9.1 Information Systems

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report 33% to 50% of their asset and process safety information is either missing, incomplete, inconsistent, or outdated?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

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.

An Intelligent machine can sense its environment, take a decision and apply an action or give a recommendation. How this can transform maintenance? With every Industrial Revolution, Maintenance tools and strategies grow to provide the needed service for this industrial era. Now what shall we do with the Augmented reality and other technologies that Industry 4.0 introduces in the production environment??! It is important to understand what AI -Artificial Intelligence in details is because it is currently part of our work and life even if we do not realize this. When we understand how AI works, we can use it as our ally. Otherwise, we shall resist its existence specially when it starts to give recommendations and report of what went good and what went bad. AI is a title frequently applied to the project of developing systems with the intellectual similar to those of humans, such as the ability to reason, discover meaning, generalize, or learn from past experience. The human maintenance team applies its intellectual process at every situation it encounters. This intellectual process includes the ability to reason, discover meaning, generalize, or learn from experience. How AI does this? It is all based on software and Algorithms. There are two (2) ways for the software to yield intelligent advices or actions. You either add to it all the possible solutions of a problem and the software searches through all possibilities to find a one matching to this situation, then returns the stored actions for this possibility. Alternatively, the other way is to let the algorithms of the intelligent software infer some reasoning based on the inputs then solve the problem. . Let us relate this to maintenance

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.

Presentation highlights the importance and interdependency of three pillars of success (people, processes, and technology) and Cameco’s asset management improvement efforts through each of these, including lessons learned. Audience will learn about the importance of organizational change management, business process management and agile methodologies, some of the technologies supporting asset maintenance and reliability, and its new Asset Management & Reliability Center of Excellence.

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?  

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.