3.05 Maintenance Delivery

This Project was established to review all facets of Maintenance within the St Lawrence Seaway Management Corporation (SLSMC) with a goal to improve productivity, maintaining a positive impact on maintenance staff moral and provide the same or increased equipment reliability. Maintenance Programs were reviewed for all major assets and analyzed using subject matter experts leveraging the FMECA (Failure Mode, Effects & Criticality Analysis) tool to determine areas of vulnerability within the assets ability to perform at the designed operational level Maintenance Processes were analyzed using some of the Lean Six Sigma and Work Measurement tools with focus on the six (6) steps of Work Management Cycle (Identify, Plan, Schedule, Assign, Execute and Learn) to get a better understanding of the problem areas and generate solutions to this issue backed by actual results. Work Organization main focus was to improve Supervisory awareness and availability in providing support to trades employees and conducting regular field audits to ensure accuracy and quality of task execution. Investigations and work process flow analysis are also planned for individual Trade Shops and Warehouse Facility Layouts to improve work space planning and component/part inventories. Change Management focus was on Vision Mapping, Stakeholder Analysis, Communication Planning and transition coordination of all improvements and changes that will affect the entire organization during the progression of each stage of the project. The findings of the project to date showed that there were a lot of excess maintenance tasks being performed on managed assets. The estimated labour times for task completion, travel and delay inefficiencies of work tasks being performed were excessive and daily performed tasks contained value and non-value activities over all process steps of the Work Management Cycle. All findings discovered and work that continuous to be performed at each stage of this project confirms that there is a lot of variability, inefficiencies and opportunities for improvements within all facets of the Maintenance within the Organization.

As the influence of the asset management approach continues to expand within Nova Scotia Power, we need a structured approach to ensure we continue to seek opportunities to optimize maintenance strategies. In a new installation, techniques such as failure modes and effects analysis (FMEA) and reliability centred maintenance (RCM) can be used to develop an optimized maintenance strategy from the start, in a top-down approach. However, the vast majority of Nova Scotia Power’s equipment was in place long before the asset management office—and, therefore, the asset management approach—existed. The result of that is a collection of value-added, but developed after-the-fact maintenance strategies. Each maintenance strategy has components of operator surveillance (rounds), testing, predictive pattern recognition (also known as advanced pattern recognition, APR), predictive maintenance (condition-based monitoring and risk-based inspections), online monitoring, and preventative maintenance. While efforts had been made to “baseline” the equipment processes when maintenance strategies were developed (i.e., “clean out” existing activities), the organic growth of the approach and the distributed nature of assets and personnel have made this difficult to maintain. Therefore, we needed an approach to optimize existing maintenance strategies, without recreating them. Nova Scotia Power has therefore undertaken an effort known as maintenance strategy optimization, and has made this activity a core accountability for the asset management team, which recognizes the need to seek continuous improvement (vs. a one-time exercise). With a focus on digitization wherever appropriate, Nova Scotia Power has asked a number of questions to streamline, standardize, and optimize its maintenance strategies. Is there opportunity to reduce PM frequency? Is there opportunity to collect more information such that we can strengthen our APR models? Can our in-house standards be revalidated to sustainably reduce operating and maintenance costs? Nova Scotia Power is answering yes to these questions, and more, and pursuing opportunities to optimize its maintenance strategies—from the bottom up! 

As our organization recently embarked on a digital transformation of our work management processes, it became obvious that what we were actually changing wasn’t just the technology we used—we also had to change our people. We recognized that this change was too big and too important to leave to chance and decided instead to apply a structured, methodical, and deliberate approach. This is a case study in the approach we took and the tools we used to ensure the changes required of our workforce were as painless as possible. We’ll outline how the approach impacted our success and detail the lessons learned. We’ll demonstrate how we approached the following topics: assessing the scope of change, analysis of gaps between present and future states, planning for change, change methodology (ADKAR), roles and responsibilities, where does change fit into project planning, communications, building desire for change, assessment of your change audience, building the knowledge and skills of your audience, and monitoring change progress. The intent of this study is to demonstrate some techniques and tools you can apply to any changes being undertaken, with the hope that you can help to “grease the wheels” of change within your own organization.

Enbridge Liquids Pipelines is at the midpoint of a multi-year journey to advance the M&R program for its electrical and mechanical assets. This presentation will share successes and challenges while aiming to apply M&R best practices to a midstream pipeline operator. Our initiative will improve M&R processes and culture, with a focus on improving multiple work streams. These include the planning, scheduling, and execution of preventative and predictive maintenance, information management, spare parts, and CMMS use.

Oftentimes, maintenance is left to suffer the consequences of otherwise “successfully” completed and handed-over projects. While project teams are more interested in the project constraints of scope, time, and cost at handover, maintenance is concerned with maintainability, reliability, availability, cost, and safety for the duration of the asset lifecycle. This conflict is not often given due consideration, and maintenance is often brought into the team long after the asset has been commissioned and handed over to operations. This presentation will make a strong case for including maintenance at all stages of a project—a case for maintenance readiness. It describes the slowly changing paradigm shift and acceptance (albeit lukewarm) of operations readiness, with no corresponding consideration to maintenance readiness. It uses real-life examples to show that the "cost savings" from not including maintenance in projects is mostly eroded in the first few months of the asset lifecycle. We'll make the case for a paradigm shift toward including maintenance readiness to all projects. Inserting the maintenance team in all the project phases will not only improve asset availability, reliability, and lifecycle cost, but also enhance cross-functional team synergy and professionalism, and ultimately reduce this stress element from maintenance.

Your KPIs and metrics—though they’re very different—are trying to tell you something. But, do you understand what they’re trying to say? Are they really measuring what you think they’re measuring, or are you missing out on important information? In this session, we’ll look at the differences between KPIs and metrics, and explore how they’re arrived at and should be arrived at. We’ll challenge some of the old and accepted maintenance measures that many organizations use. First, though, we’ll discuss why we actually measure things and examine if we meet that premise. Feel free to bring your KPIs for discussion!

Even today, many organizations see maintenance as a necessary evil neglecting the importance it has toward attaining optimum business results. These organizations have maintenance managers, supervisors, and technicians who are responsible for the preservation of their physical assets. Upon talking to and sharing experience with many maintenance colleagues in various countries, I've learned that most maintenance supervisors and managers don't have a formal maintenance educational background, yet they must make important decisions regarding assets affecting their business's bottom line. We learn about maintenance the hard way, learning from equipment failures and guessing how to avoid them by applying what has resulted well in the past and what the equipment manufacturer tells us. When organizations realize they must do something about maintenance to improve their business bottom line, they're exposed to a lot of information about many tools boasting to offering what they need to do better. This presentation will showcase the results of various case studies performed by our consulting firm at crude oil pumping, pharmaceutical, and water treatment organizations located in North and South America. Several methodologies ranging from Uptime (Strategies for Excellence in Maintenance Management) to RCM-R, ACA, RCA, and even PdM were used to tackle situations at the strategic, tactic, and operational levels.

When entering a maintenance record into a CMMS, there's often a place where the operator can enter free-form comments. These comments may contain valuable information about the health of the equipment, any maintenance activities that were undertaken, and plans or recommendations for the future. The flexibility of the comments is attractive to operators, as a precise description of the observations can be recorded. However, using the comments information in data analysis usually requires some codifying of the comments, which is time-consuming and results in a loss of nuanced information. A machine learning approach to using comments data has been applied to predict the health of hydroelectric generating units. By embedding comments into a matrix to generate a "bag of words," and applying neural networks on the vocabulary, comments can be used to assess the current state of the asset and predict its next state. In this presentation, we'll discuss three machine learning algorithms in a way that's accessible and relevant to M&R practitioners: a classification method, a clustering method, and a neural network method. Each method will be partnered with direct applications to real-life maintenance problems, including lessons learned and potential uses in other contexts.

Learn to build a business case for improvements in Maintenance at your operations. You know your maintenance performance can improve, you know the problems you are faced with, you know some of what to do to correct it, you know it will take effort and some investment, and you need to prove it is worth investing in to your senior management / executive branch. To make the case, you need to show what it can save or earn for your business - where you will see the payoff and you need to estimate the cost of the investment. This is a hand's on workshop. Bring your maintenance performance metrics and be ready to work. You will be shown useful benchmarks that can be used and how to use them. Using those and the metrics from your current state you will show the potential benefit of making changes. After working out the potential benefits we will discuss tips for getting support and making your case an easy sell.

Sherritt Fort Saskatchewan has been operating for more than 50 years and continues to use much of its original infrastructure and production equipment. Modern condition-based monitoring (CBM) technologies were not in the picture 50 years ago. So how do we cost-effectively implement CBM technology to address risk, provide advanced indication of failure, and improve the reliability of our operation? This presentation will introduce two case studies of applying CBM to aging assets: infrared thermography and continuous surface temperature monitoring for high-risk refractory lined furnace piping; and ultrasound technology to improve bearing lubrication practices for rotating equipment.