Internet of Things. Smart devices. The connected factory. These are all phrases that we have heard more and more in recent years — and for good reason. They describe technology that is driving the cutting edge and future of manufacturing. IIoT stands for the Industrial Internet of Things, and it refers to a rapidly growing network of connected industrial sensors, equipment and systems that transform how manufacturers operate.
Industrial IoT enables smarter, data-driven decision-making by continuously collecting real-time performance data, transmitting it across secure networks and feeding it into advanced analytics platforms. At its core, the goal of IIoT is to maximize uptime, reduce downtime and give plant and operational leaders complete, 24/7 visibility into the health and performance of their production environments.
Through predictive maintenance, machine learning and edge computing, IIoT makes it possible to detect issues before they occur, optimize processes continuously and improve decision-making across every level of the enterprise.
What makes up the industrial internet of things?
When asking “What does IIoT mean?” the answer can include several components — based on the application at hand, the machinery in use, and the goals and needs of the organization.
Some of the most common and critical components that may be included are:
- Intelligent assets and sensors: Sensors, smart equipment and other connected devices form the backbone of all IIoT platforms. This technology enables the data collection and communication that makes IIoT what it is. While many new machines sold today are built with integrated sensors and network connectivity, the majority of IIoT implementations are accomplished through aftermarket add-ons — sensors, Wi-Fi connections, remote controls and more. The common thread through all of this equipment is that it enables real-time monitoring of intricate aspects of equipment operation, the transmission of that data and reception of control functions.
- Data infrastructure: Smart equipment and sensors are not effective if they don’t have the right data infrastructure with which to connect. Essentially, this is a strong, high-capacity network — which may include hardwired and wireless elements — that allows communication between machines, sensors, controls and a central data repository. This infrastructure must also comprise a large amount of data storage capacity, as well as computer processing power, since data is constantly being collected, monitored and analyzed in real-time.
- Analytics: Once equipment data has been collected and transmitted to the central repository, data analytics are executed. While IIoT data will often account for basic, standard performance metrics — for benchmarking as well as for baseline performance monitoring — the true, vast potential of this technology is unlocked through advanced analytics that allows for predictive maintenance, root cause analysis and prescriptive maintenance. With the support of cloud computing, IIoT analytics platforms can scale to handle large volumes of sensor data from distributed plants.
- People: No technology — no matter how advanced — will be successful without the right people to design, implement, operate and take action based on it. For IIoT, this means capable, trained and certified reliability engineers, maintenance personnel and data scientists. These experts must be up to date with the latest technology, analytics and strategic trends to be most effective.
IIoT vs. IoT: Key differences?
The terms IIoT (Industrial Internet of Things) and IoT (Internet of Things) may be confusing. They are often used interchangeably — although they have different meanings. While they have similarities, the differences between them must be understood to gain maximum effectiveness from an IIoT implementation.
These differences include:
- Industrial vs. consumer focus: As you can tell from the names, IIoT is specific to industrial applications, while IoT technology is a more general conception that can apply to a vast range of equipment and uses, mostly for consumer products. This means IIoT systems must meet higher standards for reliability, integration and uptime than typical smart home or wearable devices.
- Sensor-driven systems vs. general connectivity: IIoT is based primarily on sensor technology and performance tracking. Communication and connectivity are key to IIoT devices as well, but act in service of the sensors. The general IoT, on the other hand, is not exclusively reliant on sensors, and can in fact cover a broad range of technology and applications — with the common thread being that a device or product is “smart” or is able to connect to the Internet. In industrial environments, this sensor data is used to monitor everything from equipment temperature to vibration, supporting precise, real-time control.
- Secure and closed-loop vs. open access: Industrial Internet of Things implementations are, in general, built as a closed-loop network. Even though access is almost always available on-site as well as remotely, to those who have access, the purpose of an IIoT system is for communication, analysis and feedback loop that occur within a controlled — and secure — ecosystem. The broader Internet of Things, on the other hand, is less security-conscious and thus more of an open system. This level of control is essential to protect sensitive operations and intellectual property from cyberattacks or unauthorized access.
For manufacturers, this distinction is critical. IIoT networks are business-critical — downtime or compromise could end up leading to lost productivity, compliance failures or safety incidents. Implementing an IIoT solution without addressing these differences can result in technical gaps, increased risk or wasted investment.
How AI and machine learning enhance IIoT
AI and machine learning (ML) technologies amplify the power of IIoT in several ways:
- Predictive maintenance: Algorithms use real-time sensor data to predict component failures before they happen. This helps maintenance teams avoid unexpected breakdowns and plan repairs more efficiently.
- Process optimization: ML models spot inefficiencies and recommend adjustments to improve an industrial process based on historical and real-time data. These insights can lead to faster cycle times, better product consistency and reduced energy use.
- Autonomous control: AI-driven robots and systems can make decisions without human intervention — adjusting operations based on current conditions. This flexibility allows systems to respond instantly to variable inputs, like material quality or line speed.
- Anomaly detection: Machine learning can detect patterns that deviate from the norm, identifying potential safety or performance issues. Early detection allows operators to act before minor issues escalate into costly failures.
Additional benefits of integrating AI with IIoT include:
- Fewer unplanned outages
- Reduced repair costs and extended asset life
- Higher process consistency and output quality
- Smarter inventory and energy management
For example, AI can collect information from sensors and predict how soon a given component or system may break down due to wear. This enables preventive maintenance to be performed at the most convenient time to prevent any unexpected downtime. Its analytic capabilities also provide manufacturers with detailed insights about their processes that they would be unable to find otherwise. Algorithms can make determinations based on data collected from equipment much faster and with more accuracy than human observers can on their own. This in turn means manufacturers can make better-informed decisions in areas ranging from market trends to finding new revenue streams.
Another important use of AI with regard to IIoT is improving automation. Rather than being fixed to a predetermined routine, robotic equipment empowered by AI can make choices based on the information it receives in real time. This means they can operate nearly autonomously, without the need for human input in most cases.
Key benefits of IIoT in manufacturing
IIoT offers a broad range of benefits across industrial settings. The list below is just a few of the most common ones — depending on need and application.
There are nearly limitless ways IIoT can improve a business:
- Advanced, ROI-driven maintenance: Implementing tactics such as machine health monitoring and condition-based monitoring, mean that IIoT technology enables more proactive, prescriptive and ROI-driven maintenance — so that critical assets are always being monitored to ensure the facility stays operating at optimal levels.
- Increased productivity: By vastly reducing unplanned downtime — allowing more control over maintenance and facilitating remote control and operations — IIoT helps to maximize output, quality and OEE, delivering benefits to the business and the customer alike.
- Improved safety: IIoT improves safety in several ways:
- By greatly reducing or eliminating catastrophic equipment failures that can create dangerous situations
- By supporting increased automation to keep human workers away from the most dangerous tasks
- By enabling off-site work, where applicable
- Asset tracking and monitoring: Connected systems can send detailed information about the status of items across the supply chain. This provides stakeholders with all the information they need to make the most informed decisions or take action to prevent issues.
Real-world industry applications of IIoT
IIoT application impacts all areas of manufacturing, across all industries, and can benefit any facility with manufacturing production equipment.
Some of the key areas where IIoT is becoming most prevalent include:
- Aerospace: IIoT enables real-time monitoring of turbine engines and aircraft production lines for performance and defect prevention. This allows aerospace manufacturers to identify fatigue patterns early and maintain strict safety standards.
- Automotive: IIoT systems manage robotics, track part quality and synchronize logistics in lean manufacturing operations. It also supports predictive quality checks, helping avoid defects before they move downstream.
- Building products: IIoT sensors optimize equipment use and minimize energy waste in high-volume fabrication environments. Energy monitoring tools can reduce operating costs while ensuring compliance with sustainability benchmarks.
- Heavy equipment: Sensors track hydraulic pressure, temperature and vibration to ensure uptime and safety. This information is used to predict component fatigue and avoid breakdowns during critical production windows.
- Consumer packaged goods: IIoT improves batch consistency, automates production changeovers and monitors packaging systems. It enables quick adaptation to new SKUs and reduces waste during transitions.
- Paper & Pulp: Real-time data guides chemical dosing, water usage and machine calibration. IIoT platforms help balance performance with sustainability goals, like water conservation and emissions control.
- Power Distribution: Smart grids use IIoT to detect outages, load imbalances and grid efficiency opportunities. This data enables utilities to reroute power dynamically and reduce system-wide downtime.
- Tire and Rubber: Monitors vulcanization cycles, tracks machine health and ensures uniform quality. IIoT also detects early signs of mold fouling or curing inconsistencies, improving defect resolution.
How IIoT supports predictive maintenance
Predictive maintenance technology is made possible by installing sensors that measure vibration, pressure, flow rate, temperature and current, allowing manufacturers to identify patterns that indicate wear or failure. Instead of relying on fixed schedules or reactive repair, IIoT allows plants to service machines when necessary — before breakdowns occur.
These systems work by:
- Collecting continuous performance data
- Sending that data to analytics engines
- Flagging anomalies and triggering alerts
The result: Lower maintenance costs, fewer production delays and improved equipment reliability.
Getting started with IIoT in your facility
Thinking about implementing IIoT solutions? Start with these steps:
1. Assess your existing equipment: What machines are already sensor-equipped? What can be retrofitted?
2. Identify high-value use cases: Focus on problem areas like unplanned downtime, quality variation or energy overuse.
3. Plan for data infrastructure: You’ll need secure, high-capacity networks and robust storage and processing capabilities.
4. Involve the right partners: Look for vendors who offer both IIoT expertise and a deep understanding of your industry.
Challenges to expect include change management, legacy system integration and ensuring cybersecurity compliance. However, with a clear roadmap, the benefits of a successful IIoT deployment are well worth the effort.
Partnering with ATS for IIoT success
With over three decades of maintenance expertise in some of the most advanced production environments, ATS has extensive knowledge in IIoT applications and reliability-centered maintenance and implementation. We are ready to work with you to develop a plan tailored to your operational needs and objectives, using the latest IIoT technology.
Learn how ATS can help improve your OEE and drive continuous operational improvement through connected, intelligent technology — the core of modern industrial operations.
For more information, contact us today.
