From Reactive to Proactive Operations: The Role of LLM in Predictive Maintenance.

The industrial landscape has undergone significant transformations over the decades, evolving from reactive maintenance approaches to more sophisticated preventive and predictive methodologies. Traditionally, maintenance operations followed a reactive model, where equipment was repaired only after failure occurred, resulting in costly downtime, emergency repair expenses, and potential safety hazards. This inefficient approach gradually gave way to preventive maintenance schedules, where maintenance activities were performed at predetermined intervals regardless of the actual condition of the equipment. While this represented an improvement, it often led to unnecessary maintenance activities and failed to prevent unexpected failures occurring between scheduled maintenance periods. The emergence of predictive maintenance strategies introduced the concept of condition-based maintenance, leveraging sensor data and analytics to predict when equipment might fail and scheduling maintenance activities accordingly. However, the true revolution in maintenance operations has arrived with the integration of Large Language Models (LLMs) into predictive maintenance frameworks. These advanced artificial intelligence systems have transformed the maintenance landscape by introducing unprecedented capabilities in data interpretation, pattern recognition, anomaly detection, and decision support. By processing vast amounts of structured and unstructured data from multiple sources, including equipment sensors, maintenance logs, operator notes, and technical documentation, LLMs can identify subtle patterns and correlations that would be impossible for human analysts to detect. This integration represents a paradigm shift from reactive to truly proactive operations, where potential issues are identified and addressed before they impact performance or lead to failure. The sophistication of LLM-enhanced predictive maintenance extends beyond simple fault prediction to comprehensive asset lifecycle management, maintenance optimization, and continuous improvement processes. As organizations increasingly recognize the competitive advantages offered by these technologies, the adoption of LLM-powered predictive maintenance solutions continues to accelerate across industries, from manufacturing and energy to transportation and healthcare. This transformative approach promises not only to reduce maintenance costs and downtime but also to enhance operational efficiency, extend asset lifespan, improve safety, and drive innovation in maintenance practices.
Natural Language Processing: Unlocking Insights from Unstructured Maintenance Data
The maintenance domain has historically grappled with the challenge of extracting actionable insights from vast repositories of unstructured data - maintenance logs, technician reports, equipment manuals, and operator observations - all containing valuable knowledge about equipment behavior, failure patterns, and effective interventions. Large Language Models have revolutionized this landscape through their remarkable natural language processing capabilities, enabling the systematic extraction, interpretation, and utilization of this previously untapped wealth of information. By applying sophisticated semantic analysis and contextual understanding to unstructured maintenance documentation, LLMs can identify recurring issues, successful repair strategies, and early warning indicators that might be buried within years of accumulated reports. The ability to process and interpret technical jargon, specialized terminology, and domain-specific language patterns allows these models to bridge the gap between human expertise and machine learning systems. For example, an LLM can analyze thousands of maintenance reports to recognize that certain phrases or descriptions consistently precede specific types of failures, effectively translating the experiential knowledge of seasoned technicians into quantifiable patterns that can be incorporated into predictive algorithms. This linguistic analysis extends beyond simple keyword matching to understand complex causal relationships, temporal patterns, and contextual factors that influence equipment performance and failure modes. The contextual understanding capabilities of LLMs enable them to differentiate between similar descriptions that have different implications based on their specific context, equipment type, operating conditions, or other factors. Furthermore, these models can integrate their analysis of unstructured text data with structured sensor readings and operational parameters to develop more comprehensive and nuanced predictive models. By correlating subjective observations from operators with objective sensor measurements, LLMs can identify early warning signs that might not be apparent from quantitative data alone. Additionally, LLMs excel at knowledge extraction from technical documentation, automatically parsing complex equipment manuals, engineering specifications, and manufacturer guidelines to create structured knowledge bases that inform maintenance decisions and enrich predictive models with design parameters, operational limits, and recommended procedures. This capability essentially automates the traditionally labor-intensive process of building domain-specific knowledge bases for maintenance decision support systems, allowing organizations to rapidly implement sophisticated predictive maintenance programs without lengthy knowledge engineering processes.
Advanced Pattern Recognition: Detecting Complex Failure Precursors in Multimodal Data
The superiority of LLM-enhanced predictive maintenance systems lies in their extraordinary capacity to detect intricate patterns across diverse data modalities that would remain invisible to conventional analytical methods or human observers. Unlike traditional approaches that often rely on predefined thresholds or simple statistical models, LLMs excel at identifying complex, non-linear relationships and subtle correlations that precede equipment degradation or failure events. These sophisticated models can simultaneously analyze multiple data streams including vibration signatures, acoustic emissions, thermal patterns, electrical parameters, and process variables, recognizing how these diverse indicators interact and collectively signal impending issues. The multimodal analysis capabilities of LLMs represent a significant advancement over single-parameter monitoring systems, enabling the detection of compound failure modes that manifest across different sensor types and operational domains. By correlating seemingly unrelated phenomena, such as minor increases in power consumption coinciding with subtle changes in vibration frequency and slight temperature variations, LLMs can identify specific failure patterns long before they would trigger conventional alarms. This early detection window expands the opportunity for intervention, transforming what might have been an emergency shutdown into a planned maintenance activity with minimal operational disruption. Furthermore, LLMs demonstrate remarkable temporal pattern recognition abilities, identifying not just instantaneous anomalies but evolving patterns that develop over extended timeframes. These models can detect gradual degradation trends, cyclical patterns, and intermittent anomalies that might appear insignificant in isolation but represent meaningful indicators when analyzed in their temporal context. For instance, an LLM might recognize that a particular sequence of minor operational deviations, occurring in a specific order over several weeks, consistently precedes a major equipment failure, even though each individual deviation falls within normal operating parameters. The transfer learning capabilities inherent to large language models further enhance their pattern recognition prowess, allowing them to apply knowledge gained from one equipment type or failure mode to similar situations across different assets or even different industrial domains. This cross-domain pattern recognition enables faster implementation of predictive maintenance for new equipment or processes by leveraging existing knowledge bases rather than requiring extensive historical failure data for each new asset. Additionally, the continuous learning architecture of advanced LLM systems enables them to refine their pattern recognition capabilities over time, incorporating new operational data and maintenance outcomes to improve prediction accuracy and adapt to changing equipment conditions, operational parameters, or environmental factors.
Anomaly Detection and Root Cause Analysis: Beyond Alerting to Understanding
The implementation of LLM technology in predictive maintenance transcends traditional anomaly detection by not only identifying deviations from normal operational patterns but also providing sophisticated root cause analysis that illuminates the underlying factors driving these anomalies. Conventional anomaly detection systems typically flag deviations from established norms without offering deeper insights into causality, leaving maintenance teams to investigate the source of the problem through time-consuming diagnostic procedures. In contrast, LLM-powered predictive maintenance solutions deploy contextual reasoning frameworks that automatically analyze the relationships between observed anomalies and potential causal factors, drawing from both historical patterns and comprehensive equipment knowledge bases. This capability enables the system to not merely alert maintenance personnel to an emerging issue but to suggest the most probable underlying causes and failure mechanisms, significantly accelerating the diagnostic process and reducing the expertise barrier for effective maintenance interventions. The advanced anomaly detection capabilities of LLMs are particularly evident in their ability to distinguish between genuinely problematic deviations and benign variations resulting from changes in operational parameters, environmental conditions, or production requirements. This contextual discrimination dramatically reduces false positives compared to threshold-based systems, ensuring that maintenance resources are directed toward genuine issues rather than normal operational fluctuations. By integrating operational context into anomaly evaluation, these systems can determine whether an unusual vibration pattern represents an impending bearing failure or simply reflects a temporary change in production specifications, preventing unnecessary maintenance interventions while still catching critical issues. Furthermore, LLMs excel at hierarchical anomaly analysis, recognizing how component-level anomalies might cascade into system-level failures and vice versa. This multi-level perspective allows maintenance teams to identify whether an observed issue represents a localized component problem or a symptom of a more widespread system failure, ensuring that interventions address the primary failure point rather than merely treating symptoms. The counterfactual reasoning capabilities inherent to advanced LLMs enable these systems to explore hypothetical scenarios and failure pathways, comparing observed patterns against various failure hypotheses to determine the most plausible explanation for detected anomalies. This approach mirrors the diagnostic reasoning of experienced maintenance engineers, who mentally test different failure theories against observed symptoms to identify the most likely cause. Additionally, the natural language generation capabilities of LLMs transform complex anomaly analyses into clear, actionable explanations delivered in human-readable formats, bridging the gap between sophisticated AI analytics and practical maintenance execution. Rather than presenting maintenance technicians with abstract statistical indicators or complex model outputs, these systems can generate concise narratives explaining what is happening, why it matters, what is likely to happen next, and what actions should be taken to prevent failure.
Prescriptive Maintenance Recommendations: From Insight to Action
The transformative power of LLM integration in maintenance operations extends beyond predictive analytics to prescriptive guidance, closing the loop between insight and action by translating complex diagnostic findings into concrete, contextually appropriate maintenance recommendations. This prescriptive dimension represents a crucial advancement over traditional predictive maintenance approaches, which often identify potential issues but leave determination of optimal intervention strategies to human experts. LLM-enhanced systems leverage their comprehensive understanding of equipment characteristics, operational history, failure modes, and maintenance procedures to automatically generate tailored maintenance recommendations that specify not only what requires attention but also how to address it most effectively. These sophisticated systems consider multiple factors when formulating maintenance prescriptions, including the criticality of the affected equipment, availability of replacement parts, required technical skills, estimated repair time, production schedules, and potential operational impact of both the failure and the maintenance intervention. This holistic approach ensures that maintenance recommendations balance technical necessity with operational and business considerations, optimizing overall organizational outcomes rather than focusing narrowly on equipment metrics. The decision support capabilities inherent to advanced LLM applications enable the evaluation of multiple potential maintenance strategies through simulation and comparative analysis, weighing factors such as immediate repair costs against potential downtime costs, safety implications, and long-term reliability impacts to identify the most advantageous approach. For instance, when detecting an incipient bearing failure in a critical production asset, the system might compare immediate replacement requiring a production stoppage against a temporary intervention that could extend operation until a scheduled maintenance window, calculating the associated risks and costs of each option to recommend the most favorable strategy given the specific operational context. Furthermore, these systems excel at procedure optimization, automatically generating step-by-step maintenance instructions tailored to the specific equipment configuration, observed failure patterns, available tools, and technician expertise levels. By drawing from manufacturer documentation, industry best practices, organizational knowledge bases, and historical maintenance records, LLMs can compile comprehensive maintenance procedures that incorporate lessons learned from previous interventions while adhering to technical requirements and safety protocols. This capability significantly reduces the expertise barrier for complex maintenance tasks, enabling less experienced technicians to perform sophisticated interventions with proper guidance. The parts and resource management aspects of maintenance operations also benefit from LLM integration, with these systems automatically identifying required replacement parts, specialized tools, and technical expertise based on their diagnosis of the issue. Advanced implementations can interface with inventory management systems to verify parts availability, recommend preemptive procurement for anticipated failures, and optimize inventory levels based on predicted maintenance requirements, ensuring that necessary resources are available when needed while minimizing excess inventory costs.
Integration with Industrial IoT and Edge Computing: Expanding the Predictive Maintenance Ecosystem
The convergence of Large Language Models with Industrial Internet of Things (IIoT) networks and edge computing infrastructure has catalyzed the emergence of a comprehensive predictive maintenance ecosystem capable of processing and analyzing data at unprecedented scale and speed. This technological integration enables the deployment of sophisticated predictive maintenance capabilities across distributed industrial environments, overcoming traditional constraints related to data transmission, processing latency, and connectivity limitations. By strategically distributing LLM processing capabilities between cloud resources and edge devices, organizations can implement real-time analysis of high-volume sensor data directly at the equipment level while maintaining access to the deeper analytical capabilities of cloud-based systems for more complex pattern recognition and knowledge integration tasks. The synergy between LLMs and IIoT sensor networks significantly expands the scope and granularity of equipment monitoring, with advanced sensor technologies capturing increasingly diverse parameters including vibration characteristics, thermal signatures, acoustic patterns, electrical properties, chemical composition, and environmental conditions. The ability of LLMs to process and correlate these multimodal data streams enables comprehensive equipment health monitoring that captures subtle interactions between different physical parameters and operational variables. Edge deployments of optimized LLM components address critical requirements for real-time response in industrial environments, enabling immediate analysis of sensor data and rapid detection of emerging issues without the latency associated with cloud transmission. These edge-optimized implementations can identify critical anomalies and trigger appropriate responses within milliseconds, essential for preventing cascading failures or safety incidents in high-speed industrial processes. The hierarchical architecture commonly implemented in these integrated systems establishes a sophisticated data processing pipeline, with edge devices handling immediate anomaly detection and basic pattern recognition, fog computing layers managing intermediate analysis and correlation across multiple data sources, and cloud-based systems conducting deep historical analysis, complex pattern recognition, and knowledge integration across the entire organizational asset base. This tiered approach optimizes system performance while ensuring that critical protective functions remain operational even during connectivity interruptions. Furthermore, the bidirectional learning capabilities inherent to advanced implementations enable continuous improvement across the entire ecosystem, with insights derived from cloud-based analysis of historical data informing refinements to edge-based detection models, and novel patterns identified at the edge enriching the organization's central knowledge repository. This learning cycle creates an ever-evolving predictive maintenance system that continuously adapts to changing equipment conditions, operational parameters, and emerging failure modes. The integration with digital twin technologies represents another powerful dimension of these ecosystems, with LLMs helping to maintain accurate virtual representations of physical assets that reflect current conditions, historical performance, and predicted future states. These digital twins serve as both repositories for comprehensive asset information and platforms for simulation and scenario testing, allowing maintenance teams to visualize equipment condition, explore potential intervention strategies, and evaluate likely outcomes before committing to specific maintenance actions.
User Experience and Human-AI Collaboration: Democratizing Advanced Maintenance Capabilities
The revolutionary impact of LLM-powered predictive maintenance extends beyond technological advancement to fundamentally transform how humans interact with maintenance systems, democratizing access to sophisticated analytical capabilities and fostering more effective collaboration between human expertise and artificial intelligence. The natural language interface capabilities inherent to these models have eliminated the traditional technical barriers that limited access to advanced maintenance analytics, replacing complex query languages and specialized data visualization tools with intuitive conversation-based interactions that allow maintenance personnel at all technical levels to access critical insights. This accessibility revolution enables technicians, operators, and managers to interact with predictive maintenance systems through natural language questions, receiving contextually relevant information and recommendations without requiring expertise in data science or programming. For example, a maintenance technician can simply ask the system to explain unusual behavior observed in a specific piece of equipment and receive a comprehensive analysis that integrates sensor data, maintenance history, and manufacturer specifications, presented in clear, non-technical language with supporting visualizations. The intelligent information retrieval capabilities of LLM-enhanced systems transform the experience of accessing maintenance knowledge, automatically connecting users with relevant information from vast repositories of technical documentation, maintenance records, training materials, and tribal knowledge captured from experienced personnel. Rather than requiring users to know exactly what document contains the information they need, these systems understand the intent behind queries and retrieve contextually appropriate information across multiple sources, presenting a synthesized response that directly addresses the specific maintenance challenge at hand. This capability dramatically reduces the time required to locate critical information during maintenance activities, accelerating problem resolution and reducing equipment downtime. The adaptive user experience provided by these systems represents another significant advancement, with interfaces that automatically adjust to the user's role, technical expertise, and specific context to provide the most relevant information in the most appropriate format. When interacting with a senior engineer troubleshooting a complex issue, the system might present detailed technical data and sophisticated analytical insights, while the same underlying information would be translated into simplified explanations and clear action items when assisting a less experienced operator. This contextual adaptation ensures that each user receives information optimized for their specific needs and capabilities. Furthermore, LLM-enhanced maintenance systems excel at explanatory capabilities, not merely providing recommendations but clearly articulating the reasoning behind those recommendations in terms that are meaningful to the user. This transparency builds trust in the system's guidance and serves an educational function, helping users understand the patterns and relationships that drive equipment behavior and failure mechanisms. By clearly explaining why a particular maintenance approach is recommended and what evidence supports that recommendation, these systems facilitate knowledge transfer from AI to human operators, gradually building organizational maintenance expertise alongside improved equipment performance. The collaborative problem-solving capabilities enabled by these systems establish a true partnership between human expertise and artificial intelligence, combining the contextual understanding, creativity, and adaptability of human technicians with the data processing power, pattern recognition capabilities, and knowledge integration of AI systems to solve complex maintenance challenges more effectively than either could alone.
Predictive Maintenance Analytics: From Equipment Health to Business Intelligence
The strategic value of LLM-enhanced predictive maintenance extends far beyond the tactical prevention of equipment failures to deliver comprehensive business intelligence derived from deep analysis of maintenance patterns, equipment performance, and operational data. This elevated analytical perspective transforms maintenance from a cost center focused on preventing breakdowns into a strategic function that provides valuable insights for business planning, capital investment decisions, and operational optimization. By aggregating and analyzing equipment health data across entire facilities or enterprises, these systems can identify systemic patterns that inform strategic initiatives ranging from equipment standardization programs to process redesign projects. The advanced reliability analytics capabilities enabled by LLMs facilitate sophisticated calculations of critical maintenance metrics including Mean Time Between Failures (MTBF), Mean Time To Repair (MTTR), Overall Equipment Effectiveness (OEE), and various reliability indices, providing maintenance and operations managers with comprehensive visibility into equipment performance trends. These metrics can be automatically contextualized against industry benchmarks, historical performance, and strategic targets, highlighting opportunities for improvement and quantifying the business impact of maintenance initiatives. The ability to track these metrics across different organizational dimensions – equipment types, production lines, facilities, or business units – enables comparative analysis that identifies best practices, problematic assets, and optimization opportunities across the enterprise. The lifecycle cost analysis functionality inherent to advanced implementations provides valuable insights into the true cost of ownership for different equipment types, incorporating not only acquisition costs but also maintenance expenses, energy consumption, productivity impact, and reliability factors. This comprehensive view informs capital planning processes by enabling accurate comparison of different equipment options based on their projected lifetime costs rather than just initial purchase price. When an organization needs to replace aging equipment or expand production capacity, these insights ensure investment decisions consider long-term operational implications rather than focusing narrowly on acquisition costs. Furthermore, predictive maintenance analytics generate valuable inputs for production planning and scheduling processes by providing accurate predictions of equipment availability, maintenance requirements, and potential failure risks. This integration enables production planners to develop more realistic schedules that account for equipment health conditions, reducing disruptions caused by unexpected maintenance requirements and optimizing production sequencing to minimize the operational impact of necessary maintenance activities. The performance benchmarking capabilities of these systems establish a data-driven foundation for continuous improvement initiatives by identifying performance gaps between similar equipment units operating under comparable conditions. By analyzing why certain assets consistently outperform others despite similar specifications and operating environments, organizations can identify and replicate optimal maintenance practices, operator behaviors, or environmental conditions that contribute to superior performance. This data-driven approach to best practice identification replaces subjective assessments with objective analysis of performance patterns, accelerating improvement initiatives and ensuring they target the factors with the greatest impact on equipment reliability and performance.
Change Management and Organizational Transformation: Implementing the Predictive Maintenance Paradigm
The successful implementation of LLM-powered predictive maintenance represents not merely a technological deployment but a fundamental organizational transformation that reshapes maintenance culture, processes, and capabilities. Organizations that achieve the full potential of these advanced systems recognize that technology implementation must be accompanied by thoughtful change management strategies that address the human, process, and cultural dimensions of the transformation. The journey from reactive to predictive maintenance paradigms requires systematic capability building across multiple domains, including data management practices, analytical skills, maintenance processes, and decision-making frameworks. Organizations must develop comprehensive data governance structures that ensure the quality, consistency, and accessibility of the diverse data streams that fuel predictive maintenance systems, including sensor measurements, maintenance records, equipment documentation, and operational parameters. This foundation of high-quality, well-organized data is essential for LLM systems to generate accurate insights and reliable recommendations. Beyond technical infrastructure, successful implementation requires significant investment in workforce development to build the skills necessary to effectively interact with and leverage advanced predictive maintenance systems. This includes not only training maintenance technicians to use new digital tools and interpret AI-generated recommendations but also developing more sophisticated analytical capabilities among maintenance engineers and managers to effectively oversee and validate system outputs. The most successful organizations create blended skill profiles that combine traditional maintenance expertise with data literacy and digital fluency, enabling maintenance professionals to work effectively at the intersection of physical equipment and digital intelligence. Process redesign represents another critical dimension of the transformation, with organizations systematically reviewing and restructuring maintenance workflows to incorporate predictive insights and recommendations into decision-making and work execution. This often involves transitioning from calendar-based maintenance scheduling to condition-based approaches, implementing new processes for validating and acting on system-generated alerts, and establishing feedback mechanisms that capture maintenance outcomes to continuously improve prediction accuracy. The integration of predictive maintenance insights into broader operational and business processes further extends the transformation, with leading organizations establishing clear pathways for maintenance intelligence to inform production planning, inventory management, capital budgeting, and strategic decision-making. This integration often requires breaking down traditional organizational silos between maintenance, operations, engineering, and business functions to create collaborative processes that leverage predictive insights across multiple domains. Cultural transformation represents perhaps the most challenging aspect of implementation, requiring organizations to shift from reactive mindsets focused on heroic repair efforts to proactive approaches centered on prediction, prevention, and continuous improvement. This cultural evolution typically encounters significant resistance, particularly from experienced maintenance personnel who have built their professional identities and expertise around traditional approaches. Successful organizations address this resistance through compelling demonstration of system value, involvement of maintenance experts in system development and validation, and careful articulation of how predictive systems enhance rather than replace human expertise.
Conclusion: The Future of Intelligent Maintenance in the Age of AI
The integration of Large Language Models into predictive maintenance frameworks represents a transformative advancement that fundamentally reshapes maintenance strategies, capabilities, and outcomes across industries. As we have explored throughout this analysis, LLM-powered predictive maintenance transcends conventional approaches by combining unprecedented pattern recognition capabilities with contextual understanding, natural language processing, and prescriptive guidance to create truly intelligent maintenance systems. These technologies have effectively bridged the longstanding gap between the theoretical promise of predictive maintenance and practical implementation, overcoming traditional barriers related to data integration, pattern complexity, and knowledge accessibility. The trajectory of this technological evolution points toward increasingly autonomous maintenance systems that not only predict potential failures but proactively orchestrate intervention strategies with minimal human oversight. As these systems continue to mature, we can anticipate further advancement in several key dimensions that will define the future landscape of intelligent maintenance. The emergence of self-optimizing maintenance systems represents a particularly promising frontier, with next-generation solutions automatically refining their own monitoring parameters, analytical models, and intervention strategies based on continuous evaluation of prediction accuracy and maintenance outcomes. Rather than requiring manual tuning by data scientists or reliability engineers, these systems will implement sophisticated closed-loop learning processes that continuously improve performance through systematic analysis of successes and failures, adapting to changing equipment conditions, operational parameters, and business priorities without human intervention. The integration with advanced automation technologies including robotics, drones, and autonomous inspection systems will further extend the capabilities of intelligent maintenance, creating end-to-end solutions that not only identify maintenance requirements but physically execute routine inspection and maintenance tasks. This convergence will enable fully autonomous maintenance cycles for certain equipment types and failure modes, with AI systems detecting potential issues, dispatching robotic systems to conduct detailed inspections, and implementing corrective actions through automated maintenance systems, reserving human intervention for complex scenarios that exceed autonomous capabilities. As these technologies continue to advance, the boundaries between traditional maintenance domains – predictive, preventive, and corrective – will increasingly blur, replaced by holistic asset management approaches that dynamically optimize maintenance strategies based on equipment condition, operational requirements, resource availability, and business priorities. This evolution will transform maintenance from a segregated function focused on equipment reliability to an integrated discipline that continuously balances technical, operational, and business considerations to maximize overall organizational value. The broader implications of these advancements extend beyond maintenance operations to reshape fundamental aspects of industrial equipment design, manufacturing processes, and business models. Equipment manufacturers will increasingly incorporate advanced self-monitoring capabilities and digital twins into their products, establishing the foundation for lifetime predictive maintenance from the moment of installation. Production processes will evolve to incorporate real-time equipment health monitoring alongside traditional quality and productivity metrics, creating truly adaptive manufacturing systems that automatically adjust operations based on equipment condition. Perhaps most significantly, the proven value of predictive maintenance will accelerate the adoption of outcome-based service models, where equipment providers guarantee specific performance levels rather than simply delivering physical assets, fundamentally transforming industrial business models from product-centric to service-oriented approaches. As organizations navigate this evolving landscape, those that successfully implement LLM-powered predictive maintenance will establish significant competitive advantages through improved equipment reliability, reduced maintenance costs, enhanced operational efficiency, and more effective capital utilization. The transformation from reactive to proactive operations represents not merely a technological upgrade but a fundamental shift in how organizations conceptualize, manage, and leverage their physical assets to create business value. To know more about Algomox AIOps, please visit our Algomox Platform Page.