the importance of reliability has made the top management of major corporations invest in reliability and physical asset management. When the top management invests in reliability, they typically set the corporate strategy and directions for reliability through a road map for all the manufacturing plants that operate under the corporation. When the commitment or support from the top management is available for reliability initiatives, the onus is now on the individual manufacturing plant to develop a reliability plan that aligns with the corporate strategy and/or reliability road map and the current needs of the plant. The reliability and maintenance teams typically build all the strategic, tactical, and operational reliability plans that have initiatives that would make the biggest impact on continuous improvement in reliability and bring the desired benefits for the site. This presentation will explain the systems thinking approach, along with the seven golden rules and seven key factors, that will help reliability and maintenance teams to build an effective reliability plan. In addition, this presentation will also address the top three challenges in building a reliability plan and how to overcome those challenges through two case studies. The first case study is about developing a three-year reliability plan, and the second case study is about developing an annual reliability plan. Both the case studies will explain the application of the systems thinking approach, seven golden rules, seven key factors, and top three challenges that were dealt with and solved.

Presentation highlights the importance and interdependency of three pillars of success (people, processes, and technology) and Cameco’s asset management improvement efforts through each of these, including lessons learned. Audience will learn about the importance of organizational change management, business process management and agile methodologies, some of the technologies supporting asset maintenance and reliability, and its new Asset Management & Reliability Center of Excellence.

Join us for an opportunity to learn about what we must consider for asset management with new constraints to the supply chain. We will delve into shipping restrictions, budget changes and planning considerations. We will also touch on opportunities that have risen out of the learns from the past two years and moving forward with the risks in mind. 

We all love to solution and check off that win for the team, but moving too quickly may result in introducing new problems or even amplifying previously smaller issues. The latter is common particularly in technology implementation where the hope is for efficiency improvement, but the results sometimes don’t meet the desired outcome. Although root cause analysis was informally practiced at the SMCDSB, the implementation of a formal program using a process approach to problem solve, define requirements, and solution has strengthened troubleshooting and preventing future problems. By taking a very focused stance on identifying the problem or need clearly and leveraging the Kepner-Tregoe Analytical Troubleshooting method, there is improved clarity, definition, and logic in how the analysis completed. I wish to share the journey of RCA program implementation for the SMCDSB with examples and successes we've achieved.

Recent developments in AI, ML and related techniques see wide adoption in many industries. However, in the asset management area, such technical advances are still in their infancy, especially in the maintenance, repair, and operations (MRO) area. Part of the reason is that, contrary to production, MRO data has its unique characteristics (e.g. incompleteness, inconsistency and heterogeneous), and most organizations are still planning to introduce diagnostic sensors dedicated to maintenance and equipment reliability. We face both challenges and opportunities in advancing data-driven continuous improvements within the asset management world. This presentation shares the findings of our current research and development focus. Titled “How to use historical data to find opportunities to improve the effectiveness of equipment reliability programs, optimize MRO inventory operations, and enhance MRO workflow management processes”, we will first examine the characteristics of MRO data as their uniqueness to a specific company, plant or equipment and their commonality across all sectors. Then we evaluate the feasibility of applying AI/ML techniques with MRO history for better operational efficiencies. We need to understand what data is related to human knowledge, human interaction and process, and what data is associated with the actual condition of the asset, and if there are patterns and models that can be learned. Last, we will demonstrate that AI/ML can find equipment agnostic models and patterns which help continuously improve MRO operations across different industries. Based on the findings, we will also show how AI/ML models learned from historical MRO data can be translated into prescribed actions for improvements in equipment reliability, MRO inventory and workflow operations for individual organizations.