Managing and maintaining a complex system of assets for daily operational success can be challenging. Organizations face the ongoing task of consolidating information from multiple sources, addressing real-time issues, and prioritizing innovation. The costs associated with unplanned downtime are rising, skills shortages are impacting productivity, and traditional maintenance practices are being challenged by sustainability initiatives.
Asset lifecycle management (ALM) encompasses various strategies aimed at prolonging asset lifespan and enhancing operational efficiency, representing a comprehensive approach to strategic asset management. IBM leads the ALM market, as recognized by IDC in their latest Worldwide Semiannual Software Tracker®. Adopting ALM practices involves transitioning maintenance strategies from reactive to predictive, utilizing technology to proactively monitor asset conditions and performance rather than responding to failures.
A notable example of ALM in action is Transport for London (TfL), which is optimizing public transportation assets such as buses, boats, bikes, and the tube. IBM’s technology aids TfL in preemptively addressing issues and extending asset lifecycles, thereby reducing the need for replacements and mitigating the risk of major failures. TfL estimates a net savings of GBP 21 million over the next decade solely for its London Underground operations.
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While this example highlights ALM’s potential benefits, there are numerous opportunities to save time, reduce costs, and decrease emissions. The latest release of IBM Maximo Application Suite (MAS) version 9.0 introduces innovations designed to optimize asset lifecycles and address evolving business needs through enhanced data and AI capabilities.
Generative AI has the potential to significantly enhance asset lifecycle management, offering organizations increased operational efficiency, asset reliability, and overall success. Maximo Work Order Intelligence, now available in MAS version 9.0, utilizes IBM Watson™ generative AI capabilities to expedite work order approval processes, improve data quality, and provide highly accurate failure code recommendations. We are continuing our collaboration with IBM Research® to unlock additional value from generative AI within our asset management solutions.
One common challenge in maintenance management is accurately assigning problem codes with limited information, leading to delays, mistakes, and unnecessary resource expenditures. Maximo Work Order Intelligence addresses this issue by leveraging generative AI to enhance incomplete data. An AI model trained on work order descriptions generates recommendations for the most probable problem code, enabling maintenance managers to swiftly identify issues, assign appropriate codes, allocate technicians efficiently, and reduce troubleshooting time.
This feature accelerates work order approval, optimizes maintenance resource allocation, and minimizes errors. Additionally, as additional details are added to the work order, the AI recommendation is updated with the latest information to further enhance its reliability. Consequently, organizations can reduce maintenance errors, eliminate unnecessary operations, lower material costs, and enable maintenance personnel to allocate their time more effectively.
Maintaining assets effectively requires clear visibility into their current condition, a task made challenging by the diverse range of equipment and facilities managed across various locations.
Introducing Maximo Field Service Management, a new solution in the MAS version 9.0 release, designed to enhance client capabilities in delivering exceptional field service experiences. This technology equips maintenance and dispatch teams with advanced scheduling and intelligent dispatching functionalities. Leveraging advanced algorithms developed by IBM Research, Maximo Field Service Management increases operational efficiency by enabling the system to handle a tenfold increase in work orders. It also improves metrics such as jobs completed, total resources utilized, and job completion times by more than 10%.
Through this collaboration, the solution considers factors like technician location, availability, skills, and expertise to schedule and dispatch personnel optimally. Technicians can access crucial asset information remotely, facilitating quicker issue resolution and enhancing overall service efficiency
One of the significant challenges companies face is determining the optimal timing for maintenance of their assets. Traditional preventive maintenance approaches often rely on reactive or scheduled methods, which can result in unnecessary downtime and expenses. While optimizing preventive maintenance can yield substantial cost savings, it is typically labor-intensive and may not deliver accelerated returns on investment.
To address these challenges, Maximo Reliability Strategies introduces a new solution that empowers organizations to analyze failure modes and access a comprehensive library of asset-specific failure details and mitigation activities. This functionality simplifies the creation and optimization of highly tailored maintenance reliability strategies. Additionally, we are enhancing client capabilities by enabling them to create, import, and modify Failure Mode and Effects Analysis (FMEAs) within this Maximo release.
This technology plays a crucial role in helping companies develop more reliable assets that are less prone to unexpected failures. In MAS version 9.0, we collaborated closely with IBM Research and leveraged Watson™ generative AI to expand the FMEA library, allowing for faster and more effective development of FMEAs specific to any industry’s assets. This capability is currently available for technical preview and will be fully integrated into the next MAS update, enabling clients to build FMEAs with greater efficiency.
By integrating innovations from across our business, we deliver comprehensive solutions that drive significant value for our customers, advancing towards AI-powered Asset Lifecycle Management (ALM).
As industries pursue growth and profitability, they are increasingly challenged to manage operational emissions effectively. Tools like Maximo Emissions Management, now part of the Maximo Application Suite, play a crucial role in balancing operational efficiency with environmental responsibility. This integrated solution enables enterprises to monitor both continuous and fugitive emissions in near real-time, facilitating compliance management. It also supports operational emissions reporting and enhances corporate sustainability tracking through seamless integration with IBM Envizi ESG Suite.
Additionally, we are empowering organizations with our new cloud-based data platform, Environmental Intelligence. This platform provides comprehensive environmental data and insights that help organizations mitigate the environmental impact of their assets and establish more climate-resilient operations. Together, these technologies offer a robust framework for achieving greater efficiency, responsibility, and sustainability in business operations.
Installing IBM Maximo APM - Asset Health Insights
IBM Maximo Application Suite Overview
In today’s fast-paced world of data analytics and AI, optimizing your data infrastructure is key to unlocking valuable insights and driving innovation.
In today’s fast-paced world of data analytics and AI, optimizing your data infrastructure is key to unlocking valuable insights and driving innovation.
In today’s fast-paced world of data analytics and AI, optimizing your data infrastructure is key to unlocking valuable insights and driving innovation.
At Pragma Edge, we are a forward-thinking technology services provider dedicated to driving innovation and transformation across industries.
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IBM Partner Engagement Manager Standard is the right solution
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IBM Partner Engagement Manager Standard is the right solution
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