In the world of manufacturing and industrial operations, a lot of confusion still swirls around TPM. Is it “Total Preventive Maintenance” or “Total Productive Maintenance”? Are they the same thing or different?
Let’s clear the air.
Total Preventive Maintenance (TPM) is focused on scheduling checks and interventions to reduce the chance of a failure. But Total Productive Maintenance (TPM) on the other hand is focused on more than just prevention, it’s a holistic maintenance transformation. It drives toward zero breakdowns and losses by involving everyone, not just the maintenance department.
In fact, you could argue that the bridge between the two isn’t just a process difference; it’s about intersecting and connecting people.
The TPM paradox and why we still get confused
For years, many organizations have used “TPM” to refer to Total Preventive Maintenance, focusing heavily on schedules, work orders, and checklists. While preventive maintenance is an essential part of operational efficiency, it often exists in a silo and is carried out by maintenance teams alone, detached from frontline operations.
Total Productive Maintenance, on the other hand, aims to maximize the effectiveness of equipment by eliminating all forms of loss, including downtime, speed losses, and defects.
This model, which has evolved into TPM 4.0 today, is inherently cross-functional. TPM 4.0 moves away from preventive maintenance and moves more towards predictive maintenance approaches. It brings maintenance out of the shadows and into the hands of everyone who touches the equipment, especially the people who know it best: your operators.
TPM isn’t just about managing machines. It’s about empowering the humans behind them.
A brief history of TPM and Nippondenso
In order to understand TPM we need to under the concept of productive maintenance, and where it came from. Productive maintenance originated in Japan in the early 1950s, and was influenced by Dr. W. Edwards Deming, who promoted the Shewhart cycle (plan-do-check-act).
In 1960, Nippondenso, a Toyota Group company, implemented a plant-wide preventive maintenance program for its automated processes. Initially, maintenance was handled solely by the maintenance department, but this change required a large number of specialized personnel.
To address this need, Nippondenso trained machine operators to perform routine maintenance themselves. This approach reduced labor costs, gave operators a deeper understanding of their equipment, and allowed them to detect problems early on. Maintenance teams were then free to focus on more complex repairs and long-term reliability improvements. The enhanced communication between operators and maintenance led to fewer breakdowns, better product quality, and reduced defects.
These practices evolved into a system combining preventive maintenance and maintainable improvement, then termed productive maintenance. Toyota became the first company to fully adopt and be certified in TPM, and Nippondenso received the distinguished plant prize from the Japanese Institute of Plant Engineers (JIPE) for its development and implementation of the methodology.
Traditional Preventive Maintenance and Total Productive Maintenance: How they work and connect
The flowchart contrasts Traditional Preventive Maintenance (PM) with Total Productive Maintenance (TPM), showing how each approach evolves and intersects at the operator level. On the left, Traditional PM is depicted as a top-down, schedule-driven process focused on minimizing downtime through planned interventions, often relying heavily on maintenance teams and sensor data.
On the right, TPM emphasizes a holistic, bottom-up strategy where operators play a central role in maintaining equipment, improving Overall Equipment Effectiveness (OEE), and fostering teamwork. The intersection highlights how empowering operators with tools, training, and ownership bridges the gap between reactive and proactive maintenance, driving continuous improvement and operational excellence.
The human element of prevention is more than a schedule
Traditional PM programs often live in the realm of planners, schedulers, and specialized techs. And while that structure keeps critical equipment running, it can also isolate maintenance knowledge from day-to-day operations.
True “Total” Preventive Maintenance demands the involvement of everyone. That’s where Autonomous Maintenance (Jishu Hozen) comes in, it’s a core pillar of TPM that enables machine operators to take ownership of routine care like cleaning, lubrication, and basic inspections.
Operators are the front lines of failure detection. They hear strange noises before they’re detectable by sensors, feel vibrations that indicate imbalance, and spot oil leaks or misalignments while doing their daily rounds. Their involvement turns routine tasks into your most valuable early warning system. By empowering your people to engage with maintenance proactively, you’re turning preventive action into productive excellence.
From prevention to productivity: The unseen ROI
Bridging the gap between prevention and productivity isn’t a theory; it’s measurable. There are six main preventable losses in maintenance:
Breakdowns: Equipment failures that halt production, often caused by overlooked wear, poor lubrication, or delayed minor repairs.
Setup and adjustments: Time lost during changeovers or fine-tuning due to unclear procedures, poor training, or inconsistent standards.
Idling: Periods when machines are ready but not running, often due to poor coordination, missing materials, or unclear responsibilities.
Minor stoppages: Frequent short interruptions from issues like jams, misfeeds, or sensor faults that are often ignored but add up over time.
Quality and rework: Defects and reprocessing caused by equipment not running at optimal condition, leading to wasted time and materials.
Operator-led preventive care empowers frontline maintenance workers to take ownership of basic maintenance tasks like cleaning, inspecting, and tightening. This helps catch early signs of wear or malfunction before they become costly breakdowns. This proactive involvement builds a culture of shared responsibility and continuous improvement, aligning with the principles of Total Productive Maintenance (TPM). When operators are trained and engaged in equipment care, it reduces reliance on reactive maintenance, enhances equipment reliability, and drives productivity gains.
Here’s how operator-led preventive care feeds into true Total Productive Maintenance (TPM) outcomes with examples:
Outcome
Example
Reduced minor stops and slowdowns
Small hiccups are caught before they snowball into unplanned downtime.
Faster problem solving
Operators who know their equipment inside out can troubleshoot issues quickly and communicate them clearly
Increased morale and ownership across the maintenance team
When people feel responsible for the health of their machines, they take pride in their performance.
Empowered teams constantly find ways to improve equipment reliability, safety, and ease of maintenance.
Safer work environments
Well-maintained machines reduce injury risks, especially when operators are trained to notice unsafe conditions early.
There’s even more ROI to be found beyond this, for example you would see:
Reduced emergency repair costs through fewer breakdowns.
Lower production losses thanks to minimized scrap and rework.
Optimized spare parts inventory, avoiding overstock and stockouts.
Extended asset lifespan, deferring expensive capital purchases.
Lower energy consumption as machines run cleaner and more efficiently.
Building a financially intelligent TPM strategy
To make Total Productive Maintenance (TPM) stick and to secure buy-in from leadership, it needs to be tied to business goals. Here are the steps on how to build a TPM strategy with financial intelligence:
Align with business objectives:Maintenance should support revenue, profit margins, and delivery performance, not just uptime.
Use data to guide decision making:CMMS and EAM systems can reveal trends in equipment failure, maintenance spend, and recurring issues that training or design changes could resolve.
Invest in people and technology: Justify training, technology upgrades, or predictive analytics tools by linking them to clear business impacts.
Design for simplicity: A TPM strategy only works when it’s understood by everyone, so design processes are easy to adopt on the shop floor.
Summary: Where predictive meets productive
The future of maintenance isn’t one model versus another. It’s convergence. Total Predictive Maintenance gave us the sensors, scheduling tools, and early detection methods to reduce unplanned downtime. Total Productive Maintenance, specifically TPM 4.0, gives us the culture, structure, and cross-functional empowerment to make those tools stick and to scale performance across the whole operation.
AI driven predictive maintenance (PdM)(opens in new tab) has become a buzzword in the maintenance world, but not all PdM is created equal. While AI-driven PdM has set a new standard for performance, many solutions on the market today still rely on traditional, non-AI methods. These techniques have value, but they don’t offer the intelligence or automation many organizations assume they’re getting.
In this blog, we’re touching base with our subject matter experts to draw a clear line between what’s actually predictive and what’s just marketed as such.
The long history of non-AI predictive maintenance
Predictive maintenance didn’t begin with artificial intelligence. As Mike Cooper(opens in new tab) highlights, “for decades, maintenance teams have used tried-and-true methods to anticipate failures before they happen. AI is relatively new for maintenance teams; there are a lot of traditional forms of prediction that aren’t always talked about.”
The traditional forms Mike mentions include:
Condition-based or threshold monitoring This is when an alert is triggered for a variable like temperature or vibration that exceeds a predefined limit.
Statistical trend analysis This is when you are spotting anomalies by analyzing changes in equipment performance over time.
Physics-based models An example of this is using mathematical models to simulate how and when components are likely to degrade.
OEM guidelines and lifecycle models This is when you are estimating failure timelines based on historical data and manufacturer specs.
Each of these approaches laid the foundation for PdM, and they’re still being used today. Mike adds that “the catch with these four approaches is that they’re not truly predictive in the ways AI can be, and that’s important for the users of a CMMS to understand.”
AI driven predictive maintenance isn’t what you think
After speaking with some of our industry experts, one thing has become abundantly clear: many CMMS providers are claiming predictive AI capabilities, but it’s mostly statistical or threshold-based logic.
What we see in the market is the bold claim that a CMMS has AI-driven predictive maintenance capabilities, and that’s simply not true.
Mike goes on to explain that true AI-driven PdM uses machine learning (ML) to identify certain patterns. For example, it can:
Recommend optimal maintenance timing based on real-world behavior and not just predefined rules
“When predictive maintenance is truly AI-enabled, it changes how teams think, work, and measure success. It doesn’t just detect problems; it helps prevent them altogether, with fewer false alarms and more confident decision-making,” says Mike.
But what form can AI-enabled PdM take? What does it look like?
“For one thing, AI-enabled means it can look at specific, measurable, machine details,” Mike adds. For example, these can be:
Remaining useful life (RUL): Instead of guessing when an asset might fail, AI calculates how much time is left before a breakdown occurs.
Failure rate: AI models continuously learn from operational data to estimate how often similar failures occur.
Mean time to repair (MTTR): With earlier warnings and clearer fault diagnostics, teams can reduce the time it takes to fix issues.
Unplanned downtime reduction: AI helps maintenance teams prioritize interventions before failures happen, cutting costly disruptions.
Condition indicators: By analyzing data like vibration, temperature, and pressure, AI detects subtle shifts that precede failure, often weeks in advance.
Non-AI vs. AI based PdM approaches
Below is a table that outlines the non-AI and AI based predictive maintenance approaches:
Does predictive maintenance really work?
In short, yes. But it needs to be implemented correctly in order to work effectively for maintenance teams.
“Since PdM uses data from sensors, ML models, and historical trends to predict things like failures, that data needs to be accurate, and not only does it need to be accurate, but it also needs to be available,” Mike highlights.
Success for PdM really depends on the data quality and availability, the technological maturity (i.e., AI/ML models are trained and validated properly), and of course integration with operations (i.e., PdM is embedded into the workflows and decision-making process).
Why is predictive maintenance so important today?
Predictive maintenance is important because it changes maintenance from a reactive or scheduled task into a strategic, data-driven process. For industries like manufacturing, energy, and transportation, PdM can lead to thousands of dollars in savings and productivity gains.
Improving safety (e.g., preventing hazardous failures in critical systems)
Increasing operational efficiency (e.g., keeps production lines running smoothly)
Not all predictive solutions are made the same
In an industry crowded with predictive claims, it’s easy to assume all solutions offer similar value. But when predictive maintenance doesn’t use AI, you’re still doing most of the heavy lifting like manually setting thresholds, analyzing trends, and reacting after the fact.
With a true AI-powered system, your maintenance strategy becomes proactive, adaptive, and continuously improving.
If your PdM strategy still relies on old-school rules or statistical models, you’re missing out on the full potential of what AI can do, not just for assets, but for uptime, productivity, and peace of mind.
That’s where solutions like Fiix CMMS AI features stand out.
It’s 2025, but many organizations still rely on a paper-based work order system. Even those organizations that have a fully functional Computerized Maintenance Management System (CMMS)(opens in new tab) still rely on a hybrid, semi-automatic system that involves both the CMMS and paper printouts. Does this process sound familiar?
How much time is wasted by the maintenance manager performing data entry duties? Typically, this is about an hour a day or 250 hours per year. As a maintenance manager, you have better things to do than print work orders, deliver papers, and type in completion notes written on those work order printouts. Some may argue that it is a good way to validate someone’s work, but regardless of whether they fill out the details on paper or digitally in the CMMS, you still have to trust they completed the work they signed off.
Here’s a typical example for a team of 6 technicians and 3 work orders each per day:
Item
Every day
Minutes a day
Minutes a month
Number of technicians getting printouts
6
6
6
Work orders printed per technician
3
18 work orders a day for all technicians
360 work orders a month for all technicians
Minutes to print and deliver one technician’s work orders
In this example, this hybrid method costs the maintenance manager 20 hours of his time per month. This time would be better spent delivering real value to your organization by optimizing maintenance schedules, analyzing past work orders for trends, and streamlining maintenance to deliver better reliability at the same or at a lower cost.
How can you cut the cost of data entry?
Based on the example above, you can give your 6 technicians an account each on the CMMS. This way they can input data from the work order in real-time as they complete the repairs on terminals at your facility or using their tablet or smartphone.
Every day, they work through their list of assigned work orders in their CMMS and sign the work off digitally in the CMMS. For 6 technicians, freeing up 20 hours is an additional $114 per month—surely money well spent.
Creating an asset hierarchy is essential for efficient maintenance management in any manufacturing capacity. A well-structured hierarchy simplifies your maintenance and enhances asset tracking, asset management, asset performance monitoring, spare parts management, and ultimately, cost savings. In this blog we’ll explore the basics of asset hierarchy, provide examples, and offer some insights into setting it up for your maintenance team.
What is an asset hierarchy?
An asset hierarchy is an organized structure that breaks down assets within an organization into multiple levels, from high-level facilities to individual components. The hierarchy visualizes how assets are related and helps streamline asset management, maintenance, and troubleshooting processes.
Asset hierarchy example
In a manufacturing setting, for example, an asset hierarchy could start with the entire facility (plant level), go down to specific departments (e.g., production and packaging), then to systems within those departments (e.g., conveyor systems), individual equipment or machinery, and finally the components within each piece of equipment (e.g., motors or pumps).
How to set up an asset hierarchy
To build an asset hierarchy like in our example above, you need to start by mapping out your plants’ physical layout and identifying the functional relationships between the different assets. Here’s a simple step-by-step process to set it up:
Define the top level: The highest level represents the entire facility or plant.
Identify your departments: Break down the plant into key departments or functional areas, like production, quality control, or maintenance.
Organize your systems within each department: Identify the major systems within each department, such as the HVAC or conveyor system.
List all of the assets for each system: Drill down further to identify the equipment or assets in each system.
Pinpoint any components: Lastly, include individual components that may require separate maintenance (e.g., pumps, belts, motors).
Each layer of the hierarchy should be clearly defined, allowing maintenance teams to trace issues back to their source.
What is ISO 14224 and why is it important to asset hierarchy?
ISO 14224 is an international standard that provides guidelines for collecting and managing reliability and maintenance data for equipment within the oil, gas, and petrochemical industries. It was developed by the International Organization for Standardization (ISO), and it’s important to asset hierarchy because it offers a framework for classifying equipment, setting up asset hierarchies, and capturing critical information such as failure modes, maintenance history, and reliability data. The ISO 14224 standard provides a hierarchical pyramid for taxonomic classification consisting of nine levels.
The example above illustrates the typical pyramid of taxonomic classification. Level 1, at the very top, represents the type of industry, while Level 9, at the bottom, represents a specific part of an individual asset. Assets sit at level 6, this can include any machines, IoT sensors, motors, pumps, etc.
With a clear asset hierarchy like this one, data collection is consistent across systems and equipment in a common format. ISO 14224 also specifies what data should be collected at each level of the hierarchy, this can include failure rates, repair times, and maintenance types. By defining and using standardized asset hierarchy with clear data, it allows teams to develop reliable benchmarking. Then organizations can track their asset performance and compare them against industry norms. It also makes it easier to predict and prevent failures, since the asset hierarchy simplifies the tracking and analysis of maintenance events.
Setting up an asset hierarchy in a CMMS begins by creating an underlying structure for assets. There are several different ways to set up a hierarchy in a CMMS. Within a CMMS top-down hierarchy might look like this:
Site or plant level: The highest level may include multiple plants if the company operates in different locations.
Department level: Different departments or function areas (e.g., production, quality control, maintenance).
System level: Major systems within each department (e.g., conveyor systems, HVAC).
Asset level: Individual pieces of equipment that comprise each system and their unique attributes (e.g., asset criticality, serial number, asset ID, etc.).
Component level: Specific parts of the asset, such as motors, pumps, belts, and control panels.
This structured setup helps maintenance teams drill down from broader plant-level maintenance needs to individual asset or component-level actions, ensuring each layer is maintained effectively. Some CMMSs’ also have set rules for setting up asset hierarchies.
CMMS asset hierarchy example
Let’s say a technician at a bottling plant receives a CMMS alert that there’s an issue with a bottling conveyor belt and it’s not running properly. By navigating the CMMS asset hierarchy:
The technician can start from the site or plant level and quickly drill down to the department level, which is the packaging area, and then to the system level, which in this example is the bottling line system, and locate the bottling conveyor belt which is the asset level.
Within the bottling conveyor belt, they can look at the component level and identify specific components, like the motor or rubber belt on the conveyor line that might be causing the issue. With systems like these, parts can wear down after prolonged use.
This structured hierarchy in a CMMS makes it easier for maintenance teams to navigate, isolate issues, and conduct repairs efficiently without downtime. It also supports historical data tracking, allowing maintenance teams to analyze past repairs at each level for optimized performance. Ensuring that your asset naming conventions are clear and consistent will help make things easier to find on your CMMS.
Best practices for naming conventions for your asset hierarchy
Naming conventions for your asset hierarchy are essential to maintain consistency, improve navigation, and streamline communication across your team. Here are some best practices for creating a clear and practical naming convention for asset hierarchy in a CMMS:
Standardize naming across all levels of the hierarchy
Use descriptive but concise names
Incorporate location codes
Utilize function-based codes
Include equipment type and ID numbering
Avoid special characters
Document naming conventions and train staff
Include manufacturer or model information if relevant
Prioritize unique identifiers for critical assets
Last but certainly not least, the final step, step 10, would focus on always including your full maintenance and operations team in the development of your asset naming conventions. This is important for two core reasons:
So that when a technician for example sees an asset code, they know exactly what that asset is.
So that communication between maintenance and operations can be streamlined, thus saving time for both teams when there is an issue.
Understanding the parent-child relationship in asset hierarchy
In asset management, the parent-child relationship refers to the hierarchal structure where a parent asset is a higher-level or more complex asset that includes one or more child assets.
The illustration above shows the relationship visually. The parent asset is always at the top and it can have multiple child assets, but any child asset is limited to a single parent. Below is a table to help you understand the difference between the two when it comes to asset hierarchy:
A higher-level asset that comprises of multiple components or sub-assemblies.
A component, sub-assembly, or part that belongs to and depends on a parent asset.
Examples
A production line in a factory.An HVAC system in a building.A server rack in IT infrastructure.
Motors, conveyors, and IoT sensors in a production line.Air handling units and compressors in an HVAC system.Individual servers and network switches on a server rack.
There are several benefits of parent-child asset relationships, the most obvious being improved asset tracking and maintenance planning. However, the relationship also offers a unique way to organize asset hierarchies. In this case, it provides a clear mapped-out view of asset dependencies and organization. This makes it a lot easier for maintenance planners to see their data and analyze it at both parent and child levels for better decision-making.
Standardize asset naming conventions: A consistent naming system for assets, systems, and components simplifies navigation and search.
Train maintenance teams on CMMS usage: Familiarize team members with the asset hierarchy structure and show them how to navigate it within the CMMS.
Conduct regular audits: Schedule periodic reviews of the asset hierarchy to ensure all equipment and components are accurately represented and updated as changes occur.
Integrate data across departments: Ensure the CMMS integrates with other department systems, such as procurement and finance, for a complete picture of asset performance and cost.
Utilize feedback from technicians:Maintenance personnel can provide valuable insights on what changes may be needed within the hierarchy for practical, on-the-job usage.
Setting up asset hierarchy is a key step for maintenance management
Asset hierarchy allows teams to understand their plant operations from the facility down to each part or component needing attention. Whether teams are using a CMMS software like Fiix or manually setting up their hierarchies, a clear, and concise structure lets maintenance and operations teams streamline their work and enhance their overall equipment efficiency.
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