9.0 Information Management General

SIGGA Technologies deployment with Votorantim Cimentos SAP PM

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.

Digitalization for sustainable operations. This presentation will discuss digitalization for sustainability, discuss the hype cycle for AI/ML lead initiatives, and provide a road map for real-world actions. Actions must include a bottom-up approach to delivering sustainable value and return on investment for a tactical realization of your organization’s ESG sustainability goals. A case study will show how a digitalized reliability initiative improved asset availability by 25% and reduced flaring by more than 80% over the past five years.

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.

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.

Our collective goals are to operate and maintain the facility in a safe, compliant, effective, and socially responsible manner to sustain a 100% commercial availability. There is no greater impact to these goals than the people directly responsible for the Operation and Maintenance at the Facilities. These goals are met through a combination of strategies that involve a myriad of challenging communications. More than anything, these communications require a complimentary skillset not necessarily honed by todays skilled craftsmen, soft skills. They may take the form of questioning the status quo, holding each other accountable, or courageous conversations. Essentially, we want teams who will listen and understand the facts of the whole situation at hand, and work together to elevate to the occasion, thereby addressing the failure or opportunities for improvements to the absolute best of their collaborative abilities. In addition to having the Technical skillset to operate and maintain our critical assets, our high functioning Teams need to contribute and extrapolate their soft skills by:-    Creating a psychologically safe space to work to collaborate and challenge -    Know, understand, believe the facts, then Act -    Fostering an Environmental & Safety conscious workplace -    Empowering each other, challenging the status quo and continuous improvement Teams that can leverage these 4 key strategies are able to rise to greatness at the most critical moments and inspire each other to do their very best work. As such, the focus of this MMP Capstone will be the strategic investment and development of our Teams’ communications skillset.  We will explore the many communications related failures that I have personally experienced and dissect individually the cost of each such failure, thereby quantifying the cost of poorly managed communications and, the cost benefit to investing in our greatest asset; Our People. 

Is your company struggling to forecast, and have parts arrive on time to perform preventive maintenance, or know when downtime should occur that would be least disruptive? Would your company benefit from more accurate cost predictions and a decrease in last minute emergency breakdowns? Or perhaps you cannot depend on what the system has for inventory or how many staff are required on site at a given time – there are somehow too many or not enough at any given time. These are common issues faced by many large manufacturers from Marine, to Mining to Oil & Gas, and many others. This session will provide an overview of how basic Machine Learning techniques can be applied to Supply Chain, Reliability, and Asset Management to gain increased insight, and better overall performance. But what is Machine Learning? It is a subset of Artificial Intelligence, where algorithms improve through experience (new data sets). The algorithms constantly evolve as more and more data is run through it. Machine Learning is useful for finding unknown patterns and relationships in data, such as sales, plants, store, or forecasting. It is an effective tool to gain insight and efficiency in day to day operations, while also providing a future forward view.

In 2020, as the COVID-19 pandemic gripped the world, the foundations of business operations shifted instantly. With ripple effects continuing today, organizations continue to strive to adapt to the changing landscape. In these times, the principles and practices of asset management are more critical than ever and can be a framework for embedding resiliency. This presentation will delve into the relationship between asset management principles and three focus areas impacted by COVID, while sharing real life examples from water and wastewater operations. First, we will explore how facility lockdowns and supply chain disruptions impacted equipment repair parts availability. We will dive into how facilities with strong critical spare parts management techniques weathered these disruptions with less panic and time spent chasing parts. And, how techniques such as predictive maintenance programs and planning scheduling better accommodated longer lead times before equipment intervention was required. Second, we will discuss what happens when only a few individuals own the asset management program and they are suddenly absent for health or family support obligations for an extended period. We will interconnect how building a strong ownership of asset management across disciplines and embedding processes and procedures ensures a lasting culture of asset management. Finally, lockdowns and travel bans forced our team to explore new methods for deploying technical support without onsite visits. Innovative approaches from virtually facilitated risk review workshops to augmented reality condition assessments have been developed and implemented over the past year. With advancements in technology, as the accumulation of data grows there has been a concentrated effort to leverage this data toward making data-justified decisions. Over the past year there has also been an increasing emphasis to leverage these analyses to bring decision teams to agreement as managers and client interact virtually. With an interdependent global economy it is only a matter of time until the next disruption hits, and implementing the core principles of asset management can enhance the resiliency of your organization.

The Global Forum on Maintenance & Asset Management and highlights of the second edition of it’s recently published Maintenance Framework, including the project to develop the document and differences from the first edition.