Empowering EAM users to be power users with self-service AI
By Kevin Price
Managing the lifecycle of the many varied assets that make up U.S. infrastructure is a complex task, with no easy answers in sight. Most would agree that the old-school “don’t worry about it until it breaks” approach of the post-war building boom was sadly inadequate. The public is paying for yesterday’s short-term thinking today.
To end this deterioration cycle, maintenance teams must up-level their strategies and leverage software technology to its fullest advantage. Turning to Enterprise Asset Management (EAM) solutions for operational improvements is the foundation-building step.
Public agencies and municipalities will be able to better manage resources, prioritize preventive care, budget for long-term projects, and monitor the State of Good Repairs. But even EAM solutions deployed in the last decade need to be refreshed and upgraded to next-generation functionality. If state and local governments are going to get ahead of the escalating deterioration of the infrastructure, transformative tools such as artificial intelligence (AI), machine learning (ML), and predictive science will need to be leveraged. Using a metaphor to explain the step-up, these advanced tools — and the insights they bring – are like turning a gasoline-powered lawn mower engine into a turbo-charged rocket engine.
Maintenance teams shouldn’t resign themselves to basic solutions, poorly integrated networks, and add-on third party mobile solutions. Nor should they assume all advanced solutions are hard to implement.
Work Smarter, Not Harder
Maintenance teams must boost productivity and efficiency. To fully optimize resources, including technician time, teams must be able to drill into influencing factors behind assets failures, understand the variables impacting asset lifecycle, and proactively set aside resources to conduct inspections of the complete asset and its various parts and components, looking for early warning signs of failures. The detailed asset profile must be captured so the current status as well as future availability can be recorded, monitored, and analyzed.
The analysis is the key part of the next generation approach. Business intelligence (BI) tools using AI technology can identify patterns and relationships that the typical human will miss. ML technology helps the system discern if the patterns warrant action, or if they are “acceptable” variations from the norm. A designated training period allows users to give feedback to the system. The more data the system has, the more accurate it is likely to be.
Operationalizing AI is important so that more organizations and users can take advantage of the technology. The user-interface is where usability begins. But, empowering users goes beyond eye-pleasing screens and intuitive navigation.
Just like counterparts in the commercial sector, teams in the public sector work hard to meet the expectations of customers, the constituents who rely on the infrastructure every day. Constituents approve spending at the ballot box. Proposals for modernizations, upgrades, and replacements of assets face rigorous scrutiny. Public sensitivity to government overspending can deter even much-needed investments.
Undergoing a major organizational change – such as turning to data-driven insights generated from an IoT application – can be a transformative shift in workflows and attitudes. Even adopting mobile solutions for field maintenance can be an intimidating prospect for senior technicians. Many modern solutions, though, are so user-centric and intuitive that resistance typically dissipates once users are exposed to the user interface. Tools which guide best practices, automate tedious steps, and provide relevant role-based information to assist decision-making help win over even the toughest cynics.
Solutions which already have industry-specific functionality built-in also help streamline the implementation stage. The industry-specific features eliminate the need for modifications that can become restrictive tangles impeding future upgrades. Software designed specifically for the public sector and EAM applications will help government agencies and municipalities effectively apply the newest technologies.
Advanced EAM solutions deployed in the cloud can be up and running in weeks, not months, quickly achieving results. The solution will help focus on prescriptive management of assets, anticipating maintenance needs and elongating the lifecycle of critical machinery through attentive preventive care.
Implementation accelerators and pre-populated templates make it easier for users to apply repeatable models for AI applications and self-service BI. No longer is AI a luxury that only capital-rich enterprises can enjoy. Government agencies, too, can use proven, integrated solutions to design their own data-centric reporting and predictive insights.
The state of the U.S. infrastructure is alarming. A new focus on simplifying implementation and improving productivity is essential. Fortunately, some innovative EAM solutions now include tools that empower users to apply advanced technologies such as AI, ML and IoT. These capabilities provide more opportunities for leveraging data and improving the commitment to repairing and protecting the infrastructure. Now, maintenance teams can step-up their efforts—leveraging powerful tools to make the job easier.
Kevin Price is the Technical Product Evangelist & Product Strategist for Infor EAM. To learn more, please visit www.infor.com.