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  • Vaibhav Verma

Industrial Internet of Things (IIoT): The need for modern manufacturing

Aktualisiert: 3. Jan.

The world’s leading manufacturing nations find themselves at the cusp of the manufacturing renaissance and their future standing in the global economy depends upon the modernization of the process of production of goods. The global middle income class is expected to grow and reach 5.5 billion by 2030. From 3.5 billion people in 2017 to about 5.3 billion by 2030, with China and India representing over 43.3% of the global middle class by 2030. (European Commission) This growth in buying power is followed by increased demand for high quality consumer goods at a reasonable cost. Manufacturers will have to increase the output and efficiency to meet this demand and to fulfil these objectives Internet of Things (IoT) and Big Data Analytics will play a key role in changing the manufacturing companies.

In 2021, around 47.5% German economy depended upon manufacturing exports as a component of their economies. (The World Bank) Therefore, it is crucial for the Mittelstand’s to embrace IoT and Big Data Analytics to maintain its global competence. German government created Plattform Industrie 4.0 to enable and shape the digital transformation in manufacturing (Plattform Industrie 4.0)


Industrial Internet of Things (IIoT)

IIoT is a combination of (Fraunhofer IOSB):

a. Cyber-physical systems in manufacturing, e.g. electronics, sensors, embedded systems in machines and their components which receive and deliver data for operation via industrial communication channels.

b. IT Systems, e.g. for customer order management, resource planning as well as cloud computing in the form of infrastructure, platform or Software-as-a-Service (IaaS, Paas or SaaS).

IIoT in manufacturing enables the cyber-physical systems collect and exchange data with IT systems. The smart sensors are deployed at different stages on the manufacturing floor. These sensors send the data to the IoT gateway, which acts as hub between cloud and IoT devices. The IoT gateway receives and transmits the data to the cloud for processing and analysis.

Applications of IIoT:

1. Industrial automation

The Internet of Things (IoT) is a key driving factor behind industrial automation. Automation of machines and IoT enables companies to function in efficient ways and helps streamline industrial systems with sophisticated software tools to monitor and make improvements for next process iteration. Several layers of devices are used at the industrial level to make these improvements. IoT devices from the plant floor, analyzers, actuators, robotics, etc. communicate data to local process control units which in turn sends it to SCADA (Supervisory control and data acquisition) (IOT and Industrial Automation).

SCADA is a top-level software application used for controlling industrial processes. It provides manufacturers with the tools to make data-driven decisions regarding their industrial process (Peter Loshin, 2021). Industrial automation tools like SCADA are used with smart sensor networks connected to central cloud which collect data.

Industrial automation improves efficiency, accuracy, reduces errors and provides remote accessibility via applications.

2. Connected factories

A connected factory uses the data from sensors to analyze real-time and historical data from manufacturing devices and processes. This analysis of data provides timely, detailed intelligence into the efficiency, productivity and facility yields. It also provides inventory levels, status of production machines and yield levels at manufacturing facilities. It benefits managers to efficiently shift production among machines or facilities, prioritize production, order raw materials, and prioritize production leading to improvements in cost, yield, agility, and efficiency (Cognizant).

3. Condition monitoring

Condition monitoring can be performed after the assets are connected and data is streaming to a centralized IIoT system. It allows the users to monitor parameters like vibration, pressure, temperature etc. and other key performance indicators to track operating conditions for all connected assets.

4. Fault prediction and preventive maintenance

There are two types of preventive maintenance: time-based and usage based. Preventive maintenance helps in planning as it provides prediction concerning probable future breakdown of equipment. To mitigate the breakdown, the manufacturer may plan a shutdown for repairing or plan a break. Such breaks are usually made to avoid considerable delays and failures which can be caused by major breakdowns. (Mentioned in detail in Article 1 – Data Science in Manufacturing)

5. Inventory, warehouse and supply chain management

A fully integrated IIoT solution can help manufacturers in managing supplies and inventory simultaneously at all manufacturing locations. By combining current, real-time activities and and events with desired outcomes like KPI performance, a manufacturer aligns its warehousing needs and orders in the supply chain based on analytics and decisions. This analysis depends on the data fed into the system analyzed by the software platform. This results in manufacturing agility enabling real-time insights into right-time actions of inventory management and supply chain decisions.

6. Integration of software for product optimization

Companies are implementing customized software for analyzing hordes of data collected from large sensor networks installed in manufacturing plants and machines. The analysis of the collected data provides insights into manufacturing improvements and gives better overview of process improvement strategies for product optimization. The manufacturing improvements could be optimization of machinery for better performance and output. This results in saving costs post analysis of data and its behavior patterns over a period. Prior to introduction of these software tools the analysis of data was difficult, inaccurate and time consuming.

7. Power management

Specific sensors can detect variation in the environment of the manufacturing plant and can trigger to power on or off the humidifiers, liquid flow, lights, air conditions, etc. for efficient power management resulting in cost saving.

Apart from the various opportunities presented by IIoT, there is a challenge pertaining to data standardization. Data standardization converts the incoming data from different machines and sources into a standard format. Modern industrial machines come equipped with a plethora of sensors which relay data to the IT systems. Manufacturing companies employ equipment made by different OEMs which have different data formats and protocols. Without standardized data, it is difficult to process the disparate data and translating them into singular model from which people and systems can consume the data for analysis (Graham Immerman, 2022)


(n.d.). Retrieved from The World Bank:

Cognizant. (n.d.). Connected Factory. Retrieved from Cognizant:

European Commission. (n.d.). Developments and Forecasts of Growing Consumerism. Retrieved from Knowledge for Policy:

Fraunhofer IOSB. (n.d.). Industrial Internet of Things (IIoT). Retrieved from Fraunhofer IOSB:

Graham Immerman. (2022, March 22). The importance of Data Standardization in Manufacturing. Retrieved from Machinemetrics:

IOT and Industrial Automation. (n.d.). Retrieved from Hitachivantara:

Peter Loshin. (2021, December). SCADA (Supervisory control and data acquisition). Retrieved from Techtarget:

Plattform Industrie 4.0. (n.d.). Retrieved from Plattform Industrie 4.0:

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