4.04 Data & Information Management

Often organisations are conflicted on where to direct capital and resources, given constant request and competing priorities requiring resources and money, many initiatives and maintenance activities are deferred or cancelled all together in order to meet the constraints of budget and available capability expectations. How do you know if the allocation of your money/capital and resources are being directed to the areas of greatest need and also delivering greatest value? How do you evaluate value? A deep dive into capital and resource allocation may uncover wasted effort where valuable resources have been allocated to initiatives and activities that are not as value add as some activity that has been deferred or cancelled. These decisions are often made with little or no consideration of the vast amount of information being held within the company’s CMMS. Every initiative and job can be justified but is it the best use of time and money? What data do you use to decide where best to allocate your valuable money and resources? I will share the principles behind real life examples of where failure data has been sorted to demonstrate both micro and macro impacts on a business bottom line, allowing for managers to make better decisions on resource allocation, and decisions that will deliver high value outcomes for the business. These decisions were made using information freely available, but largely ignored, within the CMMS. The information was sorted into failure modes/types and overall cost of unreliability and presented in the form of a pareto chart. When organised in this simple manner the data clearly identifies areas requiring attention that have significant impact on the business performance, and also leading to reduced wasted effort on jobs that are less important. This approach also removes some of the countless debate over the order of priority when people within the organisation feel they are competing for the limited resources up for grabs, instead allowing people to focus on what is best for the overall organisation and not just their patch.

The Northwest Territories has 33 geographically dispersed communities, many of which are not grid connected for community power supply. The majority are powered by small, local diesel plants with others supplied by three hydro systems which generate the majority of the total power produced annually by the Northwest Territories Power Corporation (NTPC). This paper will describe how the condition of these assets is assessed and monitored, the process used and how the results form part of the annual capital planning and implementation process. Asset management at NTPC is divided into Thermal, Hydro and Transmission and Distribution; the focus of this paper will be primarily on thermal and hydro asset health inspections. On an annual basis, one third of the assets are inspected in person by the relevant asset manager and a small team of subject matter experts. The process by which these inspections are conducted will be outlined, including what information is gathered, and what we look for when carrying out the inspections. The asset condition information for various component parts is entered into one spreadsheet for each asset. The paper will describe how an asset health index for that asset is assigned. The condition of the asset determines what further action or inspections are required and the criteria used will be discussed. The presentation will also outline how the results from the inspections are used as part of a project prioritization process for capital planning. Current reporting practices on individual asset health indices and for each power plant will be described. In discussing this topic, NTPC sees an opportunity to receive feedback from others and to promote discussion on best practice from other power utility companies.

Around the turn of the 21st century, municipalities began using the term asset management (AM) to refer to a broad range of processes that span the continuum, from managing short-term asset maintenance to long-term asset investment planning. Over the last two decades, AM has been accepted as an umbrella term that refers to the processes that work together to answer the following: When, why, and how should we spend money on infrastructure assets to ensure they meet evolving societal expectations? But the traditional approaches to developing AM processes—extremely granular asset inventories, age-based spending need forecast models, complex level of service metrics—have frustrated organizations, cost them millions, and not practically helped to answer this fundamental question. In recent years, a number of AM champions have developed a consumer-based approach to realize value from assets in the achievement of societal objectives. Clear processes create an asset expenditure to asset performance relationship that helps set overall spending levels and enables a living management system that’s used to develop dynamic prioritized short-term spending plans. This approach enables organizations to engage with their customers/clients to balance asset performance (level of service) expectations with financial affordability. This Asset Stewardship Quality Management System resets the AM paradigm by bringing clarity to what AM processes should do and how enablers (data, people, technology) can help. The tangible impacts resulting from the new AM paradigm include a reduction in the granularity of asset registers, improved accuracy of measuring asset performance and tracking asset spending, and confident spending need forecasts to ensure infrastructure systems can effectively underwrite desired societal progress. These tangible impacts are realized through addressing all three enablers: improving data management and data collection activities, providing a training program to staff, and leveraging innovations in hardware and software technologies.

Too often we hear the struggle of not being able to get the information that is required out of the system. It's like the data has disappeared into a big black hole. Maintenance Staff get frustrated in having to feed a system that is difficult to use and time consuming for what is perceived as little or no benefit. This discussion details what a CMMS can do and how best to realize the benefits from using a CMMS. There are a number of critical success factors in managing your CMMS including:• Organization policies and objectives• staff turnover• training• reduced staffing• database review audits• outdated software

New technologies regularly enter the world of asset management (AM), often leveraging new inventions, which, in turn, are driven or supported by other advances such as computing power and big data handling. Such is the case for Motion Amplification, a new technology impacting crucial aspects of vibration analysis, machinery and structural troubleshooting, root cause analysis, and communications. This presentation will outline which areas of AM are impacted so we can have a road map facilitating the integration of the new tool into a global strategy and get an overall picture of the impacts it will have. We’ll also provide a brief technical introduction and some practical illustrations.Originally presented at MainTrain 2020

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.

Asset management (AM) is a far-ranging topic and can be very confusing or overwhelming to anyone who is now embarking on a program or trying to take their existing program up a notch. The biggest impact in AM is on the planning side of the AM Framework. Essentially, if you plan well, then you can execute the right activities (capital project, operations, and maintenance tasks) well. Conversely, which is the case with many organizations, there is poor planning but with efficient execution of the work and attendant sub-optimal performance. Excellence in AM requires effective planning in three areas: Growth and Rationalization, Renewal & Replacement, and Operations & Maintenance. This presentation will provide best-in-class concepts for developing these three areas and the return on investment in effective planning, and will be supported by real-life examples.   Originally presented at MainTrain 2020

Previous presentations regarding the City of Toronto’s journey in Enterprise Work Management Solution deployment discussed the requirements gathering process and methodology for ensuring alignment within the service delivery group as well as across the municipality with other project partners. This presentation outlines the project’s current progress on moving from the program layer towards more specific divisional needs and the “readiness tasks,” which are required to ensure the meeting of critical milestones to realize deployment. Strategies focus on leveraging requirements documents for OCM and integration planning, with particular attention to engaging end users.

Many organizations have implemented processes and tools to collect data to facilitate informed decision-making. Often, they will seek out best practices and measures to assist in decision-making or rely on technology to guide their basis. In many cases, however, these same organizations approach a gap in tactical deployment and in the ability to draw a connection to the follow-up or pre-emptive actions required to derive value from assets. We'll review the processes for establishing a framework for alignment and priority setting while looking at the techniques used for resiliency and risk management using a technology-agnostic approach. We'll review potential data sources that can be leveraged for decision-making and can reflect the needs and current state of the business environment. Additionally, we'll discuss the relationship and application to the decision-making process.  

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.