This Tech Tuesday features essential IoT maintenance tools for SMEs. Discover affordable platforms that predict equipment failures and reduce unexpected repair costs.
Welcome to Tech Tuesday, where we explore the technologies reshaping Australian business. When a critical piece of equipment fails unexpectedly, the costs extend far beyond repair bills. Production halts, deadlines slip, and frustrated customers question reliability.
For Australian manufacturers and facility managers, IoT data platforms are transforming equipment maintenance from reactive crisis management into predictive, data-driven operations that prevent failures before they occur.
Full-Feature Condition & Predictive Maintenance Suites
SAP Asset Performance Management (SAP APM)
SAP Asset Performance Management (SAP APM) is a cloud-native solution on the SAP Business Technology Platform that helps organizations use IoT sensor data and AI to predict, monitor, and optimize equipment maintenance. It combines real-time sensor data, operational history, and engineering practices to enable condition-based and predictive maintenance for critical assets.
Key Features:
- Connects to IoT sensors, data lakes, and files
- Monitors equipment health continuously and detects anomalies
- Sends automated maintenance alerts and manages backlogs
- Estimates risk and remaining useful life using AI and methods like FMEA and RCM
- Supports visual inspections with computer vision
- Triggers maintenance actions using rules-based and streaming IoT data
Best for: Industrial and asset-intensive businesses (energy, utilities, manufacturing, oil & gas), plant engineers, and maintenance teams looking to move from reactive to predictive maintenance.
C3 AI Reliability
C3 AI Reliability is an enterprise-grade platform that uses IoT sensor data, operational history, and machine learning to predict equipment failures, reduce downtime, and improve overall equipment effectiveness (OEE).
Key Features:
- Unifies data from sensors, maintenance logs, and asset templates
- Detects anomalies, predicts failures, and estimates remaining useful life (RUL)
- Provides prescriptive maintenance recommendations
- Embedded generative AI interface for operational insights and root cause analysis
- Supports cloud and edge deployment with low false-positive alerts
Best for: Industrial and asset-intensive enterprises (oil & gas, manufacturing, utilities) looking to move from reactive or scheduled maintenance to predictive and condition-based maintenance.
IBM Maximo Application Suite
IBM Maximo Application Suite is an enterprise-grade platform for managing the full asset lifecycle, from IoT monitoring and predictive analytics to inspection, maintenance, and decommissioning. It combines sensor, operational, and historical data to reduce downtime, extend asset life, and optimize maintenance planning.
Key Features:
- Maximo Monitor ingests IoT and edge data in near real-time to detect anomalies
- Supports condition-based maintenance using sensor data
- Predicts remaining useful life and provides risk scoring with Maximo Health
- Offers visual inspection tools, dashboards, automated work orders, and historical data retention
Best for: Large, asset-intensive organizations (manufacturing, energy, utilities, transportation) needing a comprehensive, IoT-driven maintenance platform.
Siemens Insights Hub
Siemens Insights Hub (formerly MindSphere) is an industrial IoT platform that helps organizations turn asset and operational data into actionable insights. It supports preventive, condition-based, and predictive maintenance by integrating machine data, digital twins, and real-time analytics.
Key Features:
- Connects to physical machines, legacy systems, and PLCs via edge agents
- Performs edge analytics, anomaly detection, and condition monitoring
- Integrates with maintenance systems to trigger workflows and requests
- Offers custom dashboards, low-code development (Mendix), and predictive models
- Supports OT/IT integration for flexible operations
Best for: Large industrial operations, manufacturers, OEMs, and utilities needing real-time maintenance visibility and predictive capabilities.
PTC ThingWorx
ThingWorx (by PTC) is a comprehensive Industrial IoT platform built to drive real-time diagnostics, predictive maintenance, and asset optimization across complex machinery and production systems.
Key Features: The platform supports robust device and asset connectivity, integrating data from sensors, PLCs, and OT/IT systems via standard industrial protocols. It includes anomaly detection, streaming analytics, real-time alerts, time-series dashboards, and pre-built predictive maintenance applications. Additional capabilities include digital twin support and role-based dashboards for maintenance teams.
Best for: Asset-intensive organizations—especially in discrete manufacturing, industrial equipment, utilities, or engineering—seeking to scale up their equipment maintenance programs from pilots to enterprise deployment.
IoT Data / Time-Series & Edge-First Platforms
AWS IoT
AWS IoT is a comprehensive suite of cloud-native services from Amazon Web Services that enables collection, processing, and analysis of IoT sensor and operational data for equipment maintenance.
Key Features: Includes AWS IoT Core for secure device connectivity, AWS IoT SiteWise to collect, model, and monitor industrial asset data with built-in anomaly detection, AWS IoT Events for detecting equipment state changes, AWS IoT Device Management for managing fleets, and SageMaker for custom ML models. Edge computing via AWS IoT Greengrass and dashboards complete the picture.
Best for: Industrial, utility, or manufacturing firms with large-scale or complex equipment fleets that want strong security, device management at scale, and flexibility in model building.
Microsoft Azure IoT
Microsoft Azure IoT is a comprehensive Internet of Things platform offering a broad suite of services designed to collect, process, and analyze equipment sensor data to support maintenance workflows.
Key Features: Provides secure device connectivity and management via IoT Hub, supports edge-native data processing, stream analytics, anomaly detection, digital twin modeling, and predictive modeling. IoT Edge allows running analytics closer to devices to reduce latency.
Best for: Industries with large or dispersed equipment fleets—such as manufacturing, energy, utilities, or transportation—needing scalability, strong security, and integration of predictive maintenance into their maintenance programs.
InfluxDB
InfluxDB (by InfluxData) is a time-series database platform purpose-built for handling high-velocity and high-volume IoT data, with features suited for equipment maintenance and predictive asset health monitoring.
Key Features: Provides fast ingestion of time-series metrics and events from sensors, efficient storage for long-term data, and real-time querying to detect anomalies or forecast failures. Supports edge architectures using Telegraf agents and predictive maintenance through combining historical and live streams.
Best for: Industrial equipment operators, manufacturing plants, utilities, energy providers, or any organization with numerous sensors needing real-time monitoring and historical trend analysis.
SUSE Edge
SUSE Edge is a cloud-native edge computing platform purpose-built for managing large fleets of distributed edge devices and applications at scale.
Key Features: Provides full lifecycle management of edge infrastructure, including OS, Kubernetes, and applications, with GitOps-driven automation for provisioning and updating even in disconnected settings. Uses lightweight Kubernetes (K3s, RKE2), strong edge security, and is validated for managing thousands of clusters.
Best for: Organizations operating dispersed or remote equipment (smart manufacturing, industrial IoT, utilities, logistics) needing reliable, secure, scalable infrastructure for edge data collection and preprocessing.
Telus IoT Connectivity & Device Management
TELUS IoT Connectivity & Device Management provides connectivity management and remote device management for fleets of sensors, gateways, and other equipment.
Key Features: Offers dashboards for managing IoT SIMs/global connectivity, usage monitoring, activation/deactivation of devices, and remote device firmware updates with security patching. Integrates raw telemetry for visibility and operational workflows.
Best for: Organizations with large or globally distributed IoT device fleets needing accurate telemetry, remote firmware control, and secure connectivity to feed into analytics tools.
Asset / Work Order / CMMS with IoT Extensions
UpKeep
UpKeep is a mobile-first, AI-powered asset operations platform (CMMS / EAM) that integrates IoT sensor data, preventive maintenance, and analytics to help maintenance teams improve equipment reliability.
Key Features: Supports preventive maintenance scheduling automatically triggered by meter readings or IoT sensor data, visibility into asset health, inventory, work orders, and dashboards. Offers “Edge” connected IoT sensors for condition monitoring and analytics for data-driven decisions.
Best for: Small to mid-sized industrial, manufacturing, facilities, or operations teams looking to leverage IoT-triggered maintenance.
MaintainX
MaintainX is a modern, mobile-first maintenance and asset management platform (CMMS/EAM) designed to combine traditional maintenance workflows with IoT-enabled data and AI insights.
Key Features: Offers preventive and condition-based maintenance scheduling triggered by IoT sensor inputs, asset management, work order tracking, parts/inventory control, AI anomaly detection, and predictive maintenance insights.
Best for: Small-to-mid-sized industrial and facility operations with many assets wanting to incorporate IoT sensor triggers and analytics to move toward condition-based maintenance.
Limble CMMS
Limble CMMS is a cloud-based maintenance management platform that combines CMMS/EAM functionality with IoT integrations to support proactive maintenance.
Key Features: Supports preventive maintenance automation, real-time asset tracking, spare parts inventory with alerts, IoT sensor integrations for condition monitoring, dashboards/KPIs, and open API to pull in telemetry.
Best for: Small-to-medium industrial, manufacturing, or operations teams moving from preventive to condition-based maintenance enabled by IoT.
Digital Worker / Workflow + Augmented Maintenance
Augmentir Connected Worker Platform
Augmentir is a digitized work system designed to support frontline and maintenance operations by combining workflows, workforce intelligence, and asset management.
Key Features: Includes a no-code digital work instructions builder, asset & issue management, integrations with CMMS/ERP/MES, predictive analytics for identifying maintenance inefficiencies, and AI assistants for troubleshooting and operator guidance.
Best for: Companies with sizeable frontline workforces and maintenance operations that want to digitize and modernize their maintenance processes and integrate with existing maintenance systems.
Comparison Table:
| Tool | Sensor / IoT Data Ingestion & Protocol Support | Predictive / Condition-Based Maintenance Capabilities | Edge / Real-Time Monitoring & Alerts | Maintenance Workflow Integration (Work Orders, CMMS etc.) | Best Fit Scenarios / Trade-Offs |
| SAP APM | Strong: supports sensor data, streaming, OT/IoT integration; SAP Edge components. | Yes: anomaly detection, risks estimation, remaining useful life. | Yes: real-time monitoring, alerts from live sensor/streaming data. | High: integrates with maintenance scheduling, work order / CMMS via SAP ecosystem. | Excellent for large industrial orgs already using SAP; high functionality with matching complexity and cost. |
| C3 AI Reliability | High: ingest large sensor data, historical logs etc. | Yes: ML-based failure prediction, RUL estimation. | Yes: real-time anomaly detection, low false positive. | Moderate-high: integrates with operations / reliability management workflows. | Good for asset-intensive orgs wanting advanced prediction & analytics; needs maturity around data infrastructure. |
| IBM Maximo Application Suite | Very strong: real-time sensor data, edge-device feeds, IoT connectors. | Yes: Predict modules, health scoring, predictive analytics. | Yes: condition monitoring dashboards, real-time alerts. | Very strong: full EAM / work order / asset lifecycle management. | Best for large enterprises with diverse assets; high implementation cost; steep learning curve. |
| AWS IoT | Very high: broad protocol support, various ingestion methods (MQTT, etc.). | Yes, but more via custom configuration / ML integration (e.g. SageMaker, IoT Events), not all pre-built PM tools. | Excellent: real-time processing, edge components, SiteWise etc. | Moderate: not primarily a CMMS, so workflow modules might need build or partner integrations. | Best for organizations with cloud infrastructure and developer capacity; flexible, scalable, more DIY. |
| Azure IoT | Very high: IoT Hub, device connectivity, IoT Edge etc. | Yes: supports predictive models; many templates & accelerators. | Yes: stream analytics, digital twins, edge processing. | Moderate: built-in dashboards, but CMMS/work order modules usually via partner or custom layers. | Good for Microsoft-centric companies, those that want hybrid cloud/edge; possibly lighter out-of-box workflows. |
| InfluxDB | Designed for high-velocity time-series data ingestion; strong for IoT use cases. | Partially: anomaly detection and forecasting possible, but advanced PM logic requires building. | Excellent: real-time querying, efficient storage, alerts. | Low-to-Moderate: doesn’t include built-in work order management; needs integration. | Best as a foundation or component for maintenance platforms; strong in telemetry & analytics but less in workflow. |
| Siemens Insights Hub | Strong sensor / PLC / OT integration, edge agents, digital twin capabilities. | Yes: condition monitoring, predictive analytics, health insights. | Yes: real-time dashboards, alerts, asset health apps. | Moderate-high: includes asset health / maintenance apps; likely integration with maintenance systems. | Very good for industrial operations especially manufacturing / utilities; strong edge + OT support. |
| PTC ThingWorx | Strong: supports varied data sources and device connectivity. | Yes: anomaly detection, predictive maintenance applications. | Yes: real-time dashboards & streaming analytics, twin models etc. | Moderate: has apps and templates; CMMS/work-order integration possible but may require configuration. | Suited for manufacturers/OEMs needing flexible platform to build custom maintenance apps. |
| SUSE Edge | Edge OS / device management, supports connectivity & infrastructure at edge. | Limited: primarily infrastructure; predictive PM needs layers above. | Yes: edge compute, local agents, secure device health monitoring. | Low: not a maintenance workflow tool; more infrastructure platform. | Best for ops with remote/disconnected sites, constrained connectivity; more of a data collection backbone. |
| UpKeep | Supports sensor/IoT triggers for condition-based PM (meter-based / sensor) to some extent. | Yes: condition-based maintenance; preventive; possibly simpler analytics. | Moderate: dashboards, alerts; perhaps less advanced real-time anomaly detection. | High: strong CMMS, work order scheduling, mobile access, preventive maintenance. | Best for small-to-mid organizations upgrading from reactive/preventive toward condition-based maintenance with manageable scale. |
| MaintainX | Supports IoT sensor input / condition-based triggers in its offering. | Yes: condition-based maintenance; less high-end predictive analytics than full suites. | Moderate: real-time visibility on asset inspections, sensor triggers. | High: full CMMS-style work orders, inspections, checklists. | Good for teams needing digitized workflows + IoT triggers without huge overhead. |
| Limble CMMS | Some IoT/sensor integrations; real-time data depends on integrations. | Moderate: likely simpler condition monitoring and alerts rather than full predictive modeling. | Moderate: dashboards, metrics of downtime etc. | High: CMMS/work order/asset management are core. | Best for small-to-medium operations that want to incorporate IoT-based condition alerts with their CMMS workflows. |
| Augmentir Connected Worker | Less focused on raw sensor data streaming; more on workflows and issue tracking; may ingest data. | Limited: focus is more on guided workflows, operator feedback, digitized inspections rather than deep predictive. | Moderate: gives visibility, operator input; alerts likely via issue detection. |
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