Manufacturing is undergoing a digital transformation known as Industry 4.0, driven by digital technologies like the internet of things, edge computing, AI, digital twins, and robotics. Instead of relying on scheduled reports, manual checks, and disparate manufacturing software, factories can get a real-time view of what is happening across production operations.

This is the main reason companies of all sizes invest in IoT development services: to build and implement an IoT solution tailored to their specific business needs and make accurate decisions on the shop floor. Before successful IoT integration, the best thing companies can do is dive into the IoT technology in manufacturing. In this article, we’ll cover everything about industrial IoT, including use cases, challenges, real-life examples, and statistics about the future of this technology.

Industrial internet of things (IIoT) explained

The industrial internet of things is a subcategory of IoT that focuses specifically on manufacturing, supply chains, and production operations. In other words, IoT in manufacturing refers to using smart sensors, devices, machines, and barcode/RFID scanners to collect and exchange data in real time for better production operations.

By analyzing data from industrial assets and workflows, manufacturers can optimize equipment maintenance, quality control, resource allocation, and supply chain operations. A typical IIoT ecosystem combines the following components:

Smart IoT sensors, devices, and machines for data collection and tracking

Connectivity solutions for reliable data exchange between devices and systems

Cloud and edge computing for data storage and processing

AI and ML for predictive maintenance, anomaly detection, and automation

Data analytics systems for analyzing collected data

Enterprise integration systems to connect IIoT data with production processes

When IIoT data is properly aggregated and analyzed, manufacturers gain visibility of their operations, helping identify inefficiencies, reduce asset downtime, improve shop-floor safety, and boost overall equipment effectiveness (OEE). As a result, smart factories produce high-quality products faster while lowering operational costs over the long-term.

IoT vs. IIoT: What is the difference?

In a nutshell, the IoT is the connection of physical devices with unique identifiers that connect to the internet to collect, transmit, and share data without human intervention. Examples of IoT apps include smart TVs, refrigerators, wearable health devices, and home appliances.

The Industrial Internet of Things (IIoT) is a network of smart devices and sensors with computing capabilities that collect, monitor, exchange, and analyze specific datasets to control manufacturing processes. Examples of IIoT apps are barcode scanners, RFID readers, connected machines, industrial robots, cameras, and signal lights.

Common IIoT use cases include predictive maintenance, digital twins, quality control, worker safety, inventory and energy management. Let’s consider them in depth.

Predictive maintenance

To ensure tight maintenance in a factory and avoid excessive, unplanned, or costly downtime, manufacturers rely on predictive maintenance. First, businesses deploy IoT sensors on target assets that collect equipment data under normal operating conditions.

Second, the technology allows workers to monitor equipment performance 24/7 and automatically alerts the maintenance team when abnormal indicators exceed specified thresholds. As a result, technicians can identify and investigate early warning signs of failure sooner, and repair or replace equipment components before they turn into an issue.

Since repair expenses can be massive, manufacturers are obviously interested in ways to cut down these costs and maintain the equipment’s proper performance for as long as possible. IIoT enables manufacturers to stay aware of their assets’ status, forecast equipment failures, and schedule timely maintenance, thereby ensuring optimal machinery efficiency and uninterrupted production flow.

Remote control

Sensors and IoT management platforms allow manufacturers to track the location, status, and movement of assets across different locations in real time. Even if a manufacturer’s facilities are distributed across multiple locations, all collected information is stored in a centralized hub, and every approved user has access to it. By providing a centralized view of assets, IIoT helps remote teams know where assets are, how they are performing, and whether operations are on track or require remote control.

With this visibility, manufacturers can easily compare production lines across facilities, control the flow of assets and resources, and even resolve certain issues via a virtual network. Needless to say, the ability to remotely control equipment and resources saves time, streamlines manufacturing operations, and adds transparency to asset monitoring.

Digital twins

A digital twin is a digital copy of a physical object (a manufacturing facility) that fully replicates all processes running within it. Digital twins capture data from connected IoT sensors installed across a factory and feed it into a digital interface to simulate, track, and test real-world behavior.

By collecting real-time production data, such as asset temperatures, vibration levels, equipment uptime, engineers can see what’s happening on the connected factory floor and how changes will affect the object and its processes from a distance. In this way, they can adjust production systems and optimize assembly processes in near real time.

There are various types of digital twins:

Digital twin prototype. Created before the physical object is built and is used to test the future object’s behavior and to evaluate possible risks.

Digital twin instance. Developed after the physical object is built and is used to run various scenarios and tests before applying changes to the physical object.

Digital twin aggregate. Modeled after the physical object is built and is used to collect information and to monitor the object’s parameters and capabilities.

No matter which type of digital twin a factory chooses, its implementation can help manufacturers reduce costs and downtime by monitoring equipment status and detecting anomalies. Digital twins also optimize equipment utilization and improve product quality by identifying performance issues and analyzing equipment KPIs. They also help to detect trends and maintain uninterrupted operations on production lines.

Inventory management

In traditional inventory management, the process of tracking and controlling assets is often manual and time-consuming, leading to inaccurate data, surplus stock, stockouts, and increased labor and storage costs. IoT sensors embedded in products, smart shelves, and cameras throughout a warehouse allow manufacturers to continuously collect data on inventory location, quantity, and condition. By using RFID tags and beacon technologies, factories can identify, locate, and track tagged assets throughout the facility. As a result, factories can:

Enhance inventory visibility by always knowing exactly what is in inventory at any given time and in what quantities.

Enhance automation via IoT and RFID systems that automatically trigger alerts or actions when thresholds are reached.

Collect accurate, real-time information on both stored items and inventory status for smart decision-making.

Better plan demand and avoid stockouts/surpluses by always knowing the amount of items in inventory.

Improve efficiency by eliminating manual processes like manual searches for specific items and improving visibility.

The combination of IoT and RFID not only improves inventory tracking, but also cuts down operational expenses and increases transparency, accuracy, and efficiency of inventory management.

Quality control

When it comes to quality control, IoT helps to make this process continuous, automated, and data-driven. IoT devices and sensors installed throughout the production line improve quality control by continuously monitoring production processes and equipment. This allows manufacturers to detect deviations in real time and prevent defects before they reach customers.

For example, an automaker can use IoT sensors installed on assembly lines to monitor parameters such as temperature, pressure, and vibration in real time. If a sensor detects that a parameter has gone outside the permissible range, the system can instantly alert operators, and even initiate the automatic removal of defective parts from the line. The data collected by sensors helps to determine where and why a defect occurs.

Apart from this, IoT can further help manufacturers:

Strengthen defect traceability by tracking products and production parameters end-to-end.

Improve supply chain management through condition monitoring and quality control from suppliers to production.

Reduce production faults by catching quality issues early, minimizing errors and rework.

Support automated inspection through IoT-connected sensors and cameras combined with computer vision and ML to detect defects faster and more accurately than manual checks.

Integrate IoT data with Quality Management Systems (QMS) to streamline quality processes, manage non-conformances, and automate documentation.

Energy management

An IoT-based energy management platform, combined with sensors and devices, enables manufacturers to automatically collect energy data and visualize it in real time via graphs and dashboards. Smart meters and sensors installed on or near machinery and other equipment continuously monitor energy consumption and equipment load. By analyzing this information, manufacturers can identify energy waste, detect inefficient equipment, and adjust its settings to eliminate the waste.

IoT-driven energy management platforms use predictive analytics to model energy usage patterns, detect anomalies, and prevent equipment failures. Connected sensors send automatic alerts when energy consumption exceeds set limits. The collected data and reports can also be used to support environmental, social, and governance (ESG) requirements and comply with regulations. With smart systems, manufacturers can move to greener operations by reducing carbon footprint, minimizing waste, and optimizing water consumption.

Worker safety

First, IoT-enabled devices and sensors identify risks that could lead to injuries or health hazards. Data collected via wearable devices, such as sensor-equipped hard hats, jackets, and watches, provides insight into workers’ movements, activities, and interactions with the industrial environment. Sensors can monitor staff health indicators such as heart rate, body temperature, blood oxygen levels, and fatigue, helping to spot early signs of overexertion and physical strain.

Second, IoT sensors can detect workplace hazards, including gas leaks, excessive noise, temperature changes, and unsafe weather conditions, and automatically send alerts when risks arise. Some IoT systems can prevent uncontrolled gas leaks by rapidly responding to temperature changes or gas concentration fluctuations. Moreover, remote equipment monitoring helps reduce exposure to hazardous areas by minimizing the need for manual inspections.

Beyond this, IoT systems can monitor worker actions and alert them if they are not using the appropriate safeguards and protocols. In addition to reducing injuries, such solutions improve employee compliance with safety protocols and reduce the risk of accidents or lost time.

Supply chain optimization

In manufacturing, IoT makes the supply chain more transparent and precise. By equipping machines and products with RFID/NFC tags or QR codes, manufacturers can track their movement and status in real time throughout the intelligent production process. By collecting and analyzing this data, manufacturers track and optimize the entire delivery route when changes of delays occur. As a result, manufacturers can improve inventory visibility and product traceability, ensuring on-time and more efficient delivery.

Along with that, IoT also helps meet strict compliance requirements, which is especially vital for pharmaceuticals, food production, and other sensitive industries. Smart sensors installed on vehicles, in containers, and on product packaging enable real-time tracking of the condition of goods, such as temperature, pressure, and humidity. This real-time monitoring enables immediate responses to deviations and prevents spoilage, damage, or degradation during transportation.

IoT architecture in manufacturing

IoT solutions for manufacturing typically consist of six layers: perception, connectivity, data processing, application, process and security.

Perception layer

The perception layer includes smart sensors, actuators, RFID readers, QR code scanners, and other devices deployed on the shop floor. They collect real-time, often raw, data from machines, production lines, products, and workers, and enable interaction with physical manufacturing processes.

Connectivity layer

The connectivity layer links devices, machines, and production systems through industrial networks, communication protocols, gateways, and edge devices. It facilitates data transfer between the perception layer and other layers of the IoT architecture, enabling data collection from sensors and the transmission of control commands to actuators.

Data processing layer

The processing layer collects, stores, and analyzes data from machines, production lines, and other shop-floor devices so users can get operational insights and take strategic actions. To make this happen, the layers include databases, data warehouses/lakes, analytics platforms, edge or cloud computing, real-time and near-real-time data processing, and AI/ML tools.

Application layer

The application layer provides web, mobile applications, and other software solutions to access and manage manufacturing operations. These tools present production information and AI-driven insights in an understandable format via dashboard and reports, and integrate IoT data with enterprise systems such as MES, ERP, and others.

Process layer

The process layer integrates governance procedures, operational workflows, and system management mechanisms to maintain the compliance and reliability of the IoT ecosystem of the shop floor. It coordinates data flows among machines, the production system, and operators while also performing critical tasks such as access control, system monitoring, and incident response.

Security layer

The security layer protects manufacturing IoT devices, systems, machines, and operational data across all architecture layers. It includes multiple security measures for each layer, such as physical device protection, end-to-end encryption, multi-factor authentication, network segmentation, access management, regular software updates, and risk assessments.

The core benefits of IoT in manufacturing

IoT technology plays an important role in manufacturing as companies phase in smarter, connected approaches and work toward more efficient production processes.

Reduced unplanned downtime

IoT devices and sensors can continuously monitor equipment operating parameters, such as temperature, vibration, and electrical characteristics throughout the equipment’s lifespan. This approach helps manufacturers predict malfunctions and breakdowns before they negatively affect the production process. As a result, the use of IoT reduces unplanned production stoppages, enables planned repairs at convenient times, and cuts down system downtime by up to 50%.

Improved production efficiency

By implementing IoT systems and sensors in the manufacturing process, businesses can automate many processes, detect equipment anomalies early, and reduce production errors, thereby increasing operational efficiency. Accurate, real-time data from machines, the environment, and production systems gives manufacturers greater visibility into operations, helping them make data-driven decisions that lead to up to 10%–20% improvements in production output.

Lower maintenance costs

IoT-enabled automation reduces the need for human intervention in repetitive or labor-intensive maintenance tasks. As IoT sensors constantly monitor equipment performance, they pick up on potential issues early and flag signs of equipment wear before they turn into costly breakdowns. As a result, manufacturers carry out maintenance when needed, reduce unnecessary repairs, extend equipment lifespans, and cut maintenance costs by up to 40%.

Increased supply chain visibility

IoT sensors and GPS-enabled trackers give manufacturers real-time insight into the condition and location of materials and products as they move through the supply chain. Improved asset visibility helps businesses optimize asset tracking and management, spot shortages or delays early, and respond swiftly to disruptions. In turn, companies can reduce unplanned stoppages by up to 25%, cut inventory levels by up to 30% while maintaining service reliability.

Enhanced product quality

IoT sensors boost quality control throughout production lines by checking products non-stop, finding exactly when defects happen, and regularly monitoring products’ condition to support compliance and quality assurance. Manufacturing companies that use IoT for automated quality monitoring cut defects by up to 40%, improving overall efficiency and customer satisfaction, and achieve a 26.9% increase in first-pass yield.

Better workforce safety

Due to 24/7 data collection by smart sensors, manufacturers can immediately learn about potential threats or ongoing accidents and respond quickly. Real-time tracking of workers’ health further improves workforce safety by enabling early detection of stress and other health risks and minimizing the likelihood of accidents and injuries associated with manual labor and hazardous tasks.

Challenges of IoT in manufacturing and their solutions

Adopting IoT comes with challenges and considerations that can hinder successful implementation. However, manufacturing companies can overcome these obstacles by taking the right steps from the start.

IoT implementation risks in manufacturing. Source

According to Deloitte statistics, operational risk remains the top concern for business leaders, cited by 65% of respondents, followed by strategic (42%), cyber (32%), financial (31%), and compliance risks (29%). These concerns can arise at different stages of IoT adoption. Below, we explore the key challenges manufacturers may face when implementing IoT and the ways to address them.

One of the key challenges in implementing IIoT is that existing organizational processes may not be ready to support connected, data-driven operations. IIoT does not fix the existing process gaps on its own. It makes them visible in real time via sensors that collect data and dashboards that visualize what is already happening on the shop floor. If processes are inefficient or poorly controlled, IIoT can simply create an “automated mess” that is more expensive and harder to manage. Resistance from employees can further slow down IIoT adoption.

To prepare for IIoT implementation, manufacturers should first bring order to their processes manually: examine how they actually operate, eliminate inefficiencies, and establish current metrics as a baseline. The next step is to implement IIoT in a single area where the process is already stable and predictable. Then, determine in advance, for each alert from IoT systems, who receives it, how much time there is to react, and what will happen if no one responds.

Data security

91% of manufacturing organizations surveyed by Deloitte reported experiencing at least one cybersecurity breach in the past year. An IoT infrastructure can comprise hundreds or even thousands of connected devices, each serving as a potential entry point for attackers. IoT devices continuously collect and transmit operational data, and the compromise of such a device could grant attackers access to it or even to industrial systems. Manufacturing leaders therefore need to take security seriously across manufacturing processes.

To guarantee strong industrial IoT security, companies have to start with basic security measures, such as implementing access controls, authentication protocols, and regular updates, then gradually strengthen protection across different layers. Best practices also include network segmentation, multi-factor authentication, secure remote access, security risk assessments, and real-time monitoring. Manufacturers should also provide security training for workers and regularly conduct audits of their environment and its protection.

Integration issues

It might be needed for businesses to connect IoT with MES, ERP, analytics and other systems to enable real-time data exchange, reporting, and better visibility across production operations. However, the integration of IoT solutions with existing infrastructure can be complex and costly due to legacy systems, incompatible data formats and communication protocols, along with limited interoperability between devices and platforms.

To ensure seamless IoT integration, manufacturers can rely on middleware or integration platforms, APIs, connectors, or gateways, adopt standardized protocols where possible, and implement a common data transformation model. They also need to establish internal standards for any new device/platform procurement and adopt a phased rollout with testing to minimize interruptions and delays.

Lack of technical expertise

According to Deloitte, 69%–72% of manufacturers report challenges in hiring skilled workers across fields that the IoT ecosystem depends on. IoT demands a blend of skills, including hardware integration, software development, data management, and system maintenance. The learning curve can be steep, and finding or training talent in these areas is costly.

Manufacturing organizations planning to implement IoT into the workflows have to either assemble an in-house team or outsource IoT development to a technology partner. If they choose the second option, they can benefit from expert support in selecting the most suitable IoT technologies, developing and integrating IoT solutions, ensuring their security and scalability, and providing long-term maintenance.

High implementation costs

The initial costs of implementing IoT technology can be high, particularly for small and medium-sized businesses. IoT infrastructure requires an investment in specialized hardware, sensors, network infrastructure, software platforms, and ongoing services. As a result, manufacturers delay IoT implementation due to uncertainty regarding ROI and fears that costs could spiral out of control as the project scales.

To keep the budget under control, manufacturing companies need to craft a cost-efficient strategy from the ground up. Manufacturers can take an MVP approach, launching Industry 4.0 projects in stages, using user feedback to refine them.

The other way is to select open-source IoT platforms and cloud-based infrastructure that can scale as the company’s IoT systems grow. It’s also recommended to consider the IoT-as-a-Service model to access IoT infrastructure and services without making large upfront investments. Companies can also engage experienced IoT development partners to optimize project costs.

Inadequate change management

Real-life examples of IoT in manufacturing

Leading global companies such as Siemens, Airbus, General Electric, Volkswagen, and Bosch are actively adopting IoT to create fully automated factories and accelerate production processes.

Bosch

Airbus

Siemens