IIoT in Manufacturing: How the Industrial Internet of Things Improves Efficiency
Modern manufacturing is impossible to imagine without intelligent data collection and analysis systems. The Industrial Internet of Things (IIoT) has changed the approach to equipment management, product quality and energy consumption. According to analysts, the IIoT market in 2025 is valued at $276.6 billion, with projected growth to $964 billion by 2035. This is not just a technology trend but a real tool that delivers measurable results within the first year of implementation.
If you work with variable frequency drives, programmable logic controllers or industrial sensors, you already have the basic infrastructure for building an IIoT system. All that remains is to connect these components into a unified network and configure analytics.
Traditional vs. IIoT Approach to Manufacturing
| Parameter | Traditional Approach | IIoT Approach |
|---|---|---|
| Equipment Monitoring | Scheduled rounds, visual inspection | Continuous real-time data collection |
| Maintenance | Scheduled or after breakdown | Predictive, based on condition analysis |
| Quality Control | Sampling, at final stage | End-to-end, at every process stage |
| Energy Consumption | Fixed modes, excess consumption | Adaptive control, savings up to 28% |
| Response to Deviations | Minutes or hours | Seconds, automatic correction |
| Data Collection | Manual log filling | Automatic, with cloud storage |
| OEE (Overall Effectiveness) | 45–65% | 75–90% |
| Unplanned Downtime | 5–15% of working time | Reduction by 30–40% |
Predictive Maintenance: Anticipating Failure Before It Happens
The main advantage of IIoT in manufacturing is the ability to transition from reactive to predictive maintenance. Instead of waiting for a motor to fail, the system analyses vibration, temperature, load current and other parameters to forecast the moment of failure with up to 90% accuracy (according to IBM).
In practice, this works as follows. Vibration and temperature sensors installed on electric motors transmit data to a PLC, which compares current values with reference values. If bearing vibration has increased by 15% over the past two weeks, the system generates a warning and schedules replacement during the nearest maintenance window — without stopping the production line.
Results of implementing predictive maintenance, according to McKinsey and Gartner:
- Reduction of unplanned downtime by 30–40%
- Decrease in maintenance budget by 15–25%
- Return on investment within 12–18 months
- Increase in mean time between failures (MTBF) by 20–35%
Read more about how IIoT reduces automation costs in our article on Industrial Internet of Things technologies.
Variable Frequency Drives and IoT: Intelligent Motor Control
Modern variable frequency drives (VFDs) already have built-in interfaces for connecting to industrial networks — Modbus RTU/TCP, PROFINET, EtherNet/IP. This allows not just controlling motor speed, but collecting detailed telemetry: current, voltage, frequency, torque, heatsink temperature, and operating hours.
VFD integration with an IIoT platform provides specific benefits:
- Energy efficiency — the PLC analyses process load and dynamically adjusts rotation speed. Research shows this delivers a 28% reduction in total energy consumption and a 28% decrease in peak demand
- Remote monitoring — the operator sees parameters of all plant drives on a single dashboard, receives anomaly notifications and can change settings without being physically present at the equipment
- Fault diagnostics — current waveform analysis can detect rotor imbalance, bearing wear or alignment problems before visible symptoms appear
- Start-up optimisation — data on the number of starts, acceleration duration and current overloads help configure optimal acceleration and deceleration ramps
IIoT System Architecture in Manufacturing
Building an industrial IoT system does not require replacing all equipment. Most enterprises start by building on top of existing infrastructure. A typical architecture consists of four levels:
Level 1. Field Level — Data Collection
At this level, sensors (temperature, pressure, vibration, flow), actuators and variable frequency drives operate. They generate primary data about equipment status and process conditions. Modern industrial sensors support IO-Link, HART protocols and wireless standards WirelessHART and ISA100.11a, simplifying their network integration.
Level 2. Edge Level — Edge Computing
Programmable logic controllers and edge gateways aggregate data from the field level, perform primary filtering and local analytics. Edge computing allows processing critical data without delay, without waiting for a response from the cloud server. For example, if a VFD detects a current spike, the PLC instantly reduces the load, and only then transmits the event to the cloud for analysis.
Level 3. Network Level — Data Transmission
Industrial protocols (MQTT, OPC UA, AMQP) ensure reliable data transmission from the edge level to the cloud platform. MQTT is particularly popular in IIoT due to its low network overhead and QoS level support, which guarantees message delivery even with an unstable connection.
Level 4. Cloud Level — Analytics and Visualisation
Cloud platforms (AWS IoT, Azure IoT Hub, Siemens MindSphere) store historical data, train machine learning models for predictive analytics and provide interfaces for KPI visualisation. This is where dashboards with OEE, MTBF, MTTR and energy consumption metrics are generated.
Digital Twins: A Virtual Copy of Production
Digital Twin technology is closely linked to IIoT and is one of the most promising directions. The digital twin market in 2025 is valued at $18.9 billion, with projected growth to $428 billion by 2034 (CAGR 41.4%).
A digital twin is a virtual model of a physical object (a pump, conveyor, or entire workshop) that updates in real time based on sensor data. This enables:
- Modelling parameter changes without risk to real equipment
- Testing new operating modes of variable frequency drives on a virtual model
- Predicting system behaviour under changing load or external conditions
- Training staff on a realistic copy of the production line
OEE: How IIoT Impacts Overall Equipment Effectiveness
OEE (Overall Equipment Effectiveness) is a key manufacturing performance indicator that accounts for three factors: equipment availability, performance and quality. World-class enterprises achieve OEE at the 85% level, but the industry average remains around 60%.
IIoT impacts each OEE component:
- Availability — predictive maintenance reduces unplanned stops. Automatic alerts on critical parameters allow responding in seconds rather than minutes
- Performance — optimisation of electric drive operating modes through VFDs, automatic speed adjustment to current load, minimisation of idle running
- Quality — parameter control at every production stage, automatic rejection of defective products, statistical analysis of defect causes
According to HiveMQ research, 29% of companies that implemented IIoT recorded productivity growth, and 23% reported improved overall OEE.
Smart Sensors: The Eyes and Ears of an IIoT System
Without quality sensors, it is impossible to build an effective IIoT system. Modern industrial sensors are not just measuring devices. They have a built-in microprocessor for signal preprocessing, self-diagnostics and the ability to transmit data via digital protocols.
Types of sensors required for a manufacturing IIoT system:
| Sensor Type | What It Measures | Where It Is Used |
|---|---|---|
| Vibration sensors | Acceleration, vibration velocity | Bearings, gearboxes, motors |
| Temperature sensors | Surface and ambient temperature | Motor windings, VFD heatsinks |
| Current sensors | Current amplitude and waveform | Power circuits, feeders |
| Pressure sensors | Liquid or gas pressure | Hydraulic systems, pneumatics |
| Encoders | Position and rotational speed | Servo drives, positioning |
| Flow sensors | Liquid or gas volume | Water treatment, HVAC |
Read about trends and prospects for smart sensors in our industrial sensor overview.
Practical Example: IIoT for a Pumping Station
Consider a specific scenario. A water utility pumping station is equipped with three pumps driven by 15 kW electric motors. Before IIoT implementation, all three pumps ran continuously at fixed speed, with flow regulation handled by throttle valves.
After modernisation:
- Each motor received a variable frequency drive with Modbus TCP
- Pressure sensors were installed at the collector inlet and outlet
- A PLC implements cascade control: depending on water demand, it activates one, two or three pumps and adjusts their speed
- Data is transmitted to a cloud platform for trend analysis and predictive maintenance
Results after the first year:
- Energy consumption decreased by 35%
- Bearing life doubled thanks to soft starting
- Zero emergency shutdowns — the system provided early warning about maintenance needs
- Return on investment in 14 months
IIoT Security: Protecting the Industrial Network
Connecting production equipment to the network creates new cybersecurity risks. Industrial enterprises become targets for cyber attacks, and the consequences can be more severe than in conventional IT — from equipment damage to threats to personnel safety.
Core principles of IIoT system security:
- Network segmentation — separating IT and OT networks using industrial firewalls and DMZ
- Encryption — TLS for MQTT, VPN for remote access, certificates for device authentication
- Firmware updates — regular firmware updates for PLCs and edge gateways to close vulnerabilities
- Traffic monitoring — intrusion detection systems (IDS) specialised for industrial protocols
- Physical security — restricting physical access to network ports and controllers
IIoT Implementation ROI: Real Numbers
For a manufacturing enterprise, IIoT return on investment depends on the scale of implementation and the initial level of automation. A typical cost and payback structure looks like this:
| Cost Category | Investment Range | Payback Period |
|---|---|---|
| Sensors and edge devices | 30–40% of budget | 6–12 months |
| Network infrastructure | 15–20% of budget | 12–18 months |
| Cloud platform and analytics | 20–25% of budget | 12–24 months |
| Integration and configuration | 15–25% of budget | 6–12 months |
According to McKinsey, most companies achieve full return on investment within 12–18 months. Moreover, over 30% of manufacturing enterprises plan to double their number of IoT devices in the coming years, demonstrating proven technology effectiveness.
How to Start IIoT Implementation
You do not need to automate all processes at once. The practical approach is to start with a pilot project on a single production line or section:
- Identify pain points — where the greatest downtime occurs, where energy consumption is highest, where defects most frequently arise
- Select equipment — variable frequency drives with industrial protocol support, PLCs with cloud connectivity, sensors with digital output
- Launch a pilot — collect data for 2–3 months, compare with the previous period
- Scale up — extend the solution to other areas based on lessons learned
If you are interested in specific solutions for industrial automation, check out our VEICHI VFD overview — they have built-in IoT protocol support and can form the foundation of your IIoT system.
