Every manufacturing conference I’ve been to lately has someone talking about the “Industrial Internet of Things.” Usually with fancy graphics showing factories covered in sensors, all feeding into AI systems that magically optimise everything.

The reality is messier. But it’s also more accessible than the vendor presentations suggest. You don’t need to become a “smart factory” overnight. You can start small, learn what works, and build from there.

Here’s a practical guide based on what I’ve seen working in Australian plants.

What IIoT actually means in practice

Strip away the buzzwords and IIoT is really just: putting sensors on things, getting that data somewhere useful, and doing something with it.

That breaks down into:

• Sensors: Devices that measure something (temperature, vibration, pressure, flow, position, etc.)
• Connectivity: How the sensor data gets transmitted (wired, wireless, or a mix)
• Edge processing: Initial data handling near the source
• Platform: Where data is stored, analysed, and visualised
• Applications: What you actually do with the information

Let’s go through each layer.

Sensors: What to measure and how

The sensor you need depends entirely on what you’re trying to achieve. But here are the most common types in manufacturing:

Vibration sensors

The workhorses of predictive maintenance. Changes in vibration patterns often indicate bearing wear, misalignment, imbalance, or other developing issues. You’ll find these on motors, pumps, compressors, and rotating equipment generally.

Industrial-grade vibration sensors run from $200-2,000 depending on accuracy and features. Cheap consumer accelerometers don’t cut it—you need sensors rated for industrial environments.

Temperature sensors

Thermocouples and RTDs for high-accuracy process monitoring. Infrared sensors for non-contact measurement. Simple but essential for process control and equipment monitoring.

Current and power monitors

Clamp-on current sensors can detect motor issues, measure energy consumption, and spot anomalies without touching electrical systems. Relatively easy to retrofit.

Flow and pressure sensors

Essential for process industries. Measuring fluid flow rates and pressures across your systems gives visibility into process performance and equipment health.

Proximity and position sensors

Knowing where things are and whether they’re present. Useful for counting products, verifying positions, and monitoring material movement.

Environmental sensors

Temperature, humidity, air quality. Important for climate-controlled processes and worker safety monitoring.

Connectivity options that work in factories

Getting data from sensors to where it’s useful is often the trickiest part. Factory environments are harsh—electrical noise, metal structures, vibration, heat, moisture.

Wired connections

Still the most reliable option for fixed equipment. Industrial Ethernet (especially PROFINET and EtherNet/IP) is common in modern plants. Older serial connections (RS-485, Modbus) work fine for simple sensors.

Downside: installation cost and flexibility. Running cable in an operating plant is disruptive and expensive.

Wi-Fi

Works for some applications but has limitations. Standard Wi-Fi networks struggle with factory interference and handoff between access points. Industrial Wi-Fi solutions exist but require careful design.

LoRaWAN

Low-power, long-range wireless. Great for sensors that report infrequently and are spread across large areas. Battery life can exceed five years. Common for environmental monitoring, tank levels, and similar applications.

Cellular (4G/5G)

Useful when you need reliable connectivity without existing infrastructure. Many IIoT devices now have built-in cellular modems. Private 5G networks are emerging for larger industrial sites.

Mesh networks

Technologies like WirelessHART and ISA100 create self-healing mesh networks between sensors. Reliable but more complex to set up.

Most real implementations use a mix. Wired connections for critical equipment, wireless for the rest.

Edge vs cloud: Where should processing happen?

This is a bigger question than people realise.

Edge processing means analysing data locally, near the sensors. Benefits include lower latency, reduced data transmission costs, and operation even if internet connectivity fails.

Cloud processing means sending data to centralised servers (AWS, Azure, or similar). Benefits include easier scaling, more powerful analytics, and simpler maintenance.

For manufacturing, I generally recommend a hybrid approach:

• Critical real-time decisions happen at the edge
• Historical analysis, machine learning training, and cross-site comparisons happen in the cloud
• Local data is aggregated and summarised before transmission

An example: a vibration monitoring system might do basic anomaly detection at the edge (so it can trigger an immediate alert) while sending aggregated data to the cloud for longer-term trend analysis.

Platform options for Australian manufacturers

The platform is where everything comes together. Here are the main categories:

Industrial automation vendors

Companies like Siemens, Rockwell, ABB, and Schneider Electric all have IIoT platforms that integrate with their own equipment. If you’re already standardised on one vendor’s gear, this can make sense.

Cloud giants

AWS IoT, Azure IoT, and Google Cloud IoT provide infrastructure for building custom solutions. Powerful but require significant development effort.

Specialised industrial platforms

Companies like Samsara, Uptake, and PTC ThingWorx offer purpose-built industrial IoT platforms. Often faster to deploy than building on cloud primitives.

Local/boutique options

There are Australian companies offering IIoT solutions tailored to local industry. Worth exploring if you want local support and understanding of Australian manufacturing contexts.

A sensible starting point

If you’re new to IIoT, here’s a practical path:

Step 1: Pick one problem. Maybe it’s unexpected downtime on a critical machine. Maybe it’s energy cost in a particular process. Something specific and measurable.

Step 2: Instrument minimally. Put sensors on what matters for that problem. Don’t try to sensor everything.

Step 3: Use a simple platform. You don’t need enterprise-grade infrastructure for a pilot. Something that collects data and lets you visualise it.

Step 4: Learn and iterate. What worked? What didn’t? What would you do differently?

Step 5: Expand thoughtfully. Apply lessons from the pilot. Build internal capability. Don’t rush.

A Perth-based processor I know started with vibration sensors on just three critical pumps. That pilot taught them more about IIoT implementation than any vendor presentation could. Now they’re rolling out across the plant, but with realistic expectations and tested processes.

Common mistakes to avoid

Over-engineering the pilot: Start simple. You can always add complexity later.

Ignoring cybersecurity: Connecting operational technology to networks creates risk. Make sure your IT team is involved from the start.

Underestimating connectivity challenges: Test wireless options thoroughly in your actual environment. What works in a demo room may fail on the factory floor.

Forgetting about maintenance: Sensors fail. Batteries die. Calibration drifts. Plan for ongoing maintenance.

Not involving operations: If the people running the plant don’t see value, the project will stall.

IIoT isn’t magic, but it is genuinely useful when implemented thoughtfully. Start small, learn as you go, and don’t believe anyone who says it’s plug-and-play.