4.03 Asset Information Systems

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report 33% to 50% of their asset and process safety information is either missing, incomplete, inconsistent, or outdated?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

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 will be presenting a case study of why a new mobile Plant Maintenance application was required to replace their Agentry solution with the BlueWorx Plant Maintenance solution for their 1900 technicians at a large oil & gas organization in Canada. The need was to improve: executing work orders, raise notifications, execute proper inspections of their assets and equipment to meet their requirements and utilizing GIS to geo spatially locate work. One of the most important reasons on deciding to displace the Agentry solution is to help alleviate their previous cumbersome and frustrating process of slow transmit times and cryptic errors to a faster sync time combines with an administrator tool that’s an easy-to-follow guided process, to correct any processing error that the technician will encounter on a daily basis, to achieve more accurate data recorded to the backend. The pain points they faced were not having the ability to carry out and do inspections in an efficient manner, major transmit fails because of the non-ability to upload large data points and cryptic errors messages. The key driver for the client is the ability to easily enhance the out of box functionality to suit their business model and needs. This also gave them enhanced field capabilities and access digital documentation to assist in their daily tasks. We will showcase the implementation approach, associated project deliverables and ingredients to making this a success for both the customer and S4A IT Solutions. This was all achieved during an unprecedented pandemic which forced us to deliver this project from many continents, all while being delivered fully remote. What was to be achieved of the new solution? The solution helped transform and improve not only their current in-adequate maintenance solution, but also helped culturally shift, across multiple business units, a non engaged workforce into a fully engaged, collaborative team which resulted in increased efficiency. The new solution has transformed business processes into a paperless workflow to help with waste reduction, regulatory compliance, tool time productivity, downtime reduction and enhanced data driven decision making.

Applying agile data governance and leveraging 21 century tools and methods to create an ecosystem that supports the success of asset data management strategies. This approach addresses challenges in resourcing for developing strong governance that considers strategic, tactical and operational needs while providing a unique approach to data gathering, quality and quantity of data at a program level.

Recent developments in AI, ML and related techniques see wide adoption in many industries. However, in the asset management area, such technical advances are still in their infancy, especially in the maintenance, repair, and operations (MRO) area. Part of the reason is that, contrary to production, MRO data has its unique characteristics (e.g. incompleteness, inconsistency and heterogeneous), and most organizations are still planning to introduce diagnostic sensors dedicated to maintenance and equipment reliability. We face both challenges and opportunities in advancing data-driven continuous improvements within the asset management world. This presentation shares the findings of our current research and development focus. Titled “How to use historical data to find opportunities to improve the effectiveness of equipment reliability programs, optimize MRO inventory operations, and enhance MRO workflow management processes”, we will first examine the characteristics of MRO data as their uniqueness to a specific company, plant or equipment and their commonality across all sectors. Then we evaluate the feasibility of applying AI/ML techniques with MRO history for better operational efficiencies. We need to understand what data is related to human knowledge, human interaction and process, and what data is associated with the actual condition of the asset, and if there are patterns and models that can be learned. Last, we will demonstrate that AI/ML can find equipment agnostic models and patterns which help continuously improve MRO operations across different industries. Based on the findings, we will also show how AI/ML models learned from historical MRO data can be translated into prescribed actions for improvements in equipment reliability, MRO inventory and workflow operations for individual organizations.

Reliability and Maintenance (R&M) teams at manufacturing facilities employ different maintenance strategies on their physical assets to achieve the desired reliability and maximize the availability of the assets. Most of the production downtime in manufacturing facilities is because of unexpected (or) random failures of equipment and the associated reactive maintenance work. One of the factors that affects the total time to fix failed equipment is spare parts availability. The increasing complexity to minimize production downtime with aging assets demands problem-specific decision models. In this study, a multi-criteria decision model is proposed to assist the R&M stakeholders at manufacturing facilities in making decisions on stocking the right parts. The proposed model will help facilities to stock the spare parts required to maintain the system with-in acceptable and manageable risk. Two case studies from a pulp mill will be presented to demonstrate the use of the proposed decision model. The first case study deals with “Pulp Machine Process Area” with historical data on equipment failures and spare parts usage while the second one focuses on a newly commissioned plant without failure information. The proposed decision model helped to identify the right parts to stock and minimized the risk and inventory costs in both cases.

An Asset Health Index or AHI refers to analysis performed using various asset data to determine the state or condition of the asset. AHI can be used to better assess asset condition, used and useful life, progression toward potential failure, and failure probability. Further, using AHI can also enable the development of optimized maintenance and replacement strategies for assets using a set of objective criteria to assess the true health of the asset. However, entities vary widely in whether they develop Asset Health Indexes (AHIs) for their key assets. For those that do, there are marked differences in the level of rigour and sophistication employed in developing and applying AHIs for effective asset management decision-making. AHI calculations involve identifying and collecting data which may include a review of core asset attributes such as manufacturer, inspection data including field observations, destructive and/or non-destructive test data, maintenance data including historical records, operational records, and asset failure/refurbishment data. In other words, some are core inventory data, some work records, and some inspections or tests. This presentation will go through how to make the best use of asset SMEs and how you can start to develop useful AHIs from what you already know/have. Technically, the process begins with identifying the most critical assets and determining which can best benefit from AHI formulation development. The next steps are used to develop proposed condition factors (CF) and weighting factors (WF) that provide insight into the condition of the assets. Finally, CFs and WFs are used to develop a mathematical algorithm or formulas for the Health Index. We will also discuss how AHI can be used to develop asset management and maintenance strategies – the whole point of the data and analysis in the first place.

The successful use of the technologies associated to Building Information Management (BIM) depends on the interest and levels of investment that owners are willing to put into their projects. According to U.S. and Australian studies, the costs of poor information management in construction for each of these countries are nearly 15 billion U.S. $. The largest losses (almost two-thirds) were found among property owners. The implementation of BIM technologies for facility management focuses mostly on the technological aspect and often neglects the change management required to migrate from traditional approaches to asset management processes. BIM leverages the generation and use of digital representations of buildings and infrastructures in design, construction, and operations. The cost, efficiency and communications benefits that accrue from fostering single source of truth integrated data sets throughout infrastructure project lifecycles are forcing engineering firms, construction companies and public policy offices to rethink their processes and actions. The biggest potential opportunity for leveraging BIM processes following design and construction is for Facilities and Assets Management. Potential benefits include higher quality overall results, improved data preservation and transfer between life-cycle actors, effective predictive maintenance and energy efficiency. Leveraging the benefits of BIM technologies is easier said than done. There are few generally recognized best practices and many outstanding questions. How can we better plan the integration of BIM and FM into future projects? How can we integrate BIM into the management of existing infrastructure and real estate inventories? What best practices can we learn from existing global trends? This presentation offers some insights on how to transition towards BIM-enabled facility management. Success on this digitization path requires strong leadership from owners and operators, from project inception to operations phase. It investigates the transfer process of information technologies in place as well as changes in the business culture and organizational structure through case studies. Ultimately, a robust process to seamlessly create and transfer data across a facility lifecycle lays the ground for leveraging advanced Construction 4.0 technologies to further optimize the operations and improve the occupancy conditions for facility users.

Let us look at an ordinary omnipresent part of Asset Management: bolting and fastening the machinery or structure. This single vital activity turns out to be remarkably neglected with undesirable consequences for reliability. Motion Amplification®, a new technology has the capacity to show machinery and structural movements, and so naturally brings out looseness or unnatural behaviors. Fastening errors seem to be one of the most prevalent occurrences and it is therefore important to look at what we can learn and how the technology can drive improvements and uses of best practices. A variety of problems are shown, such as forgotten difficult to access bolts, rusted/corroded fasteners, improper tension, improper washers, bolt grade or size and custom modifications such as a bunch of small shims. Some of the issues can be attributed to design errors, some to lack of asset information provided, some to installation errors, lack of training and proper tools or a culture of installation and maintenance excellence. Sometimes and improvement push comes with the integration of more advanced tools, for example tools exist where the torque can be properly managed. This requires that many other items come into place, such as the missing information, correct parts, training and cultural to get the job done properly.