SCADA systems have been the backbone of industrial monitoring for four decades. They've earned their place — when a plant needed visibility into process variables, alarms, and equipment status, SCADA was the only game in town.

But here's what's happening in 2026: manufacturers aren't replacing their SCADA systems because SCADA stopped working. They're looking for alternatives because SCADA was built for a world that no longer exists — a world where data lived on-premise, where remote access meant a VPN headache, where adding a new data point required an integrator and a purchase order.

The modern manufacturing floor demands real-time cloud analytics, mobile access, AI-powered predictive maintenance, and deployments measured in minutes, not months. Legacy SCADA can't deliver that. These alternatives can.

You've successfully polled your PLC. Registers are coming back as arrays of 16-bit unsigned integers. Your Modbus transaction completed without error. Now what?

The raw register values sitting in your receive buffer are useless until you transform them into meaningful engineering units — degrees Celsius, PSI, gallons per minute, kilowatt-hours. This transformation is where a shocking number of IIoT deployments break down, producing subtly wrong data that goes unnoticed for weeks until someone realizes the chiller outlet temperature has been reading 16,384°F.

This guide covers the real-world data normalization challenges you'll face when connecting to industrial equipment, and the strategies that actually work at scale.

EtherNet/IP is everywhere in North American manufacturing — from plastics auxiliary equipment to automotive assembly lines. But the protocol's layered architecture confuses even experienced controls engineers. What's the actual difference between implicit and explicit messaging? When should you use connected vs unconnected messaging? And how does CIP fit into all of it?

This guide breaks down EtherNet/IP from the wire up, with practical configuration considerations drawn from years of connecting real industrial equipment to cloud analytics platforms.

Food and beverage manufacturing operates under constraints that most other industries don't face. Your products expire. Your regulators show up unannounced. Your equipment touches what people eat. And when a production line goes down during a seasonal peak, the raw materials waiting in your cooler don't politely pause their biological clocks.

These constraints make food and beverage one of the most compelling use cases for industrial IoT — and one of the most underserved. Most IIoT platforms were built for automotive, aerospace, or heavy industry. They don't understand changeover frequencies, CIP cycles, cold chain requirements, or why a 2°F temperature deviation at 3 AM matters more than a 20°F deviation in a metal stamping plant.

This guide breaks down how IIoT specifically helps food and beverage manufacturers address their unique challenges — not in theory, but in the practical, measurable ways that justify the investment.

In pharmaceutical manufacturing, a deviation isn't just a quality issue — it's a regulatory event. Every out-of-specification reading triggers an investigation, potentially a CAPA (Corrective and Preventive Action), and possibly a batch disposition decision that can write off millions of dollars in product. FDA warning letters, consent decrees, and import alerts have shut down facilities for years over preventable equipment and process failures. Industrial IoT provides the continuous monitoring that traditional periodic checks miss — catching deviations in minutes instead of hours, and enabling predictive equipment management that keeps validated systems running within their qualified parameters.

The industrial IoT platform market has exploded. Gartner counts over 150 vendors. IoT Analytics tracks 450+. Choosing the right platform for your manufacturing operation feels like navigating a minefield of buzzwords, vendor claims, and analyst reports that somehow all recommend different winners.

Here's what most comparison guides won't tell you: 80% of IIoT platform evaluations end without a purchase. Not because the technology isn't ready — but because buyers get paralyzed by options, overwhelmed by complexity, and spooked by implementation timelines that stretch into quarters and years.

This guide cuts through the noise. We've evaluated 12 IIoT platforms across the dimensions that actually matter for manufacturing engineers and plant managers: deployment speed, total cost, features that deliver ROI, and the honest trade-offs each platform makes.

When manufacturing engineers evaluate predictive maintenance platforms, two fundamentally different philosophies emerge: monitor what the machine is already telling you through its PLC, or add external sensors to detect what the PLC can't see.

MachineCDN and Augury represent these two approaches in their purest forms. MachineCDN connects directly to PLCs and reads the data your machines are already generating. Augury attaches vibration and temperature sensors to rotating equipment and uses acoustic AI to detect failure patterns. Both claim to prevent unplanned downtime. Both deliver real results. But they solve different problems, require different infrastructure, and suit different manufacturing environments.

This comparison helps you understand which approach — or combination — makes sense for your operation.

Here's a question that trips up more maintenance managers than they'd admit: do you need a platform that tells you what's happening with your machines right now, or one that manages what to do about it?

MachineCDN and Fiix answer different versions of this question. MachineCDN is an industrial IoT platform that monitors machine health in real time and predicts failures before they happen. Fiix (now part of Rockwell Automation) is a cloud-based CMMS that manages work orders, asset records, spare parts, and maintenance workflows.

They're not direct competitors in the traditional sense — they solve different layers of the maintenance problem. But manufacturers evaluating maintenance technology investments often find themselves choosing between them, and understanding where each excels determines whether your maintenance program actually improves or just gets more software.

Choosing a manufacturing analytics platform is one of the highest-stakes technology decisions a plant manager can make. Get it right, and you unlock millions in avoided downtime, energy savings, and throughput gains. Get it wrong, and you're stuck with a six-figure consulting engagement that takes eighteen months to deliver a dashboard nobody uses.

MachineCDN and Sight Machine both promise to turn raw machine data into actionable manufacturing intelligence — but they approach the problem from fundamentally different directions. This comparison breaks down where each platform excels, where each falls short, and which type of manufacturer should choose which.

UpKeep has built one of the most popular mobile-first CMMS platforms in manufacturing, making it easy for technicians to manage work orders from their phones. MachineCDN has built an industrial IoT platform that tells you what your machines are doing in real time — before a technician ever needs to file a work order.

These platforms represent two different entry points into maintenance modernization. One digitizes the human side of maintenance (work orders, schedules, parts). The other digitizes the machine side (real-time data, predictive analytics, condition monitoring). Both reduce unplanned downtime, but through completely different mechanisms.

This comparison breaks down where each excels and helps you decide which investment delivers faster, more measurable returns for your operation.