3.2 Performance Measurement & Optimization

In our increasingly digitized and networked environment, the expectations for excellence in asset management are ever growing. While an abundance of maintenance and sensor data have become available, companies must develop the proper application of the data in their maintenance strategies. In this presentation, we’ll discuss the potential of your operational and maintenance data in the context of asset management, and explore different machine learning (ML) algorithms and how they may be leveraged to unleash hidden patterns in your asset management strategies. We’ll introduce some foundational topics required for ML, such as the taxonomy and data preparation steps critical to all ML approaches, the probability and statistics supporting ML, and how the evaluation of the quality of our models. C-MORE has actively applied machine ML methods to interesting real-world problems, such as the categorization of power generation units according to reliability characteristics, and anomaly detection in linear assets to optimize required maintenance actions. We’ll share a few of our case studies so participants can experience how ML methods can be used in maintenance, reliability and operations.

The asset hierarchy is often thought of as a way to organize assets so they’re easy to find in the CMMS. While a well-structured asset hierarchy does make work management easier, it’s much more than that. The asset hierarchy, when well conceived and utilized, will ensure the right reliability and costing data can be extracted from the CMMS. This enables more than just micro improvements in reliability involving a single asset; instead, it enables macro views of reliability and cost trends across the entire organization. Setting up an asset hierarchy to support these types of activities requires forethought and planning, but by following some guidelines, any organization can be set up for success. First, the asset hierarchy must have a standard that identifies how all assets will be categorized and described, and the specific data required for each asset class. This is vital, as not all assets warrant the collection of specific data, reducing the burden of the setting of the hierarchy. As assets are categorized, the failure code library can be developed and linked to the specific asset classes. This ensures only relevant failure codes are displayed for the assets, improving the adoption of failure data collection. With the asset hierarchy built and relevant failure data collected, trends can be established across asset classes, similar processes, etc. The trends enable reliability improvements to be implemented across larger swaths of assets, providing rapid improvements in reliability. This presentation will provide guidance in how to develop an effective asset hierarchy based on ISO 14224, how to implement the changes in the CMMS, and finally how to leverage the asset hierarchy to identify macro trends. Without a proper asset hierarchy, any organization will struggle to get meaningful and actionable data from their CMMS to drive reliability.

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! 

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.

Companies are continually faced with competing priorities and limited resources, and, as a result, it's critical to drive value from the business decisions they make. With more focus on asset management principles, these decisions must be justified through demonstrated and quantified value to the business. With complex systems of equipment, such as a pipeline, determining the value of a project or proposed improvement can often be difficult to estimate—especially when considering an asset's lifespan. In such cases, a more systematic, data-driven approach may be required to predict value. RAM models are one such tool that can be used to achieve this. In this case study, we'll look at how RAM models have been developed and used at Enbridge Pipelines to identify and quantify the risks to throughput volume. The models include key equipment and operational events, along with specific throughput impacts and simulated long-range demand forecasts in order to prioritize the risks and focus resources toward capturing the best opportunities. When specific improvement opportunities (e.g., additional equipment sparing) are identified, alternate cases of the model can be created and the results compared to the base case to support decisions and justify projects. We'll explore some of the challenges we encountered during development and highlight examples of how this tool is being used to quantify value, as well as the approach taken to disseminate the information within the organization.

One major challenge at the operate and maintain phase of an asset is achieving and sustaining the forecasted availability and reliability as intended at the project delivery phase. Many problems arise—equipment failures, underperformance, high costs—that are caused by numerous issues. The resolution demands thorough understanding of the causes of the issues, which we usually attempt to achieve through RCA methodologies. I've experienced many repeated failures even when RCAs have been conducted, due, mainly, to most of the RCAs focusing attention on solutions to the problem outcomes with limited focus on the human and system causes that drive the outcomes. The Causal Learning Approach brings in the understanding of these other causes that ensure effective and sustainable solutions development. There are three levels of causes: the physical outcomes; the human causes; and the system causes. The Causal Learning Approach also focuses on causal reasoning instead of defensive and solution reasoning. This presentation will provide the understanding of these causes and the three key elements of this approach: discovery, learning, and solution generation.

Any plant, in order to maximize its production, must have a world-class maintenance team that takes care of every single piece of equipment in the field. Maintenance teams could be considered the superheroes of any plant, since they must always maintain and return the equipment in the fastest and most efficient way. Wrench time is the actual time a maintenance crew works on a piece of equipment, and wrench time analysis is used to measure the maintenance team's effectiveness. Many companies apply wrench time for a very limited time and do not go for a continuous way of study. This presentation will show a self-reporting wrench time case study that was implemented in a Saudi Arabian petrochemical plant. We'll aim to explore the effect of self-reporting wrench time and answer the following three questions: Does wrench time analysis increase maintenance efficiency? Does self-reporting wrench time lead to better maintenance efficiency? What is the impact of self-reporting wrench time on maintenance team performance?

Metrics, best practices, more than 40 key elements to implement, challenges, and opportunities all combine to make a successful implementation difficult. Where do you start, and how do you know how to work on what matters? Once you understand how it’s all related, you can focus on the vital few to leverage the maximum ROI. This presentation will clarify the importance of culture and employee engagement, along with other key plant floor performance indicators that will be clarified with data. We'll look at the current state of R&M; what’s working and what's not; survival skills for the next decade; impacts of connected technologies (edge computing, big data, machine learning, AI, 3D printing, augmented reality); the importance of getting your data ready for what's coming next; and relationships between R&M and safety, people engagement, quality, throughput/uptime, and cost.

This case study will show how we used an analysis of standard work management metrics and a systematic approach to identify opportunities to improve our plant. We'll provide specific examples of how we developed and implemented the approach and the results we achieved. We'll also describe the fundamental understanding and steps that could be taken to implement a similar approach at any plant, or for any particular metric. Topics will include cultural recognition of KPIs as an improvement tool, not a personnel measurement stick; understanding all the various causes and influences on any particular metric; analysis and categorization of deviations; identifying losses as acute one-offs vs. chronic systemic issues; behavioural vs. procedural issues; understanding change/improvement requirements, what can be directly controlled and what can be only influenced; determining corrective actions; and tracking the resulting improvements. Specific examples will be derived from our site's application of this methodology to schedule compliance, PM/PdM compliance, and emergency work metrics.  

As always, equipment maintainability plays an important role in uptime. Besides the reduction of failure rates, the quick recovery from those failures or the successful execution of scheduled activities makes a considerable difference in availability indicators. The application of Lean tools and Six Sigma analysis contributes to the improvement of maintenance execution by applying the 5 steps of Lean Six Sigma methodology (Define, Measure, Analyze, Implement and Control) and using the tools associated with them. This webcast will discuss Lean Six Sigma theory, basic principles of the methodology and case studies showing the use of tools. Case 1 will illustrate the application of Lean Six Sigma in scheduled preventive maintenance for slurry pumps operating in the oil sands industry. Case 2 will examine how the use of Six Sigma analysis reduced the corrosion rate of tubes in a bank of 12 heat exchangers shell and tube type, which heat diluted bitumen upstream of a distillation tower. Both cases emphasize the importance of using data and facts to make decisions, including front end personnel, and the sustainment of implemented solutions.