Digital Transformation has become a solution to remain competitive for manufacturing businesses. Modern technologies make it possible to cope with steadily increasing customer expectations, consumers’ need for personalization, and supply chain complexity. They transform business and operational processes, reduce risks and drive efficiency. And the Internet of Things (IoT) is one such technology.
The emergence of IoT applications in the manufacturing industry has transformed its business models. And today, McKinsey’s Global Institute estimates IoT will have a financial impact of about $11 trillion in the next three years. So, the worldwide market of industrial IoT solutions in manufacturing will grow, and without a doubt, IoT is one of the core driving forces behind the Fourth Industrial Revolution today.
Manufacturing is one of the most affected sectors by IoT. And the implementation of IIoT solutions is a critical goal for numerous companies. In the subsequent sections, we’ll look closely at the main factors of IIoT adoption.
More efficient manufacturing and faster supply chain operations enable new products to move quickly from concept to commercialization. It is possible through direct communication between employees and network components. In addition, real-time access to data improves decision-making and facilitates better response to market changes.
IoT in manufacturing helps to create a safer working environment. Special sensors monitor working conditions, workers’ health, and risky activities, preventing accidents and injuries. In addition, IIoT can be responsible for safety in potentially hazardous sectors. For instance, it’s used for monitoring gas leakages or levels of harmful emissions tracking.
Internet of Things technology helps plan, design, operate, and innovate industrial facilities, improving product quality. And this, in turn, increases clients’ satisfaction. Moreover, the human factor is eliminated in the process, preventing the commercial distribution of defective products.
Companies reduce operational costs by optimizing assets, inventory management, and shortening equipment downtime. As a result, they can more efficiently use energy and create new sources of revenue.
This process requires increased stock keeping unit (SKU) diversity, which causes complications in manufacturing operations. Also, inventory and production process tracking becomes difficult and sometimes even impossible. IoT in manufacturing simplify mass customization as a real-time data source, making thoughtful forecasting and shop floor scheduling real.
IoT solutions are changing how manufacturing systems are built and operated, improving three critical areas:
Below are detailed overviews of each digital transformation dimension and examples of IIoT applications.
The industrial Internet of things offers a new level of visibility into the shop floor and field operations. The process follows: embedded into equipment sensors gather data on operating and spare parts conditions. Then collected information goes to the cloud for analysis, and its results are sent to the user application. Thus, shop floor managers receive a comprehensive overview of the production process.
Such visibility and detailed reviews were only available after IoT sensors implementing. Today, IIoT technologies fill the gaps and send data in real-time. Using them made it possible to make instant decisions and increase manufacturing productivity.
IoT applications, allowing to gain a higher level of visibility, in turn, divide into two categories:
IoT-driven manufacturing operations are related to product quality control, equipment performance optimization, monitoring, and human-to-machine interaction. As McKinsey’s Global Institute reported, as early as 2025, the cost of IoT-induced manufacturing improvements could exceed $470 billion a year.
IoT solutions for monitoring equipment usage provide businesses with real-time metrics and a complete picture of what happens at each process stage. It starts with the data obtained by IoT sensors about the machine’s operating parameters: operating speed, run time, goods output, and more.
Gathered data is transferred to the cloud for the following processing and development into informative insights. After data analysis, its visualized results are uploaded on user applications or web versions for manufacturing workers.
There are two methods of product quality monitoring.
Embedded IoT sensors enable tracking workers’ location and health conditions, such as skin temperature and heart rate. This information is sent to the cloud, which analyzes it with contextual data like weather feeds. Unusual behavior detection enables quick reporting of a safety threat and prevents falls, injuries, and overexertion. All this is a fundamental safety point in hazardous environments and various industrial sectors, for example, transport.
For example, if IoT devices detect a combination of a raised heart rate, high body temperature, and no movement for about a minute, they can identify it as overheating. In this case, the worker’s manager or other responsible person gets a relevant notification via a mobile app.
The industrial Internet of things is also applied in manufacturing companies to ensure proper use and the best return on assets, prolong machines’ lifespan, and improve reliability.
This category of applications includes the following features:
In the subsequent sections, we’ll look closely at each of them.
In 2017 American company Zebra Technologies conducted a global study of the manufacturing sector to analyze challenges and trends that affect the industry. According to it, IoT-based smart asset tracking solutions overtook traditional, spreadsheet-based methods in 2022.
Today, IIoT devices take on the employees’ responsibilities for asset tracking, freeing up to 18 hours of monthly work time. They eliminate errors in the manual data input and provide accurate real-time data – the status of physical assets, their movements, and their current location. Tracked assets may contain:
To enable asset monitoring, IoT in manufacturing needs radio-frequency identification (RFID). And each asset gets marked with an RFID tag, which has its unique ID linked to the other asset data, like serial number, physical parameters, cost, model, responsible employee, etc. All this info is stored in the cloud.
Let’s look at an example of what such a process looks like. For instance, were have a construction site and an asset – a crane with an RFID tag. When the machine leaves a storage facility, an RFID reader at the exit scans the tag. Thus, a saved record of the crane’s actual location is sent to the cloud. Similarly, when the machine drives on the construction site, an RFID reader on the entrance scans the tag and puts updates in the database. As a result, leveraging IoT data allows company employees to track any movement.
Similar to the previous paragraph, manufacturing-process inventory management solutions are based on IoT and RFID technologies. Each inventory article has an RFID tag with a unique ID storing the whole item data. And manufacturing companies use a radio frequency identification reader to read information from a label.
During production, an RFID reader scans inventory items’ IDs and sends data received to the cloud for the following processing and storing. Also, this data includes reader location and scanning time to track tags’ movement. Then the database identifies the actual position and state of the labeled items and visualizes results in employees’ applications.
Due to industrial IoT systems, manufacturers automate inventory tracking and reporting, monitor each tagged item constantly, and improve operational efficiency. As a result, smart manufacturing saves from 20% to 50% of inventory carrying costs.
The basis of predictive maintenance is insights obtained during constant equipment condition monitoring. First, built-in sensors collect data such as their state and performance (pressure, temperature, vibration frequency, and more). Then it goes to the database, where sensors’ information unites with the equipment history of use, metadata, and technical maintenance. After that, all related data is analyzed, visualized, and sent to manufacturing employees on a dashboard or into a mobile app.
But just reporting and visualization are not enough for the prediction, and machine learning (ML) algorithms come into play. ML data analysis uses algorithms to identify abnormal patterns leading to equipment failures. And data scientists use such patterns as a base for predictive models creation. Then the models are trained, tested, and as a result, they are used for the following purposes:
For instance, each machine parameter (condition, operation, and environment) is within the normal range. But the follower analysis of this combined data set with the predictive models helps reveal that engine failure is possible with a high probability. In this case, the predictive maintenance solution sends a notification about equipment potential failure and recommendations for the following steps to the maintenance specialist.
A smart manufacturing supply chain provides data about physical objects’ actual condition and the location at any process segment. And unlike traditional supply chain management methods, which give only general data about SKU (for ex., stock availability), IoT solutions share the real-time status of each item. IoT in manufacturing supplies properties of each individual item of the SKU, like the production date or expiration date.
Also, the Industrial Internet of Things monitors the conditions under which objects are stored and delivered. In the pre-IoT era, manufacturing specialists could check goods’ condition after they arrived at the delivery point. Today, tracking the state of materials, components, and items throughout the production process and the following delivery is possible. This is particularly true for nanomaterials, glass products, foodstuffs, and pharmaceuticals.
Today, the manufacturing industry faced the problem of shop floor operations distribution. Such difficulties include the support of production standards, the complexity of the manufacturing supply chain, the lack of local experts, the growing demand for customization, high logistics costs, etc. And traditional methods aren’t able to monitor everything, but it’s possible to control with IoT in manufacturing.
IoT-based predictive maintenance and timely prediction of potential failures may replace a team of experts. Also, the Internet of things helps workers to monitor production efficiency without direct access to the shop floor. One more example of how IoT in manufacturing promotes distributed operations is smart, connected products, which are complex systems of sensors, embedded intelligence, hardware, and cloud software. Thus, managers are constantly updated on possible overload conditions and breakages in all enterprises.
The industrial Internet of things definitely has vast potential. It contributes to maximizing productivity and reducing the costs of manufacturing processes. Digital technologies provide detailed analysis and forecasts, shorten time-to-market, satisfy clients’ needs, and more. Successful adoption of IoT in manufacturing is the foundation of businesses’ transition to a new level.
