Coming to Grips with AI-Driven Asset Management and Maintenance

Industrial firms are increasingly slated to invest in AI-powered innovation for long-term asset reliability.

Traditional asset management, which often implies running assets to failure, is no longer sustainable. Slipping into reactive mode has proved to have catastrophic unintended consequences for asset-intensive organizations—leading to higher costs, greater material waste, and limited production.

Industrial AI technology is, conversely, putting a strong emphasis on prevention—not reaction!—to help maximize production uptime while driving down costs. AI techniques not only provide predictive analysis but also contextual information into the real-time condition and performance of specific equipment, further aiding firms in accurately diagnosing assets, predicting equipment failures, and averting disruptions.

Understanding the need for change is a primary step in efforts to transition from traditional to AI-powered asset management and maintenance. But AI adoption undertakings are often riddled with misconceptions. Prior to undergoing this change, many asset managers are racing to get a clearer perspective on the utilization of AI in asset management.

A Peek into AI-Driven Asset Management Practices

Intelligent machines essentially replicate—and most importantly, augment!—the knowledge of asset managers in making smarter decisions that lead to well-functioning systems. AI presents an opportunity to advance the inherent ability of an industry player in anticipating future breakdowns—and a new way to reconstruct the strategic rationale behind an asset management practice.

When AI sits firmly at the core of asset management, industry players expectantly rely on this technology to capture and sort through large data sets to primarily:

• Identify the potential failure behaviors of given equipment.
• Make informed decisions on what maintenance to perform on each individual asset—and how often.
• Optimize predictive maintenance to prevent unplanned downtime.
• Tweak service models to enhance reliability, ensure timely delivery, and improve the customer experience.
• Improve risk management processes and address worker safety issues in high-risk environments.

Predictive information plays a central role in asset-centric industries as it helps firms to avoid reactive responses by anticipating issues based on previous asset behavior patterns and historical data. Particularly in the current climate, taking preventive actions is—perhaps!—the only conceivable choice. AI delivers immediate insights into asset performance optimization, allowing industry players to monitor high-value assets remotely and make necessary adjustments even as the ongoing COVID-19 pandemic unfolds.

Driven by an AI-equipped system, asset managers can do more than turn rich insights into greater prevention efforts, healthier assets, or increased uptime. AI provides asset managers with better tools to mitigate operational risks—at a fraction of the cost. Research indicates that AI is opening up new possibilities for cost efficiency:

“70% of operations leaders expect AI to deliver the next wave of cost reductions to the asset management industry.” — Accenture, The Power of Data-Driven Asset Management

Digitizing plant operations is indeed proclaimed to drive down costs and, as research data is additionally showing, promote smart energy consumption. In consequence, organizations are increasingly talking up intelligent asset management, seeing it as part of the broader effort to achieve operational resilience and excellence without the costly hassle of constantly putting out fires.

AI-Powered Strategic Maintenance Steps to the Fore

Each asset needs to be continuously monitored for potential performance issues, yet manual inspections and paper-based systems are not just resource-consuming but also tremendously unreliable. Fortunately, technology is opening up new possibilities—enabling industry leaders to automate routine inspections in order to operate and maintain assets at higher performance levels and lower costs.

Industrial assets equipped with sensor data, operational information, and advanced analytics facilitate a comprehensive—and real-time!—view of assets, allowing for:

• Detailed and highly precise condition assessments
• Elaborated failure prediction analysis
• Optimized maintenance intervals

AI is guiding a shift in maintenance behaviors as it supports a more intelligent approach to maintaining asset reliability and availability. Smart maintenance, enabled by AI technology and advanced analytics, essentially empowers asset-intensive firms with the ability to predict system malfunctions. Building on the insights from AI investigations, managers who monitor asset health and behavior can detect deviations, prioritize resources, and make better decisions on maintenance work.

A higher maintenance maturity will yield important results, with asset managers:

• Achieving uptime improvements
• Avoiding unplanned outages
• Extending the lifetime of aging equipment
• Reducing maintenance costs
• Increasing output

AI inherently maps out a higher quality of maintenance. Advanced technology provides real-time and historical insight into asset behavior while simultaneously extending the firm’s predictive analysis efforts to maintain the top functionality of equipment at all times. Well-maintained equipment runs at peak performance—and this presents industrial firms with the opportunity to spur operational efficiency and increase machine ROI.

The Path to Intelligent Asset Management and Maintenance Goes Through High-Quality Data

As the global economy restarts, those who lag far behind the AI adoption trend will have to rethink where their priorities lie.

A well-planned asset management and maintenance practice will prolong asset life, enhance production capacity, and improve customer service efficiency. Yet taking the plunge to invest in AI for asset management and maintenance isn’t just about pouring resources into new technologically-enabled strategies for long-term resilience. The success of such initiatives rests predominately on data integrity. Industry experts echo this view, stating that building a comprehensive data strategy is necessary to support an AI infrastructure that is primed for investment:

“An overarching industrial data strategy is required—companies need accessible, valuable data that can be leveraged constructively by industrial AI.” — Adi Pendyal, Senior Director of Market Strategy at AspenTech

Firms taking a strategic approach to managing their assets amplify the reliability and robustness of AI through high-quality data. Without a reliable data strategy to support all AI initiatives, industry players may potentially walk on a contentious path to innovation. The transition to an AI-first approach typically requires a complete redesign of the firm’s operating model. Data is, therefore, essential for propelling the major shift towards technology-powered operations and motivating the use of AI for asset management and smart maintenance. Trusted data also plays a critical role in gaining executive-level trust for technology acceptance, as industry players can use data-generated insights to fill in the gaps around AI investments.