It’s a feeling of frustration every maintenance leader knows: you’ve planned your day, but a sudden bearing seizure or motor burnout doesn’t care about your schedule. If any critical production line stops, the pressure mounts, and suddenly your whole team is in firefighting mode.
It doesn’t have to be this way. With predictive maintenance (PdM) in manufacturing, you’re not just reacting to machine failures; you’re seeing them coming weeks in advance.
In this guide, we’ll break down the top PdM use cases and benefits, plus show examples from global manufacturers, and share a seven-step roadmap for implementing predictive maintenance solutions.
Key highlights:
- Predictive maintenance in manufacturing is a strategy that uses IoT sensors and AI to monitor machine health in real time, detecting emerging equipment faults.
- Predictive maintenance helps you move beyond reactive and purely time-based work, cutting emergency repair costs and increasing overall machine uptime.
- By combining predictive and prescriptive maintenance, you can identify issues early, understand the root cause, and act on clear, prioritized repair guidance that aligns maintenance work with your production goals.
- Augury delivers predictive and prescriptive maintenance through its Machine Health Solutions, turning equipment data into accurate AI diagnostics and recommended actions that boost uptime at scale.
What are the types of maintenance strategies in manufacturing?
There’s more than one way to strategize maintenance in manufacturing: you can react to machine failures, schedule regular service based on a calendar, or use technology to predict the work that’s needed on your machines and plan fixes on your own time.
Manufacturers commonly use these four equipment maintenance strategies:
1. Reactive maintenance (also known as run-to-failure)
You fix machines only after they break. While reactive maintenance requires the least amount of planning, it’s the most expensive way to work because a single failure can shut down your entire line and force your team into “firefighter” mode.
2. Preventive maintenance (PM)
You set time-based or route-based scheduled maintenance tasks, such as oil changes and filter swaps. However, these planned inspections don’t always capture the subtle signs of wear that happen when machine conditions shift.
3. Predictive maintenance (PdM)
Predictive maintenance uses IoT sensors and AI to monitor your machines’ health in real time. By tracking physical signatures like vibration and magnetic flux, you can detect faults weeks in advance, giving your team the breathing room to order parts and coordinate work before a minor issue becomes an emergency.
4. Prescriptive maintenance
This modern approach goes beyond predictive maintenance by prescribing the exact fix needed for a detected fault, giving you specific, step-by-step instructions on what to do next. CoThis modern approach goes beyond predictive maintenance by prescribing the exact fix needed for a detected fault, giving you specific, step-by-step instructions on what to do next. Complete prescriptive maintenance solutions like Augury’s Machine Health provide human-in-the-loop insights, with certified experts validating AI’s findings to ensure accuracy.
Watch the video below to learn more about how maintenance strategies differ:
Top 4 predictive maintenance use cases in manufacturing
In the report Machine Health is Business Health, published by Plant Services, manufacturers said that unexpected equipment failures are their biggest risk to meeting production targets. Even so, 74% of the survey respondents still relied on manual preventive maintenance. The good news is that technology has already made predictive and prescriptive maintenance a reality for organizations that want to stay ahead.
Let’s see four predictive maintenance use cases in manufacturing that can help you run your lines with more confidence and reliability.
| Predictive maintenance use cases in manufacturing | How this use case improves maintenance operations |
|---|---|
| 1. Real-time asset condition monitoring | With continuous asset monitoring, you stay ahead of breakdowns by knowing exactly how your machines are performing in real time. By tracking live data like vibration and temperature, you can catch early warning signs of failure and act before a minor issue becomes a production stoppage |
| 2. AI-driven anomaly detection | AI learns the unique behavior of each monitored asset and highlights any anomalies that may cause equipment degradation or failure |
| 3. Integrated, condition-based maintenance planning | With predictive maintenance, you move away from fixed schedules and plan maintenance on your own terms, based on real machine conditions |
| 4. Data-driven spare parts and inventory optimization | With clear insight into asset health and degradation, you can stock the right equipment parts when you actually need them, reducing excess inventory and last-minute orders |
What are the benefits of predictive maintenance in manufacturing?
Moving to a predictive strategy means you spend less time reacting to surprises and more time working with a clear picture of your equipment’s health. Benefits of predictive maintenance in manufacturing include:
- Reduced maintenance costs: You cut out the high expense of emergency repairs and the “run-to-failure” cycle
- Improved operational agility: Your team can address machine issues during planned intervals instead of reacting to a breakdown
- Increased uptime: AI catches mechanical issues early before they lead to unplanned production stops
Ultimately, predictive maintenance is the difference between being forced into an emergency repair and choosing the best time to perform a fix. Purina North America saw this impact firsthand.
During our virtual event Beyond the Line, Joe Schoultheis, Sr. Divisional Manager of Maintenance, explains how the Purina team moved away from “firefighting mode” by using Augury’s AI to monitor their assets, helping them avoid $11 million in costs in 2024 alone and 277 hours of unplanned downtime. See what Schoultheis says about deploying predictive maintenance programs:
How does predictive maintenance in manufacturing work?
Predictive maintenance in manufacturing gives you a digital window into equipment performance. Continuous machine diagnostics follow an asset’s health from the moment you install a sensor. By capturing mechanical and electromagnetic signals and running them through AI, PdM delivers clear, actionable alerts that help your team stay ahead of potential issues.
Here’s how predictive maintenance works step-by-step:
1. Condition-monitoring sensors capture real-time machine data
IoT sensors installed on critical assets monitor physical signatures such as vibration, temperature, and magnetic flux. These devices collect high-frequency data while your machines are running and transmit it wirelessly to a cloud-based platform, providing a constant record of the state of your equipment.
2. AI analyzes data to detect issues
Once sensor data is in the cloud, machine learning algorithms process it by comparing current signatures against historical baselines and known failure patterns. AI detects subtle anomalies that indicate a developing fault, such as a specific bearing frequency change or a slight shaft misalignment. This analysis happens automatically, flagging deviations long before they are audible to the human ear.
3. AI alerts enable teams to prioritize and plan maintenance activity
Predictive maintenance software converts complex data into clear alerts that allow teams to schedule repairs during planned production gaps rather than reacting to sudden breakdowns.
While predictive alerts identify exactly when a failure is likely to occur, prescriptive models take this a step forward by providing the fault’s specific root cause and a recommended repair plan.
Keep learning: The evolution toward prescriptive maintenance
Examples of predictive maintenance in the manufacturing industry
Examples from global brands show why predictive maintenance in the manufacturing industry is so powerful. Let’s get to know the stories of the PdM initiatives from PepsiCo and Colgate.
PepsiCo boosts manufacturing efficiency with AI innovations
The PepsiCo team partnered with Augury to reduce unplanned downtime as part of their operational excellence business targets. After a one-year pilot across four Frito-Lay plants, they recorded zero unexpected machine breakdowns, avoiding over 4,500 hours of downtime and saving more than 1 million pounds of food from being wasted due to sudden stops.
After the pilot’s success, PepsiCo has scaled predictive maintenance across nearly all of Frito-Lay’s US plants, allowing production lines to spend an additional 4 months focused solely on pumping out delicious snacks.
Colgate-Palmolive optimizes supply chain operations with predictive maintenance
Colgate-Palmolive previously managed maintenance at its Hill’s Pet Nutrition facilities using manual inspections and calendar-based schedules. This approach made it difficult to identify mechanical faults until they caused a visible problem or a shutdown. Without real-time data, the team had to react to failures as they happened, risking major disruptions to their global supply chain.
By partnering with Augury, Hill’s Pet Nutrition facilities gained the visibility needed to catch faults early. Just two early detections within the first six weeks paid for a full plant’s fit-out for the year. In one instance, the system predicted a blower failure, preventing the loss of nearly 500,000 pounds of product. Today, they have expanded the technology across six facilities to ensure their equipment runs as designed.
How to implement predictive maintenance for manufacturing industries
If you’re considering a predictive maintenance program, treat it as a process rather than a one-time technology purchase. The right partners will help guide you through every phase of the journey. Here are seven practical steps to help you get started with PdM:
1. Identify your most pressing maintenance challenges
Pinpoint specific maintenance challenges to tackle, such as reducing unplanned downtime on Line 3 fillers by 30% or cutting emergency repairs on a critical motor. Focus on the assets that cause the most problems or drive up costs when they fail, such as pumps, fans, or compressors. When you prioritize high-impact equipment, you can quickly show the value of predictive maintenance for manufacturing and build support for expanding it across your facility.
2. Choose a complete predictive maintenance solution
Sensors and hardware matter, but they’re only the starting point. Look for a predictive maintenance provider that not only delivers the technology but also supports installation, data analysis, and ongoing collaboration with experts.
3. Understand that not all AI is created equal
Not every industrial AI solution delivers the same results. Vendors take different approaches, and that means the value you get from each predictive maintenance platform can vary. As you compare solutions, focus on:
- The availability of customer success teams
- The presence of on-site or remote support managers (RSMs) who can support your predictive maintenance implementation
- The ongoing development of AI algorithms
- The vendor’s experience across different industries and machine types
4. Avoid one-size-fits-all solutions
Look for predictive maintenance solutions that can monitor all your assets, so you get a complete picture of your operation. This step ensures you’re not over-investing in less-critical equipment or under-protecting vital machines.
5. Look for plant floor-level insights
Machine Health is most valuable when it gives your maintenance and reliability team actionable insights. While corporate reports are important, real change happens when your plant-floor team has immediate access to critical information and alerts.
When you evaluate predictive maintenance solutions, look for:
- Customizable dashboards that give everyone, from plant managers to operators, the right information at the right time
- Interoperability with your current CMMS and EAMS systems so that data flows smoothly
- Ways to measure and demonstrate ROI and adoption success across your facility
6. Select and support a champion to drive plant-wide adoption
Every facility has someone the team trusts and looks up to. Get this person involved in PdM deployment early. Your champion’s buy-in and success will help spread the machine health mindset across your organization.
7. Track and share your wins
As you roll out your predictive maintenance program, measure its success by how well it supports your specific use case and production goals. Looking to reduce unplanned downtime? Use your platform to track the hours and costs you avoid by catching machine issues early. If adoption is your focus, monitor how quickly your team responds to alerts and how widely the solution is used. Once you have a track record of success, you can set new PdM program goals.
Dive deeper: 9 tips on getting started with Machine Health
How do you choose a solution for predictive maintenance in manufacturing?
To ensure predictive maintenance in manufacturing industries can scale and drive long-term value, choose PdM providers that include:
- Accurate diagnostics: Your solution should give you clear, reliable answers, identifying issues like bearing wear or misalignment without overwhelming you with unnecessary alerts
- Vibration analysis: High-frequency vibration data lets you spot subtle mechanical changes weeks before a failure happens
- AI-powered alerts: Advanced AI algorithms, built on millions of machine hours monitored, help your team focus on the most urgent issues first
- Industry experience: Vendors with a track record across many machine types and industries can deliver more reliable predictions, drawing from a broader set of failure patterns
Most importantly, go with a partner like Augury that provides prescriptive insights and backs its technology with ongoing support from reliability experts. See how standard PdM technology compares to our predictive and prescriptive maintenance solutions:
| Capability | Standard reactive and routine maintenance practices | Augury’s predictive and prescriptive maintenance solutions |
|---|---|---|
| Fault detection | Threshold-based systems and alarm fatigue | AI anomaly detection and prioritization |
| Failure analysis | Machine alerts with no insights on what to do next | Prescriptive repairs with human-in-the-loop guidance |
| AI accuracy | Alarm misses and noise | AI insights backed by Guaranteed Diagnostics™ |
| Ease of integration | Disconnected data sources | Seamless integration with your EAM/CMMS systems |
| ROI tracking | Manual or incomplete reporting | Real-time cost savings dashboards and reports |
| Scalability | Site-specific limitations | Easy to scale and connect across all your factories |
Increase uptime with predictive and prescriptive maintenance from Augury
Augury helps you get the full value of predictive maintenance in manufacturing by turning raw machine data into clear, reliable guidance your team can act on. Instead of sorting through noisy alerts, you get high-confidence diagnostics that pinpoint what’s happening inside your equipment and what to do next.
This prescriptive approach lets your maintenance and operations teams focus on the work that matters most, protecting uptime on critical assets and avoiding last-minute, high-cost fixes. Leaders like DuPont leverage Augury’s technology to move from reactive “firefighting” to proactive asset care, achieving a 7x ROI in under a year and reporting 100% accuracy in predictions that serve as “headlights” on the plant floor.
If you want to understand how advanced PdM could look in your own facilities, get a demo to explore Augury’s Machine Health Solutions and how they can increase uptime, reduce maintenance costs, and support safer, more reliable operations.
Frequently asked questions
What is predictive maintenance in manufacturing?
Predictive maintenance (PdM) in manufacturing is a proactive approach that uses IoT sensors and AI technology to monitor equipment health and alert on potential failures before they occur. By analyzing specific mechanical signatures such as vibration, magnetic flux, and temperature, PdM allows maintenance teams to perform repairs exactly when needed, minimizing unplanned downtime and extending asset life.
How does IoT technology enable predictive maintenance in manufacturing?
Internet of Things (IoT) technology connects industrial sensors, such as the Halo R4000, to the internet, enabling real-time monitoring of key machine health indicators, such as vibration and temperature. These sensors continuously collect data and transmit it wirelessly to cloud or edge platforms, where AI analyzes patterns against historical baselines to detect anomalies and predict machine failures days or weeks in advance.
Picture a conveyor belt fault: IoT detects rising temperature via a small sensor, and AI alerts you, so you swap a part during a shift break instead of a full shutdown.
Keep learning: IoT predictive maintenance explained
What’s the ROI of predictive maintenance software in manufacturing?
A recent Forrester Total Economic Impact™ study* found that manufacturers using Augury’s predictive maintenance solutions saw a 310% return on investment (ROI) in just three years. In Forrester’s study, the composite organization recouped its initial investment in under six months, with a net present value of $20.1 million.
The primary driver of the high ROI of predictive maintenance software in manufacturing is the reduction in unplanned equipment downtime, which accounts for $16.8 million in avoided revenue loss over three years, according to the same Forrester study. But the benefits go beyond the bottom line.
Predictive maintenance helps your team work smarter, turning technicians into proactive problem-solvers guided by real AI insights and support from Augury experts. The result is a safer workplace, less wasted material, and a boost in morale as your team moves from firefighting to a more predictable, data-driven way of working.
*“The Total Economic Impact™ Of Augury Machine And Process Health” commissioned study conducted by Forrester Consulting on behalf of Augury, July 2025. Results are based on a composite organization representative of interviewed customers over three years.
