7 Best Predictive Maintenance Solutions for Manufacturers

Unplanned downtime, blanket time-based PMs, and siloed machine data are squeezing manufacturing margins. You’re expected to raise OEE, extend asset life, and prioritize the right work at the right time - without adding headcount or rebuilding your tech stack. Predictive maintenance solutions promise exactly that, but choosing the right path isn’t simple. Options span full-blown EAM/APM platforms, niche analytics tools, cloud AI services, and turnkey IoT offerings. The wrong fit can stall in pilots, strain budgets, or bolt on yet another data island.

This guide compares seven best-in-class options for manufacturers - from enterprise suites like IBM Maximo and GE APM, to specialist platforms such as Siemens Senseye and Emerson AMS, to cloud-native AWS Lookout for Equipment, a modern CMMS with PdM (Fiix), and a turnkey OEM IoT platform for connected products (Scale Factory). For each, you’ll see what it is, who it’s best for, the PdM features that matter, limitations to weigh, and how pricing/deployment typically work. Use it to match your use case and constraints - time to value, integrations (SCADA/PLC/OPC UA, CMMS/ERP), edge vs. cloud, model training needs, security/compliance, and total cost of ownership. Let’s get to the picks.

1. Scale Factory

Scale Factory isn’t a traditional maintenance platform - it’s a turnkey IoT foundation that lets OEMs ship connected, branded products fast. For manufacturers, that makes it a practical on-ramp to predictive maintenance solutions: reliable telemetry, secure cloud, and configurable control logic that power condition monitoring, anomaly alerts, and usage-based service triggers across fielded assets.

What it is

A ready-to-use IoT platform with fully branded smart control apps, secure cloud backend, and optional Horizon hardware modules. It’s field-tested across hundreds of outdoor devices in North America and delivers automatic updates so products gain capabilities over time without in-house app or cloud development.

Best for

Manufacturers who need to stand up connected products quickly and build the data pipeline required for predictive maintenance - without staffing a software team. It’s especially strong for outdoor consumer/prosumer product lines.

Key features for predictive maintenance

Scale Factory provides the data, controls, and reliability layer that PdM workflows depend on, from edge capture to cloud delivery and in-app actions.

Limitations to consider

Scale Factory isn’t an EAM/APM or CMMS and doesn’t claim built-in machine-learning PdM models. Manufacturers needing advanced analytics, work order optimization, or spare parts planning will integrate this data with their maintenance or data science stack.

Pricing and deployment

Deployment timelines are typically measured in weeks, with branded apps, secure cloud, and optional Horizon modules configured to your product line. Pricing depends on scope and hardware needs; the platform is delivered as a complete, customizable solution with ongoing updates.

2. IBM Maximo Application Suite

IBM Maximo Application Suite is an integrated enterprise asset management platform that brings advanced analytics, AI, and automation to asset monitoring and predictive maintenance. It ingests data from IoT sensors alongside historical and IT/EAM sources, applies machine learning in real time, and surfaces the right alerts and insights to maintenance teams to improve reliability and uptime.

What it is

A unified EAM/CMMS suite from IBM that uses AI/ML to assess equipment health continuously and predict future states. Maximo analyzes time-series and failure data at the edge or in the cloud, triggering maintenance actions when defects are detected—not just on a calendar.

Best for

Maximo is a fit for asset-intensive manufacturers that need enterprise-scale reliability, cross-site standardization, and tight governance.

Key features for predictive maintenance

Maximo emphasizes continuous condition visibility and outcome-driven workflows backed by IBM’s AI.

Limitations to consider

Maximo’s breadth comes with complexity. Teams should plan for integration, training, and data readiness to realize predictive value.

Pricing and deployment

Maximo can collect and process data at the edge or in the cloud within an AI-enabled EAM/CMMS environment. IBM invites prospects to explore the suite and book a live demo; commercial details are provided through the IBM sales process based on scope and requirements.

3. Siemens Senseye Predictive Maintenance

Siemens has folded AI-powered Senseye into its portfolio to deliver cloud-based predictive maintenance solutions backed by consulting. Combined with Siemens’ strength in production and drive systems—and its acquisition of Brightly Software for asset and maintenance management—this offers manufacturers a route to predictive maintenance that blends technology with implementation expertise.

What it is

A cloud-based predictive maintenance offering from Siemens that leverages Senseye’s AI-driven industrial machine performance capabilities and Siemens’ domain expertise. The portfolio also includes consulting to tailor solutions to specific production lines and applications, with Brightly Software extending asset and maintenance management coverage.

Best for

Manufacturers who want a partner-led approach to predictive maintenance solutions and need deep support around complex lines or drive systems.

Key features for predictive maintenance

Siemens centers on AI, asset intelligence, and services to help teams predict and prevent failures while aligning with existing operations.

Limitations to consider

Siemens’ approach often involves custom design and services. That can raise investment and extend timelines compared with out-of-the-box tools.

Pricing and deployment

Engagements are scoped to your environment and typically delivered as cloud-based solutions with Siemens consulting. Pricing is customized based on assets, integrations, and services required; deployment efforts reflect the tailored nature of the implementation.

4. GE Digital Asset Performance Management (APM)

GE Digital’s approach to predictive maintenance solutions is rooted in its industrial IoT platform heritage. Described as offering a suite of software “from the very edge (machine-level sensors) to integrated applications,” it’s designed to help manufacturers stand up end-to-end telemetry and asset performance capabilities that are easy to implement, scale, and upgrade.

What it is

An industrial IoT-powered APM offering that connects machine-level sensors to integrated applications to support asset health insights and maintenance decisions. It focuses on providing an edge-to-cloud foundation that manufacturers can deploy across lines and sites without building a platform from scratch.

Best for

Teams that want a strong, end-to-end IIoT backbone for predictive maintenance with straightforward rollout and growth across plants. It’s well-suited to organizations prioritizing platform reliability and scalability over deep, bespoke customization.

Key features for predictive maintenance

GE Digital APM emphasizes platform strength and full-stack coverage, enabling manufacturers to capture, move, and act on equipment data in service of PdM use cases.

Limitations to consider

The platform’s power can come with trade-offs around tailoring and transparency, which buyers should weigh against integration needs and internal data science preferences.

Pricing and deployment

Deployments are typically cloud-delivered with an emphasis on quick implementation, scalability, and manageable upgrades. Pricing is scoped to environment size, assets, and required applications or services, reflecting the platform nature and integration effort.

5. Emerson AMS Machine Works

Emerson AMS Machine Works focuses tightly on field devices and rotational equipment, giving maintenance teams an at‑a‑glance dashboard with real‑time performance data. For manufacturers whose risk concentrates in rotating assets, this targeted approach can accelerate condition insights and help prevent unplanned downtime.

What it is

A predictive maintenance solution from Emerson designed for field devices and rotational equipment. It centers on an easy‑to‑use, single dashboard that surfaces equipment health and performance in real time, serving as a one‑stop shop for monitoring and management across those asset classes.

Best for

Manufacturers that want a focused, dashboard‑led approach to predictive maintenance solutions without adopting a full enterprise suite.

Key features for predictive maintenance

Emerson emphasizes clarity and speed from data to action within its supported scope.

Limitations to consider

The strength of AMS Machine Works is also its constraint: it’s intentionally specialized.

Pricing and deployment

Engagements are scoped to your supported devices and monitoring needs, with deployment geared toward delivering the at‑a‑glance dashboard and real‑time views. Pricing varies by footprint and requirements; work with Emerson to define assets, integrations, and rollout plan.

6. AWS Lookout for Equipment

AWS positions predictive maintenance around connected sensors, AI/ML, and edge/cloud processing - collecting time-series data and analyzing it in real time to flag defects, forecast health, and drive just‑in‑time maintenance. Lookout for Equipment aligns to this pattern by focusing on equipment telemetry so teams can detect anomalies and act before failures impact uptime.

What it is

A cloud‑native predictive maintenance option on AWS focused on analyzing equipment sensor data with machine learning. Data can be collected at the edge or in the cloud, evaluated continuously, and used to notify maintenance when potential defects are identified.

Best for

Manufacturers already standardizing on AWS who want to apply AI/ML to sensor streams without building a platform from scratch.

Key features for predictive maintenance

AWS emphasizes continuous condition assessment and ML‑driven insights that optimize scheduling and resources.

Limitations to consider

Predictive maintenance solutions benefit from strong data and organizational readiness; AWS is no exception.

Pricing and deployment

Deployed in the AWS cloud with optional edge processing. Typical motions: connect sensor data, establish data pipelines, pilot with priority assets, then scale. Commercials are scoped via AWS sales based on usage and requirements; start with a targeted pilot to validate value before broad rollout.

7. Fiix by Rockwell Automation

Fiix is a cloud CMMS from Rockwell Automation that provides work order management, asset management, inventory, analytics, reporting, and mobile maintenance - backed by AI-powered technology. While it’s not a full APM suite, many teams use Fiix to operationalize predictive maintenance solutions by turning condition insights into prioritized, just‑in‑time work.

What it is

A modern, AI‑assisted CMMS that centralizes assets, work orders, and inventory with dashboards and reporting. Fiix is designed for fast, practical adoption on the shop floor via web and mobile.

Best for

Manufacturers seeking a capable CMMS that can support PdM workflows without the overhead of an enterprise EAM/APM.

Key features for predictive maintenance

Fiix helps teams act on early warnings by streamlining the maintenance response and tracking outcomes.

Limitations to consider

User feedback highlights areas to evaluate in trials and pilots.

Pricing and deployment

Fiix is delivered as a cloud service with web and mobile apps, enabling phased rollouts by site or asset group. Deployment centers on configuring assets, workflows, users, and integrations; pricing is scoped through Rockwell Automation based on footprint and requirements.

Next steps

These seven picks span enterprise suites, cloud AI, focused monitors, and a turnkey OEM IoT foundation. Your best fit depends on data readiness, asset mix, integration needs, and time‑to‑value. Move quickly from evaluation to a small, measurable pilot that proves fewer interruptions and smarter work - then scale with confidence.

If you want connected products and a PdM‑ready data pipeline without building apps or cloud, partner with Scale Factory and launch in weeks.