Asset-intensive industries have faced numerous challenges in 2023. However, with advances in Enterprise Asset Management (EAM), including AI, organizations are well-positioned to enhance their operational efficiency in 2024.
In his 2024 predictions, Jon Mortensen, Global CTO EAM, discusses three trends that will fuel the adoption of EAM in asset-intensive industries.
1) In 2024, ISO 55000 certifications will increase to unprecedented levels
The ISO 55000 series of standards is focused on asset management, serving as a collaborative, worldwide response for organizations to manage assets better and improve performance. The current series was originally published in 2014.
In mid-2024, it is expected that a new series will be published (ISO 55000-2024), incorporating six standards, including:
- ISO 55001, Asset Management – Asset Management System – Requirements
- ISO 55002, Asset Management – Management Systems – Guidelines for the Application of ISO 55001
- ISO 55010, Asset Management – Guidance on the Alignment of Financial and Non-Financial Functions in Asset Management
- ISO 55011, Asset Management – Guidance for Development of Public Policy to Enable Asset Management
- ISO 55012, Asset Management – Guidelines for Enhancing People Involvement and Competence
- ISO 55013, Asset Management – Guidance on Data for Asset Management
The cadence of the refresh aligns with the typical 10-year cycle, where early adoption evolves to be generally accepted within a given industry.
While the ISO 55000 series is considered the global standard for asset management, certification numbers reflect a mixed adoption model. Many organizations stop short of certification, instead working in alignment with the standards. This trend is reflected in the certification numbers by region, for example, with ISO 55001.
Known organizations certified in ISO 55001:
With the release of the 2024 series, many organizations will formalize their certification status with the new standards, increasing adoption exponentially.
By following established ISO best practices, these companies benefit from increased productivity, improved revenues, access to new markets that adhere to specific standards, and continuity in operational practices across the organization.
2) In 2024, artificial intelligence will become more commoditized, with increased access and less reliance on in-house experts and support
Most companies that rely on asset performance management (APM) are already using artificial intelligence embedded within the technology to increase operational efficiencies.
In fact, artificial intelligence and asset performance management systems are well aligned, relying on business and operational data to perform optimally—data sources that are growing rapidly.
In 2023, it was predicted that the world would generate 120 zettabytes of data, up 23.71% from the previous year. In 2024, this is expected to increase to 147 zettabytes. These data-rich environments are perfect for AI and its associated pattern recognition capabilities.
By working together, the systems identify vital inputs from various sources, working in real-time to optimize performance management while providing accurate predictive, diagnostic, and forecasting analytics.
Yet expanding the application of artificial intelligence beyond these use cases has traditionally been the domain of organizations with the requisite resources and infrastructure.
After all, funding a team of data scientists, machine learning engineers, and big data analysts is not for the weak of heart (or budget).
Fortunately, the adoption of artificial intelligence continues to grow. In 2023, 35% of companies were actively using the technology, with 42% exploring it for future implementation.
As the infiltration of artificial intelligence accelerates within the enterprise—especially with the introduction of pre-trained models and self-learning algorithms—so too does commoditization, making it easier and less expensive for all organizations to leverage the technology.
In 2024, asset-intensive organizations will extend existing artificial intelligence capabilities, using the technology to power new use cases. For example, early anomaly detection creating a “normal” baseline status for every asset, operational workflow, and all anticipated outcomes.
Using these baselines, the artificial intelligence will automatically detect anomalous conditions that do not conform with the standard, determining the cause—and, most importantly—driving actions from data.
3) In 2024, maintenance technicians will spend less time in front of a screen than ever before
As organizations grapple with the chronic global skills shortage, retaining maintenance technicians and other specially trained employees has become a business imperative.
In fact, in a recent survey by Gallagher, 51% of organizations report that retaining talent is a top operational priority, more so than outgrowing revenue or sales (47%), maintaining or decreasing overall operating costs (29%), and ensuring business continuity (24%).
With talent in short supply, technician bandwidth must be prioritized, with companies leveraging technology to offload as much administrative and other “screen time” work as possible.
In 2024, with help from intelligent systems that incorporate artificial intelligence, automation, and rich business and operational data, technicians and other specialized workers will achieve near-zero screen time. Instead of writing reports, filing work orders, and spending time determining the root cause of a failure, these people will focus on doing what they do best: serving customers and maintaining the integrity of the company’s infrastructure.
With artificial intelligence, the system predicts an imminent asset failure after analyzing information from asset performance management (APM), enterprise asset management (EAM), and other data sources.
Instead of relying on humans to advance the situation, the intelligent system automatically determines the root cause, generating a corrective work order, and executing critical parts planning and ordering for all components.
A good example is IFS customer Holmen, a producer of timber, wood products, and various paper and packaging products. The company uses artificial intelligence and machine learning to digitalize its mills and support preventive maintenance as well as other data-driven processes.
“We will use IFS Business Connector alongside sub systems and solutions including AI and ML to support connected machines and improve predictive maintenance. For example, we see AI collect data externally, and then flag the need for maintenance on a specific object in a few months’ time, automatically generating the work order. IFS will be the center of information for our digitalized mills.” David Lyrén, Technical Manager, Holmen Paper, Hallsta Mill Read more.
With artificial intelligence, maintenance technicians are no longer tied to legacy form processing. By the time a worker is tapped for the job, all supporting tasks are complete so they can focus exclusively on the job at hand.
Navigating 2024 and beyond:
As we enter 2024, the possibilities for Enterprise Asset Management are growing at a rapid pace. With the advent of technologies like AI fueling innovation and driving digitalization – asset-intensive industries have the potential to drastically enhance their operational efficiency.
By leveraging EAM, businesses can streamline their asset management processes, optimize maintenance schedules, and reduce downtime. Ultimately, this will lead to increased productivity, improved safety and enhanced profitability.
SOURCE: Montensen J. (2024,Jan22). IFS Predıctıons 2024: Enterprıse Asset Management. IFS Blog. https://blog.ifs.com/2024/01/ifs-predictions-2024-enterprise-asset-management
