10.1 Metrics / KPIs

SIGGA Technologies deployment with Votorantim Cimentos SAP PM

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report 33% to 50% of their asset and process safety information is either missing, incomplete, inconsistent, or outdated?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.

If we were to do reliability analysis on certain assets, which assets are worth our effort and provide the greatest impact? To solve this, our team developed a focused methodology to help us identify which asset types need to be further analyzed in the coming years. Our goal is to identify the asset type that causes the highest generation of lost opportunity cost and the highest maintenance costs. Ideally, the generation of lost opportunity cost should be added to maintenance cost. Lost opportunity cost is calculated from existing operational performance data, which is collected and used for NERC GADS (North American Electric Reliability Corporation Generating Availability Data System) reporting. Maintenance cost is calculated from maintenance history in CMMS (Computerized Maintenance Management System). The operational performance and maintenance history data are illustrated using a Pareto chart. As a result, the asset type that causes the highest generation of lost opportunity cost can be identified from the chart. However, asset types with the highest maintenance costs may not indicate the asset type with the most issues. Besides, the NERC GADS codes may be related to one or more assets in CMMS and, therefore, need a relationship to be created if the costs are to be added.

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.

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.

As maintainers, we know there is a lot of value in what we do. Without our work, plant, and equipment will soon stop and our companies will then go out of business. What we do impacts safety, health, revenues, costs, and company reputation. A dirty little truth about maintenance is that it is only we who work in it, that really know the value of what we do – or do we? We do know our value in qualitative terms, but can we quantify it? Most maintenance can be improved and we know it. We can do things more efficiently, and we can keep things running more reliably. We often know how to do that, but when we want to make those improvements there is no money for them. Why? Most business people know very little about what we do and how it impacts their business. They see maintenance as a repair shop. We fix what breaks. And they know little, or nothing, more. They may know that maintenance represents a significant cost, and they may even know that they can’t get away with cutting it too much. But they do not know the full value of what maintenance can deliver, nor what it takes to deliver it. If you want to make improvements you need a decision-maker, someone with executive-level authority, to back you up. To get that, you will need to explain what value you can deliver, and in terms they can understand. You will need to show them the savings that are possible from doing things more efficiently, and the added revenues that can arise from investment in defining the right work. You will also need to show them how their support is needed to bring operators and the supply chain into the team with you to make those changes happen so that benefits are fully realized. Quantifying value and being simple in how you say it matters.

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?  

Imagine your production facility as a 1950s pickup truck. It’s functional, but a lack of available parts, new regulatory standards, and an absence of modern sensors and electronics make diagnosing problems and improving performance difficult. Commissioned in 1954, Sherritt International Corporation faces many challenges characteristic of a classic car—challenges that not only include equipment lifecycles, but also human resources, software, workflows, and asset obsolescence. This case study will review the training, business process transformation projects, organizational design changes, and continuous improvement initiatives that Sherritt is implementing to modernize their work management processes. See how optimization of these processes have helped address the challenges presented by an aging site, and learn how projects were prioritized, which initiatives helped build organizational capability and improve performance, and what wasn’t worth the effort.

We are in unprecedented times. Covid-19 wreaked havoc on supply chains; decreased production during times of increased demand. Labor shortages, chip shortages, long lead items turning into “maybe next year, if you’re lucky” items. The Russia Ukraine war added further stress to supply chains through sanctions, port closures, fuel shortages and much more. What once was reliable is now unreliable. So how can companies overcome an unreliable supply chain to maintain their reliability? There are several ways to mitigate unreliability; scenario planning, supplier management, and technology. There is no one size fits all and what may work for one company will not necessarily work for another. Scenario planning involves reviewing every potential situation that could occur, then working through to see how the company would be impacted. Ultimately this results in mitigation plans for each scenario. These can then be reviewed and implemented. Proper Supplier Management includes ensuring all suppliers have their scorecards reviewed on a regular basis. Their information updated and kept current. It can also include reviewing which suppliers can become substitutes for others in the event one is not able to provide the required product in time. Technology is important as it links all the information together. Algorithms can be created to let management know that certain parts are low, equipment is wearing out sooner, it also collects information on suppliers for the scorecards. Overall technology is the glue that binds and provides real time information updates. This presentation will review how to best use technology to help mitigate reliability and supply chain issues.

Within the mining industry, the library of KPIs has not kept up with data that has become increasingly available through digitization, therefore leading to an overuse of lagging performance indicators. Additionally, due to the fact that strategic corporate goals are oftentimes built on the basis of soft or perceptual measures from stakeholders, which are subjective in nature, organizations struggle with the process of linking strategic level goals to KPIs on the shop floor. Therefore, creating a communication gap between technical teams and senior management. As a consequence, maintenance leaders have a difficult time demonstrating the added value that maintenance activities create with an organization, leading to challenges in securing the resources required for things such as continuous improvement projects. Considering that the implementation of a robust KPI framework is an alignment of three key areas: people, process, and technology, the first portion of this presentation will be investigating a holistic approach to developing maintenance KPIs that are integrated into corporate strategic goals. Therefore, outlining the steps required for organizational leaders to begin standardizing data gathering procedures and creating a trustworthy system that can be leveraged for decision making. Aside from the previously discussed administrative challenges associated with data gathering within organizations, it must also be highlighted that KPIs are collected on a monthly or quarterly basis, meaning that training sets for forecasting models are extremely limited. Therefore, in order to address the reliance on lagging performance indicators within maintenance departments, a variety of time series models capable of achieving high accuracy on small datasets will be discussed along with direct business applications. Through achieving these goals, the intended outcome is to create a more contemporary data driven methodology for selecting organizational KPIs, as well as directly demonstrating the added value that the various business units create within the organization.