04 Asset Information

In a long career implementing and improving SAP SCM business processes in Oil & Gas, Pipelines, Utilities and Transportation companies, the number one consistently observed problem is poorly identified MRO material masters. Material master identity consists of the item descriptions, the manufacturer and manufacturer part number fields and classification data that support material identification. This presentation is meant to raise the profile of the topic without going into detailed solutions. It is aimed at a general level to all practitioners that make use of maintenance or SCM business processes that use materials. The presentation is software agnostic. These challenges are found in shops running SAP, Oracle, JDE, Maximo…Topics: • Introduction (discussion applies to asset intensive industry and the MRO materials records used in their supporting business processes)  • Generalized view of the current state and how we arrived here. • Structured identification taxonomy – standards and why coherence in the structure is important. • Material master lifecycles are often not considered- beginning, middle, end. • Conclusions: This is not an insolvable problem or one that necessarily demands another $$$ system implementation. It exists only because it has been invisible  Originally Presented at AB Chapter Online Symposium (Part 1 of 7)  05/28/2020  as "The Story of My Journey With MRO Material Master Identities."  Presented MainTrain 2020  09/15/2020

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including:• Organization policies and objectives• staff turnover• training• reduced staffing• database review audits• outdated software

Asset management processes are becoming more widely adopted across industrial facilities. In this presentation, we’ll examine an essential aspect of the overall process—asset health—to understand the value of using data-centric models and how asset health enables fact-based decision-making at the asset and asset class levels across the enterprise. As a part of Intelligent Operations—a new approach to achieve Operational Excellence—asset health today uses digital transformation to optimize production, minimize equipment downtime, enhance human performance, and manage operational risks. We’ll examine the key asset-related aspects of Intelligent Operations and explore an asset health strategy based on the principles of interoperability and real-time decision support. Outcomes supported include reduced maintenance costs, enhanced asset availability, changes to predictive repair and capital replacement strategies, improved production, and reduced risk.

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 asset-owning organizations engaging in enterprise digital transformations aren’t realizing the expected value. Follow the money and you’ll see asset-owning organizations spending tens of millions (sometimes hundreds of millions) of dollars and the only clear winners are the solution providers and supporting management consultants. Why? Some approaches can lead their organizations further away from achieving their business goals. Ambitious digital strategies are great, but success is best achieved through technology and first-principles practices and decision-making. Paul Daoust will explore a digital transformation strategy that combines technology with asset management and operational excellence practices into an integrated operational management framework that works.

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.