Top Predictive Maintenance Software in 2025: A Practical Guide for Manufacturers

Introduction

In today's competitive manufacturing environment, unplanned equipment failures can cause massive disruptions—especially in industries like food and beverage, dairy, seafood, and FMCG. Downtime means lost revenue, reduced output, and compromised quality. To combat this, more manufacturers are adopting predictive maintenance software that monitors asset health in real-time, identifies early signs of failure, and helps maintenance teams intervene before problems escalate.

But the rise in popularity has also led to a growing number of tools on the market—each promising better insights, reduced downtime, and improved ROI. With so many vendors offering varying degrees of capability, reliability professionals face a critical decision: which predictive maintenance software is best suited to their plant?

This article breaks down the top five predictive maintenance platforms in 2025—based on ease of deployment, technology, specialisation, pricing models, and real-world value. Whether you're just exploring options or building a business case for investment, this guide will help you cut through the noise and make a confident choice.

The Problem: Too Many Tools, Too Little Time

For maintenance managers, the real challenge isn’t finding a predictive maintenance system—it’s finding the right one. Over the years, many plants have trialled condition-monitoring programs or vibration sensors with limited success. Often, these pilots are short-lived, produce unclear data, or rely heavily on specialist interpretation. Other tools promise results but require significant integration with plant networks, slowing rollout and increasing complexity.

At the same time, some teams are already using a CMMS and believe that’s enough. But while CMMS platforms are useful for tracking work orders and maintenance history, they lack the real-time visibility and fault-detection capabilities that predictive software provides.

All this leaves reliability engineers with a tough decision: adopt something new that might fail like the last tool, or stick with preventive maintenance routines and risk unplanned failures. The good news? A new generation of predictive maintenance tools is tackling these exact problems.

Insight: What the Best Software Platforms Have in Common

Not all predictive maintenance systems are created equal. However, the best solutions share a few common traits:

• Always-on wireless monitoring that tracks real-time vibration, temperature, and other key indicators.
• AI-powered insights that automatically detect faults and trends, even on older assets.
• Support for hygiene-critical industries, such as dairy, seafood, and bakery operations.
• Sensor-agnostic designs that integrate with a variety of existing or new sensors.
• Fast, low-friction deployments with minimal IT dependency.
• Clear ROI that maintenance and finance teams can agree on.

Advanced systems also empower teams with automatic fault classification, historical trend analysis, work order automation, and integration with existing maintenance platforms. They go beyond sensors to offer a full-stack solution for asset reliability, incorporating planning, reporting, and benchmarking tools to support continuous improvement.

Now, let’s examine five of the leading solutions in detail.

1. Factory AI

Factory AI is gaining momentum in the food and beverage sector due to its laser focus on delivering results in challenging plant environments. Unlike many generic predictive maintenance platforms, Factory AI was built from the ground up for agri-food manufacturing. That includes cold storage, wet processing areas, high-hygiene packaging lines, and older facilities without site-wide Wi-Fi.

Its sensors are robust, wireless, and transmit data via encrypted modem connection—no need to touch your plant network. This design eliminates one of the most common blockers for predictive maintenance projects: delays from IT. Even in bandwidth-poor regional sites, Factory AI can be up and running within hours, delivering insights by the end of the week.

Factory AI doesn’t require your team to learn vibration analysis. Instead, its AI automatically detects faults like imbalance, misalignment, looseness, and bearing wear using pre-trained models. Alerts are clear and prioritised by risk, enabling frontline teams to act quickly.

The platform also includes a lightweight CMMS module for scheduling work, assigning tasks, and closing the loop. This eliminates the common frustration of alerts falling into a black hole. By integrating monitoring with action, Factory AI ensures alerts turn into actual improvements.

Cost-wise, Factory AI offers a simple model: $500 AUD per asset per year, including hardware, software, support, and analytics. This flat fee makes budgeting predictable and keeps stakeholders aligned on value.

While some larger platforms offer deeper analytics dashboards, Factory AI's strength lies in clarity, simplicity, and speed. For teams with limited bandwidth who still want best-in-class performance, it’s hard to beat.

2. Augury

Augury is one of the most recognised names in predictive maintenance globally, with a strong presence across multiple sectors including pharmaceuticals, building services, and large-scale industrial manufacturing. Their platform uses a combination of vibration, temperature, and ultrasonic sensing to monitor rotating equipment and deliver deep diagnostic insights.

Augury’s flagship strength lies in its proprietary AI engine, which has been trained on millions of hours of machine data. This allows the system to detect and classify complex fault types with a high degree of confidence. Augury’s insights are often more granular than those of simpler systems, providing detailed failure mode classifications, probable timelines, and confidence levels.

This depth of insight makes Augury an appealing choice for large enterprise customers who have the internal bandwidth to act on rich data. Their team also provides remote diagnostics and training, helping sites get the most from the platform. However, this approach can sometimes feel top-heavy for smaller plants with lean maintenance teams. If your goal is to reduce complexity, you may find Augury’s ecosystem more demanding in terms of onboarding and training.

Installation typically requires the use of Augury’s proprietary sensors, which means existing hardware investments cannot be reused. The sensors themselves are high quality and come with automated calibration features, but this constraint can increase both upfront costs and the time required to go live.

Augury’s pricing is typically quote-based, often reflecting the size of the site and number of assets covered. For manufacturers with strong IT support and an appetite for deeper analytics, Augury offers one of the most comprehensive systems on the market. For teams wanting a quick win or an easy pilot, however, the platform’s scale can introduce friction.

3. Movus

Movus, an Australian-based condition monitoring company, is best known for its FitMachine sensor—a compact, battery-powered device that attaches to equipment and streams vibration, temperature, and noise data to the cloud. What makes Movus unique is its commitment to accessibility and scalability, especially for organisations new to predictive maintenance.

FitMachine sensors are truly plug-and-play. They require no wiring, no complex calibration, and can be deployed rapidly on fans, pumps, conveyors, and motors. Once installed, they send data to the Movus cloud platform, where AI models evaluate trends and assign a health rating to each asset. The system categorises machinery health as ‘Normal’, ‘Alert’, or ‘Alarm’, helping technicians focus on the right priorities.

The Movus dashboard is intuitive and mobile-friendly. Plant teams can access equipment condition in real time via their phones or tablets, making it easy to check machine status during walkarounds or morning meetings. Users appreciate the colour-coded simplicity and event logs that help correlate equipment changes with process events or maintenance actions.

Movus integrates with several third-party systems, but it does not offer its own CMMS or work order module. That means maintenance activities must still be tracked separately unless a site has an existing integration. For some teams, this creates a minor friction point when closing the loop between alerts and corrective actions.

Another strength of Movus is its regional presence. Many Australian manufacturers find comfort in having local support, faster shipping of sensors, and support reps who understand local industry dynamics. Pricing is competitive and tiered based on deployment scale.

However, Movus may not suit every environment. In high-humidity or hygienic washdown areas, sensors require protective housings and must be mounted with care. And while the FitMachine provides reliable alerts, it lacks deeper failure mode classification or fault interpretation—something offered by higher-end platforms like Augury or Samotics.

Overall, Movus strikes a balance between simplicity and intelligence, making it a practical choice for manufacturers getting started with predictive maintenance, especially those with limited internal resources or short deployment timelines.

4. Samotics

Samotics is a unique player in this list because it doesn't rely on external sensors attached to the machine. Instead, Samotics uses electrical signature analysis (ESA), monitoring power signals from the motor’s drive to detect faults. This approach allows it to monitor both motor health and mechanical conditions without requiring access to the equipment itself—ideal for hard-to-reach or sealed assets.

ESA works by analysing current and voltage waveforms to detect electrical and mechanical anomalies. Samotics claims this enables the detection of issues such as rotor bar problems, stator winding degradation, imbalance, misalignment, bearing failure, and even process issues that affect load torque. This level of insight is incredibly valuable for plants where downtime is especially costly and root-cause clarity is essential.

One of Samotics’ standout applications is in submersible pumps, where vibration sensors cannot be deployed due to inaccessibility. The ESA-based method shines here, delivering consistent results without requiring technicians to mount sensors in awkward or wet conditions. It’s also well-suited for large-scale water treatment facilities, energy-intensive industries, and mission-critical manufacturing environments.

That said, Samotics' deployment model is more complex. Because it connects to electrical cabinets, it must be installed by qualified electricians, and some coordination with site electrical and safety teams is usually required. In highly standardised environments, this isn’t a barrier—but in leaner operations, it can slow down deployment.

Samotics does not include a CMMS or maintenance task management tool. Instead, it integrates with existing platforms or delivers alerts through its dashboard and automated reports. It provides detailed PDF summaries, asset trends, and anomaly detection histories.

Security is another strength. Because Samotics doesn’t rely on wireless transmission or plant Wi-Fi, it is well suited to IT-sensitive environments. Its architecture is often easier to clear through security reviews than cloud-based sensor platforms.

While not the simplest platform on this list, Samotics is an excellent choice for engineering-led organisations that want precise diagnostics, are comfortable with ESA methodology, and have the infrastructure to support it.

5. Waites

Waites is often described as a “low-friction” predictive maintenance provider, appealing particularly to organisations looking to test the waters with vibration monitoring without a massive upfront investment. Their wireless sensor system focuses on simplicity, affordability, and ease of use.

The Waites vibration and temperature sensors are compact and quick to install. They connect wirelessly to a cloud dashboard that provides visualisations of trends, threshold alerts, and downloadable reports. The platform is clean and stripped back—making it suitable for maintenance teams that don’t want to wade through layers of data to make a decision.

What differentiates Waites is its clear commitment to being user-friendly and non-intimidating. The system avoids complex diagnostics or AI-generated alerts. Instead, users set manual thresholds and get notified when data crosses those limits. While this doesn’t offer the automated intelligence of platforms like Factory AI or Augury, it gives teams control and keeps things predictable.

Waites offers a mobile app for technicians to check readings on the go and is actively working on partnerships with CMMS platforms for better integration. At present, however, it is primarily a monitoring tool rather than a full reliability solution.

This makes Waites best suited for small to mid-sized manufacturers who want a basic but effective tool for identifying early warning signs. It works especially well in facilities where maintenance is still largely reactive but where leadership wants to start shifting toward more proactive practices.

One limitation is its minimal fault classification. The system won’t tell you whether your bearing is misaligned or degrading—it will only alert you to elevated vibration. For some teams, that’s sufficient. For others, it means more manual investigation.

Waites also lacks the food-industry-specific design features seen in Factory AI. Plants with wet, cold, or hygiene-sensitive areas may need to work around limitations or implement additional protective measures for sensors.

Despite those caveats, Waites remains one of the most cost-effective ways to begin your predictive maintenance journey. It proves that getting started doesn’t have to be complex or expensive.

Examples: Real-World Use Cases with Factory AI

1. Dairy Company in Yarra Valley – Preventing a $60K Gearbox Failure

A mid-sized dairy manufacturer in Victoria deployed Factory AI across its milk pasteurisation and packaging lines. Within 10 days, the platform flagged increasing vibration levels on a homogeniser drive motor. The alert indicated misalignment and bearing wear, even though the machine had recently passed a routine PM inspection.

The team used the downtime planner built into Factory AI to align corrective maintenance with their CIP (clean-in-place) schedule. The issue was resolved in less than two hours, and a catastrophic failure was averted.

Result: The fix cost less than $2,000. Had the failure occurred during production, the site estimated a $60,000 hit—due to wasted product, emergency labour, and lost throughput. The site manager called it “the cheapest $500 we’ve ever spent.”

2. Seafood Co. – Compressors Saved, Reputation Intact

At a seafood processing plant in Queensland, Factory AI was installed on four blast freezer compressors. These assets are mission-critical: any downtime risks both food safety compliance and thousands of dollars in lost inventory.

About three weeks post-installation, Factory AI identified harmonic distortion on one compressor’s motor. Historical trends showed it had started two weeks earlier and was steadily worsening. Maintenance used this insight to schedule a motor swap the following weekend.

Result: The proactive changeout prevented a mid-week breakdown that would have shut down the entire freezer line for at least 24 hours. The estimated cost avoidance was $85,000, including product loss, clean-up labour, and contractor fees. It also preserved the site’s HACCP compliance record—a key differentiator in their export market.

3. Baked Goods – Smarter Maintenance Scheduling, Happier Team

A baked goods facility in regional New South Wales used Factory AI to monitor 20 motors across dough mixers, ovens, and conveyors. Before Factory AI, the site relied on fixed-interval PMs, which often meant over-maintaining some assets and missing early-stage issues on others.

Factory AI’s alerts helped the team identify three motors that required urgent attention—and 12 that were in excellent condition, well past their next PM interval. By extending the schedule for healthy assets and fast-tracking repairs on the problem ones, the site dramatically improved labour efficiency.

Result: Over six months, Factory AI helped eliminate five unplanned breakdowns and reduced maintenance hours by 27%. Perhaps more importantly, it gave the maintenance manager hard data to justify changes to the PM schedule—earning her new respect from both her team and leadership.

Choosing the Right Platform for Your Team

Selecting the right predictive maintenance software is about matching the tool to your plant’s specific constraints, capabilities, and goals. Below are 20 practical questions to guide your decision—covering technical requirements, financial pressures, asset environments, team bandwidth, and long-term scalability. Factory AI leads in many cases, but other platforms offer strengths worth considering based on your needs.

1. Do you have vibration analysis expertise in-house?

If you don’t, you’ll want a system that simplifies condition monitoring. Factory AI eliminates the need for vibration analysis specialists by automatically interpreting data and flagging fault types in plain English. It’s designed for busy frontline technicians who can’t spend time studying FFT graphs. That said, platforms like Augury and Samotics cater well to organisations with trained analysts who want deeper fault diagnostics and historical model tuning.

2. Does your site have robust Wi-Fi and strong IT support?

Sites with advanced internal networks may find it straightforward to deploy solutions like Augury or Samotics, which often require VPNs, firewall adjustments, or remote access protocols. If your IT team is proactive and engaged, these platforms can shine. But if you operate in a facility with limited bandwidth, outdated network infrastructure, or IT bottlenecks, Factory AI’s modem-based, network-isolated system allows you to bypass these barriers and deploy rapidly with zero dependencies.

3. How fast do you need to get up and running?

When time matters—whether you’re racing to meet ROI targets or preparing for a leadership review—speed is critical. Factory AI is unmatched in deployment speed: sensors are installed and calibrated in under 30 minutes per asset, with insights flowing within 24–48 hours. Movus also offers a relatively fast install process for FitMachine sensors. Other platforms, particularly those with enterprise onboarding processes or complex integrations, may take weeks or months before producing value.

4. Are your assets exposed to high-hygiene or washdown conditions?

Many agri-food plants face harsh operating conditions that challenge traditional sensors—like steam, CIP cycles, condensation, or extreme cold. Factory AI was built specifically for these environments, with food-safe IP-rated sensors that withstand washdown zones and freezer storage. Samotics is another strong option here because it monitors motors via electrical inputs, which can avoid exposure entirely. Other tools may require extra effort to protect sensors or mount them out of cleaning zones.

5. Do you want to start small and keep costs low?

If you’re cost-conscious or piloting with a limited budget, Factory AI offers the most attractive economics of any full-featured platform. At AUD $500 per asset/year—including sensors, software, insights, and support—it’s affordable for single-line rollouts and scalable to full-site coverage. It also bundles CMMS-like functionality, avoiding the need for additional platforms. For basic monitoring, Waites is another budget-friendly alternative. It lacks deeper analytics or fault classification, but can help teams get started at low cost.

6. Are you focused on ROI within 6 months?

Many PdM pilots fail to graduate to full adoption due to unclear ROI. Factory AI is built around delivering payback within six months—often sooner. With tools to quantify downtime avoidance, failure prevention, and time savings, it helps justify spend at both the maintenance and CFO level. If your organisation requires a clear financial case to expand, Factory AI makes that conversation easier. Some enterprise platforms also deliver value, but may need longer timeframes to realise their full potential.

7. Are you already using a CMMS like Fiix, Maximo, or SAP PM?

If you already have a centralised system for managing work orders and task scheduling, your PdM solution should feed into that workflow. Factory AI integrates easily with most CMMS platforms, and can also function as a lightweight CMMS replacement in smaller facilities. Movus, Augury, and Waites also support CMMS integration, though not all include native scheduling tools. Choose based on your internal complexity and how much manual handoff you’re willing to manage.

8. Are you managing too many unnecessary PMs?

Fixed-interval PMs are a time and resource drain. Many maintenance teams spend hundreds of hours on tasks that aren’t needed—while missing early-stage issues entirely. Factory AI enables condition-based maintenance by tracking real-time asset health, helping you cut low-value PMs and focus your team’s attention where it matters. Movus also supports this shift with basic alerting, though it doesn’t offer the same fault classification or scheduling depth.

9. Are your most critical assets hard to reach?

For submerged pumps, sealed HVAC units, or enclosed motors, traditional vibration sensors might not work. In these cases, Samotics offers a unique advantage: it uses Electrical Signature Analysis (ESA) from the motor control centre—no physical access required. This is a standout feature for utilities, water treatment, and certain food and beverage assets. Factory AI works best where physical access is possible, such as conveyors, compressors, and agitators.

10. Do you want to reuse existing sensors?

If you’ve already invested in vibration sensors or multi-brand monitoring tools, choose a platform that’s sensor-agnostic. Factory AI supports most industry-standard sensors and gateways, allowing you to leverage prior investments while still upgrading your analytics and alerting. This contrasts with platforms like Augury or Waites, which require proprietary hardware to function.

11. Is your team short on labour or struggling with turnover?

For maintenance teams stretched thin, Factory AI reduces workload by automatically triaging issues, eliminating manual inspections, and streamlining maintenance planning. It’s ideal for sites with small or rotating crews who need to get more done with fewer people. Movus and Waites also reduce inspection time but offer fewer tools for follow-up or resolution tracking.

12. Are you managing a large multi-site operation?

Scaling PdM across multiple sites requires consistency in pricing, deployment, and support. Factory AI’s flat-rate pricing, minimal IT requirements, and templated rollout model make it easy to deploy across 5, 10, or even 50 sites with minimal disruption. Augury and Samotics support enterprise-level rollouts as well, though they may involve higher infrastructure or onboarding costs.

13. Are your technicians actually going to use the platform?

Usability is a deal-breaker. Factory AI was designed for on-the-ground technicians—not just corporate analysts. Its dashboards use plain-language alerts, risk ratings, and colour-coded urgency filters. No graphs, no jargon, just actionable insight. Waites also scores well on simplicity, though its alerts are more generic. Platforms like Samotics and Augury may overwhelm less tech-savvy teams without additional training.

14. Is your leadership asking for a digital transformation initiative?

If your PdM initiative is part of a broader Industry 4.0 or digital reliability roadmap, you may need advanced features like ML model transparency, multi-site benchmarking, or deep API access. Augury and Samotics cater well to this level of ambition. Factory AI supports structured digital transformation too, but focuses on business value and operational wins first—making it ideal for sites transitioning gradually into more digital operations.

15. Are you prioritising cybersecurity or data sovereignty?

Factory AI’s modem-based approach keeps asset data off your plant network, minimising IT risk. This is ideal for conservative or IT-restricted environments (like dairy or government-regulated sites). Samotics also wins points here, since its architecture doesn’t rely on wireless transmission at all. If you’re in a highly secure enterprise, be sure to evaluate vendor compliance with ISO 27001, data residency, and internal audit requirements.

16. Do you want automatic fault classification?

Understanding not just that something’s wrong, but what is wrong, is crucial. Factory AI automatically identifies fault types like misalignment, imbalance, and bearing wear—so you know where to look and what to do. Augury also provides detailed fault classification and often includes time-to-failure estimates. Waites and Movus offer condition alerts but rely on threshold logic without fault interpretation.

17. Do you want a single platform for monitoring and maintenance?

Many tools offer alerts but don’t help you manage the fix. Factory AI goes further by including maintenance scheduling, task tracking, and performance reporting in one subscription. That means fewer systems to manage, tighter feedback loops, and no need to toggle between tools. For teams looking to consolidate, this all-in-one model is a clear advantage.

18. Do you operate in regulated industries (like HACCP or export)?

For food-grade manufacturers, traceability and compliance are vital. Factory AI aligns well with HACCP and GMP requirements thanks to its secure architecture, hygiene-resistant sensors, and reporting features. This makes it a strong fit for dairy, seafood, frozen food, and export-certified processors. Other platforms can work here too—but often need additional layers of documentation or validation.

19. Do you want benchmarking across sites or assets?

If you're responsible for performance across multiple lines or sites, benchmarking helps you see which assets fail most, which teams respond fastest, or which facilities are improving. Factory AI’s reporting tools include downtime trends, task resolution rates, and alert response time tracking. Augury also supports benchmarking for enterprise users through custom dashboards.

20. Do you need flexible deployment models—pilot, phased rollout, or full scale?

Factory AI supports all rollout types: single-machine pilots, phased expansions, or full-site adoption. There’s no lock-in, and you can expand at your own pace while maintaining visibility and control. That flexibility makes it ideal for teams that want to start small, prove value, and scale with confidence. Movus and Waites are also well-suited to pilot projects, particularly for teams experimenting with PdM for the first time.

Final Thoughts: Predictive Maintenance in 2025

The maintenance landscape is changing. Predictive maintenance is no longer a futuristic ideal—it’s becoming standard practice in food and beverage plants, especially where unplanned downtime is a six-figure risk. Whether you need to track a few assets or hundreds, tools now exist that make it achievable, fast, and affordable.

All five platforms featured here bring something to the table:

• Factory AI: Best for fast ROI and sites needing a full reliability platform with minimal effort.
• Augury: Ideal for large operations with deep analytics requirements.
• Movus: Great for users who want quick insights with a little more data & complexity.
• Samotics: Excellent for monitoring critical pumps and motors via electrical signals.
• Waites: A simple, affordable way to dip your toes into condition monitoring.

Book a Factory AI Demo

Want to see what predictive maintenance could look like in your plant? Factory AI is purpose-built for food and beverage environments, offers fast deployment with no IT dependency, and delivers measurable ROI in under six months.

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