Every second, a modern manufacturing plant generates millions of data points. PLC registers cycle through readings, sensors capture temperatures and pressures, vision systems inspect parts, and motor drives report speed and torque values. The question isn't whether to collect this data — it's where to process it.

For the past decade, the default answer was "send everything to the cloud." But manufacturers are learning the hard way that shipping every data point from every machine to a cloud server creates problems: network bandwidth costs, latency that prevents real-time action, dependency on internet connectivity, and enormous cloud compute bills.

Edge computing — processing data at or near the source — is emerging as the practical answer for most manufacturing IIoT applications. Here's why it matters, how it works, and what to consider for your factory.

PTC ThingWorx has been a dominant name in industrial IoT since the early 2010s. Backed by PTC's $2B+ enterprise software ecosystem (including Windchill PLM and Creo CAD), ThingWorx positioned itself as the enterprise-grade IIoT platform for digital transformation. It's powerful, mature, and deeply embedded in Fortune 500 manufacturing operations.

But enterprise power comes at a cost — in dollars, complexity, and time-to-value. As the IIoT market has matured, a new generation of platforms like MachineCDN has emerged, challenging the assumption that industrial IoT must be expensive and complex.

This comparison helps you understand which platform fits your operation, your budget, and your timeline.

OEE (Overall Equipment Effectiveness) is the gold standard metric for manufacturing productivity. It combines three critical factors — availability, performance, and quality — into a single percentage that tells you how effectively your equipment is being used. World-class manufacturers achieve 85%+ OEE. The average? A sobering 60%.

The gap between 60% and 85% represents enormous untapped capacity. For a factory running $10M in annual production, improving OEE from 60% to 75% could unlock $2.5M in additional output — without buying a single new machine.

But you can't improve what you can't measure. And manual OEE tracking on clipboards and spreadsheets? That's how you get inaccurate data, delayed insights, and arguments about what actually happened on second shift.

This guide evaluates the best OEE monitoring software in 2026 and helps you choose the right platform for your operation.

Unplanned downtime costs industrial manufacturers an estimated $50 billion annually according to Deloitte's research on smart factory operations. The average manufacturer experiences 800 hours of equipment downtime per year — roughly 15 hours per week where production stops, orders are delayed, and money evaporates.

But here's what most guides won't tell you: reducing unplanned downtime isn't about buying more technology. It's about systematically understanding why your machines stop and building processes to prevent those stoppages. Technology is an enabler — not a solution by itself.

This guide covers what actually works, based on decades of manufacturing operations experience.

IoTFlows has carved a niche in the industrial IoT space with its vibration and acoustic monitoring sensors, particularly for rotating equipment. But it's not the right fit for every manufacturing operation. Whether you need broader data collection, faster deployment, or more comprehensive maintenance management, several strong alternatives exist in 2026.

We evaluated dozens of IIoT platforms and narrowed the field to the eight best IoTFlows alternatives — ranked by deployment speed, feature completeness, and real-world ROI.

When evaluating Industrial IoT platforms for manufacturing, two names increasingly appear in shortlists: MachineCDN and IoTFlows. Both promise to reduce unplanned downtime, improve OEE, and bring AI-powered insights to the factory floor — but they take fundamentally different approaches to getting there.

This comparison breaks down the real differences between these platforms so you can make an informed decision for your production environment.

Predictive maintenance (PdM) has moved from buzzword to business imperative. According to McKinsey, manufacturers implementing predictive maintenance see 10–40% reduction in maintenance costs and up to 50% reduction in unplanned downtime. But choosing the right predictive maintenance software in 2026 is more complex than ever — the market has exploded from a handful of vendors to dozens of platforms spanning CMMS add-ons, IIoT platforms, pure-play PdM solutions, and cloud hyperscaler toolkits. This buyer's guide helps manufacturing leaders navigate the landscape and choose the software that will actually deliver results in their environment.

MachineMetrics has built a strong reputation in the CNC machine monitoring space, delivering real-time OEE tracking and production visibility for discrete manufacturers. MachineCDN takes a broader approach, providing AI-powered predictive maintenance across all types of manufacturing equipment — from CNC machines and injection molders to compressors, pumps, conveyors, and HVAC systems. This comparison helps manufacturers understand when CNC-specific monitoring is enough and when you need a full-factory predictive maintenance platform.

Samsara and MachineCDN both appear in IIoT platform evaluations, but they serve fundamentally different markets. Samsara built its business on fleet management — GPS tracking, driver safety cameras, and vehicle telematics — and has been expanding into facility and equipment monitoring. MachineCDN was purpose-built for manufacturing, delivering AI-powered predictive maintenance with plug-and-play hardware and cellular connectivity. This comparison helps manufacturers understand where each platform excels and which one actually solves manufacturing problems.

Unplanned downtime costs industrial manufacturers an estimated $50 billion annually, according to Deloitte. The promise of predictive maintenance — using data and AI to predict equipment failures before they happen — has driven massive investment in software platforms. But the market is crowded, confusing, and full of vendors who claim "AI-powered predictive maintenance" when they really offer glorified threshold alerting. This comparison cuts through the marketing to evaluate the best predictive maintenance software platforms in 2026 based on real capabilities, deployment requirements, and outcomes.