Before the 1960s, fixed asset management was largely informal and mostly reactive. The discipline of enterprise asset management simply did not evolve enough during this period for any meaningful strategy or approach to be formally introduced into the realm of maintenance.
In fact, large production operations were rare, as consolidated manufacturing operations were not yet common.
Even the humble spreadsheet was introduced to the market a whole decade later.
Companies used tools like paper logs and manual records to track maintenance-related tasks.
Maintenance approaches during this period were largely reactive and maintenance orders were carried out only after a failure occurred.
The drawbacks of these practices were well-known and paved the way for novel, modern asset management strategies to evolve.
Preventive Maintenance
As costs began to soar and instances linked to equipment failure and part unavailability began to increase, enterprises began relying on preventive maintenance for ensuring asset uptime and continuity.
This shift occurred between the 1960s and 1980s, when CMMS solutions began to emerge. Companies began adopting scheduled maintenance to reduce downtime and basic computing systems for tracking maintenance schedules were adopted.
Large companies began adopting internally developed and maintained mainframes to manage maintenance schedules. The Maximo system was developed by Project Software & Development Inc. (PSDI) in 1985. After IBM acquired the company in 2006, IBM Maximo quickly gained popularity and became a market leader.
As per a WifiTalents report, preventive maintenance programs can reduce equipment repairs by 25-40%.
When executed holistically, preventive maintenance can be one of the most ROI-positive initiatives, and the statistics seem to indicate that the results from this approach drive far greater value as far as driving operational excellence is concerned;
- Digitally Managed Work Orders - Streamlining task assignments, tracking, and completion digitally rather than manually.
- MRO Inventory Management - Ensuring spare parts and consumables are available at the right time to avoid downtime.
- Preventive Scheduling – Scheduling maintenance activities at regular intervals to avoid unexpected breakdowns.
- Labor & Resource Management - Optimizing workforce allocation and ensuring resources are deployed where they are needed most.
- Maintenance Data Management - Enhancing accuracy through asset and equipment data cleansing (discussed in this article), standardization, and integration with enterprise systems for reliable decision-making.
Predictive Maintenance
Predictive maintenance monitors the actual condition of equipment to identify potential issues based on factors such as vibration, temperature, pressure, and oil quality.
It has its roots in the development of condition monitoring systems; the earliest form of condition monitoring emerged in the 1950s and 1960s with the introduction of sensor technology.
These were largely confined to military applications and used primarily for high-cost, critical assets like turbines or generators. Throughout the late 1990s, industries such as oil and gas, mining, and utilities began adopting predictive maintenance techniques more broadly.
The discipline was adopted more widely in the late 2000s with the rise of Internet of Things, at which point there was an explosion in the number of connected devices and the amount of data being collected.
Among the early leaders were companies specializing in condition monitoring, like SKF and Schneider Electric, which developed advanced sensors and predictive analytics software.
Condition-based maintenance (CBM) is often considered a precursor to predictive maintenance (PdM). In a CBM system, maintenance is performed only when certain indicators (such as vibrations or temperatures) show signs that a component is no longer operating at peak performance.
The key distinction is that predictive maintenance not only monitors the current state of assets but also uses data trends, historical patterns, and machine learning algorithms to predict future failures. This makes PdM more proactive than CBM, which primarily reacts to real-time data without forecasting the equipment’s future condition.
These are key components of predictive maintenance and how they are analyzed:
- Vibration Analysis
- Thermography (Infrared Thermography)
- Oil Analysis
- Ultrasound Testing
- Acoustic Emissions
While the benefits of predictive maintenance are well-known, including reduced downtime, extended asset life and improved safety. However, implementing predictive maintenance typically involves significant upfront costs for setting up the infrastructure with sensors (possibly), software and to train field technicians and technology teams on its usage.
This discipline is best adopted in industries where it is known to drive value and generate significant ROI such as energy, large-scale manufacturing, and mining operations.
Reliability-Centered Maintenance
Reliability-Centered Maintenance (RCM) is a maintenance strategy that focuses on ensuring that systems continue to perform their intended functions without failure, while minimizing downtime and maintenance costs.
It’s an approach based on identifying the most effective maintenance actions for each component of a system based on its criticality and failure modes.
RCM aims to balance safety, environmental impact, and operational efficiency by focusing on what is most important for system reliability.
While it may resemble other asset management approaches, such as Reactive Maintenance, there are a few subtle differences.
RCM proactively prepares for potential failures by identifying critical components and planning maintenance actions to avoid unplanned downtimes, whereas reactive maintenance waits until something breaks.
Compared with Predictive Maintenance, RCM is more targeted and tailored. Instead of performing maintenance on a set schedule for all components, it prioritizes maintenance for components where failure could be harmful or expensive, optimizing resources.
As per GP Strategies, companies using Reliability-Centered Maintenance (RCM) have reported up to 63% ROI, 80% lower downtime costs, and millions in annual production gains.
Here are a few benefits support adopting RCM and similar approaches:
- Cost Efficiency: It helps in focusing resources on the most critical components, preventing unnecessary maintenance on less important equipment.
- Increased Reliability: By focusing on failure modes and their effects, RCM helps ensure that systems are more reliable and perform consistently.
- Optimized Maintenance Schedules: Maintenance can be scheduled based on the actual needs of the system rather than arbitrary time intervals.
A practical example from an offshore platform illustrates how an RCM-based approach incorporates techniques:
Critical Component: Crude Oil Pump – It transfers oil from the well to storage.
Failure Mode: Worn-out bearings or seal failure can cause the pump to stop, halting oil production.
RCM Maintenance Strategy
Preventive Maintenance: Regular inspections and part replacements based on manufacturer guidelines (e.g., seal and bearing replacements every 6 months).
Predictive Maintenance (PdM): Vibration sensors monitor the pump's health. If vibrations exceed a threshold, the pump is taken offline for inspection and repair before failure.
Condition-Based Monitoring (CBM): Pressure and flow rate sensors continuously monitor performance to identify early signs of wear.
Outcome: The pump is maintained just-in-time, minimizing unplanned downtime, preventing costly repairs, and ensuring continuous oil production.
As per a report by Insights Global, equipment failures have pushed financial losses to new heights. Fortune Global 500 industrial organizations lose about $1.5 trillion each year due to unplanned downtime.
Digital-Twins
A digital twin is simply a virtual representation of an asset or a collection of physical assets that represent key operating conditions and performance metrics using sensor data and other sources.
It is a digital representation that is continuously updated in real-time and mirrors the state, behavior and performance of its physical counterpart, enabling detailed analysis and optimization of the asset.
The concept has evolved from simple 3D models or static simulations to dynamic, data-driven, and intelligent systems that support everything from operations to long-term strategic decision-making.
Real-time data from sensors is fed into enterprise systems like Scada or MES and an analytics layer helps interpret the data and simulate outcomes.
Think of it as an advanced, holistic asset maintenance strategy that goes a few steps beyond standard predictive maintenance.
As per McKinsey, digital twins can improve capital and operational efficiency by 20–30% in large-scale infrastructure and asset-heavy operations.
Unfortunately, the hardware and software systems required to implement digital twins are far more complex and expensive, reserving this strategy only for enterprise-grade production operations.
Risk-Based Maintenance
As the name indicates, risk-based maintenance is an asset management strategy that prioritizes maintenance activities based on the level of risk associated with equipment failure.
It combines two critical components – the likelihood of failure and the consequences of that failure – each associated with a score.
Similar to spare-parts criticality, risk-based maintenance follows a cost-optimized approach where an assessment of costs associated with every breakdown is calculated in detail.
This combined score determines how maintenance activities are prioritized across the plant or at an organizational level.
RbM = Probability of Failure × Consequence of Failure
RbM in Mining Operations
A large open-pit copper mine uses multiple haul trucks to transport ore from the pit to the processing plant. These trucks are expensive, hard to replace quickly, and critical to production.
Steps:
1. Assess Risk
Haul trucks have moderate failure probability but high consequences (production loss, costly downtime).
Classified as high-risk assets.
2. Collect Data
Monitor engine temperature, vibration, brake wear, and oil quality.
3. Maintenance Strategy
Trucks with higher risk get predictive and preventive maintenance (e.g., vibration monitoring, oil changes, brake inspections).
Lower-risk equipment follows run-to-failure.
4. Schedule & Review
High-risk trucks maintained more frequently based on condition and data trends.
Risk levels are regularly updated to inform adjustments to maintenance plans.
Outcome:
- Reduced unexpected breakdowns
- Improved safety
- Optimized maintenance costs by focusing on critical assets
Artificial Intelligence & Agentic Solutions in EAM
Artificial Intelligence (AI) is transforming Enterprise Asset Management (EAM) by integrating data from multiple sources and applying advanced analytics to optimize maintenance decisions. This article covers this in detail.
Unlike traditional approaches that rely solely on historical data or scheduled inspections, AI-EAM leverages machine learning algorithms to continuously learn from sensor data, maintenance records, operational conditions, and industry best practices.
Step-by-step breakdown of how this works:
- AI aggregates data from IoT sensors, CMMS, SCADA systems (supervisory control and data acquisition), and external databases (such as equipment manufacturer insights, regulatory standards, and peer industry benchmarks).
- It analyzes this data to detect subtle patterns and predict failures with greater accuracy than traditional predictive maintenance.
- AI recommends optimal maintenance schedules and resource allocations by comparing current asset health against industry-wide best practices and historical performance.
- Over time, the system self-improves by learning from the outcomes of previous maintenance activities, minimizing human bias and error.
AI-driven asset management significantly enhances predictive accuracy by analyzing vast amounts of data from multiple sources, which helps reduce unexpected equipment failures and downtime. A report by PWC details how companies such as Kion Group and Airbus are adopting practical strategies in EAM and maintenance by leveraging AI and machine learning to optimize asset performance, improve operational efficiency, and support more informed decision-making across the enterprise.
It enables dynamic scheduling that adjusts maintenance activities based on real-time asset conditions, increasing operational efficiency.
By continuously benchmarking against industry best practices, it drives ongoing process improvements and compliance. Additionally, AI optimizes resource allocation, reducing costs associated with spare parts and labor while supporting smarter decision-making across the asset lifecycle.
