IIoT in Manufacturing Industries

The impact of the IIoT can be measured in three ways

From the top floor, you have visibility of the shop floor and field operations:

The Industrial Internet of Things provides a groundbreaking level of visibility into shop floors and field operations, as well as the ability to manage enterprise resources with ease. IIoT solutions bridge the gaps that cause ERP and MES systems to reach their limits: manual data entry and the inability to deal with comprehensive information (real-time equipment status reports, inventory item locations, and so on).


IIoT helps firms to significantly boost manufacturing process productivity by supplying manufacturers with second-by-second shop floor data. According to IBM, employing IIoT insights to optimise industrial processes can result in a 20% increase in product count from the same production line.

There are two types of IoT applications that assist manufacturers to acquire more vertical visibility:

  • Supporting applications for manufacturing operations
  • Applications that aid in the management of industrial assets.
Manufacturing operations aided by the Internet of Things:

According to a McKinsey study, the benefits of IoT applications in operations might be worth more than $470 billion per year by 2025. Monitoring and optimising equipment performance, production quality control, and human-machine interaction are all examples of IoT uses for manufacturing.

Keeping track of how much equipment is being used:

IoT solutions for monitoring machine use, according to ITIF study, can boost manufacturing productivity by 10 to 25% and generate up to $1.8 trillion in global economic value by 2025. IoT solutions for machine utilisation monitoring provide businesses with real-time equipment usage measurements, giving them a full picture of what’s going on at every stage of the manufacturing process.


Monitoring machine utilisation begins with obtaining important data regarding machine operating parameters from sensors, SCADA, or DCS systems, such as run time, actual operating speed, product production, and so on. Data is collected in real time and sent to the cloud to be processed.


The cloud collects data and transforms it into actionable insights about equipment utilisation KPIs (TEEP, OEE, setup and adjustment time, idling and minor stops, etc.). The results of the data analysis are visualised and provided to production workers via a user app (either web or mobile).

Condition monitoring is used to control product quality:

The quality of the commodities produced can be monitored in two ways: by evaluating a WIP (work in progress) as it progresses through the production cycle, or by monitoring the condition and calibration of the equipment on which a product is manufactured. 


Although quality control based on checking WIPs yields more accurate findings (it aids in the discovery of tiny flaws such as inconsistencies in part alignment), there are some drawbacks that limit its application:


  • Only discrete manufacturing can benefit from quality control based on WIP inspection.
  • Because WIPs are manually examined, it is costly, time-consuming, and labor-intensive.
  • Because inspecting every WIP is rarely practicable, the approach provides a fractional view.


The second technique, which is based on monitoring machine status and calibration, has a narrower scope and uses a simple binary categorization of “excellent” and “not good.” It does, however, aid in the detection of manufacturing bottlenecks, the identification of poorly tuned and/or underperforming machines, the prompt prevention of machine damage, and more.


Equipment calibration, machine conditions (speed, vibration, etc.) and environmental factors (temperature, humidity, etc.) are monitored to determine when they go above normal thresholds in order to monitor the quality of the manufacturing process. 


A quality monitoring solution pinpoints the source of an issue, sends an alarm, and advises a mitigation action to correct or adjust the machine and reduce the output of low-quality products if sensor readings are approaching the thresholds that can lead to a probable product defect.

Monitoring safety:

Workers in a variety of industries, including mining, oil and gas, transportation, and others, are given RFID tags that track their location as well as wearable sensors that monitor their heart rate, skin temperature, galvanic skin response, and other information.


The sensor data is sent to the cloud, where it is analysed against contextual data (such as data from environmental sensors, legacy work planning systems, weather feeds, and so on) to detect unusual behaviour patterns (such as sudden vertical movements, unusual heart rates, and so on) and to protect workers from falls, overexertion, and other injuries, as well as to report a safety threat in a timely manner.

For example, a person suffering from overheating might have a high skin temperature, a fast heart rate, and no movement patterns for roughly a minute.


An IoT solution notifies an employee’s responsible person (a worker’s management, a doctor, etc.) via a mobile application if such a circumstance is detected.

Industrial asset management IoT applications:

The Internet of Things is used in manufacturing to assure proper asset utilisation, extend equipment service life, increase dependability, and offer the highest return on assets, in addition to boosting the efficacy of industrial operations. 


The following are examples of IoT applications that help with industrial asset management:

  • Asset tracking in the industrial sector
  • Inventory control is important.
  • Maintenance that is planned in advance (based on condition monitoring).
Asset tracking in the industrial sector:

Smart asset tracking solutions based on RFID and IoT are likely to supersede traditional spreadsheet-based approaches by 2022, according to Zebra’s 2017 Manufacturing Vision Study. 


IoT-based asset management systems alleviate the tracking load from staff (freeing up to 18 hours of monthly working time) and minimise inaccuracies associated with manual data input by giving accurate real-time data about the enterprise’s assets, their statuses, locations, and movements.


IoT and RFID work together to provide asset tracking in production. Each asset is identified with an RFID tag that serves as an asset identifier, whether it’s a magnetic locator or a crane. 


Each tag has a distinct ID that is linked to information about a specific asset. The ID as well as the asset data are both kept in the cloud. Physical attributes, cost, serial number, model, allocated employee, area of use, and other data may be included in the asset data.


An RFID reader situated at the yard entrance scans the tag attached to the crane and stores the record about the asset leaving the yard to an in-cloud database once an asset, such as a crane, departs an equipment storage yard. 


When the crane enters a building site, for example, an RFID reader at the site entry reads the tag and updates the data in the database. The ability to see the movements of assets is made possible by logging such data along the asset’s trip.


Furthermore, GPS tracking may be utilised to determine the location of movable assets, such as construction machines. 


Asset tracking technologies are also used to calculate utilisation for transportable assets. Technicians can locate idling or underused machinery and arrange preventative repair by looking at how long any movable (say, a bulldozer) is in use.

Inventory control in a business:

Manufacturers can employ IoT-driven inventory management solutions to automate inventory tracking and reporting, maintain constant visibility into the status and location of individual inventory items, and reduce lead times (the time between placing an inventory order and receiving it). 


Smart inventory management solutions are said to save 20 percent to 50 percent of an enterprise’s inventory holding costs as a result of these enhancements.


IoT and RFID technologies are used in manufacturing to implement inventory management solutions. A passive RFID tag is attached to each inventory item. Each tag has its own unique ID that contains information about the item to which it is linked.


RFID readers are used to retrieve data from the tags. The IDs of tags are captured by a reader and sent to the cloud for storage and processing. 


The data regarding the location of the RFID reader and the time of the reading is communicated to the cloud to establish the location and movements of the scanned tags, as well as the tags’ IDs. 


The cloud determines the location and state of each item, visualises the results, and presents them to the users.

Condition monitoring and predictive maintenance:

Predictive maintenance solutions based on the Industrial IoT are predicted to lower factory equipment maintenance costs by 40% and generate $630 billion in economic value annually by 2025, according to Deloitte. 


Predictive maintenance projects are being piloted by 55 percent of enterprises, indicating that the solution is leading IoT adoption. From a technology standpoint, this is how it is done.


Predictive maintenance is based on the information gleaned from continuous equipment monitoring.


Sensors are attached to a piece of equipment, which collect data on a variety of characteristics that determine its health and performance, such as temperature, pressure, vibration frequency, and so on.


Sensor readings are merged with metadata (equipment model, configuration, operational settings, etc. ), equipment usage history, and maintenance data received from ERP, maintenance systems, and other sources once the real-time data from many sensors has been collected. 


On a dashboard or on a mobile app, all of the data is processed, displayed, and provided to shop floor workers.


However, reporting and visualisation alone aren’t enough to foretell the future. To enable prediction, machine learning techniques are applied to the combined data set to identify anomalous trends that could lead to equipment breakdowns.


Predictive models are built on the basis of detected data patterns by data scientists. The models are trained, tested, and then used to detect potential problems, forecast when a machine will fail, pinpoint operating circumstances and machine usage patterns that lead to failures, and so on.


For example, the machine’s condition parameters (such as temperature and vibration), operational parameters (such as speed and pressure), and environmental parameters (such as humidity and temperature) are all within standard limits.


Combining these factors and assessing the whole data set against prediction models, on the other hand, reveals that the combination of parameters that are normal when taken separately can cause, for example, a machine’s engine failure. 


Once a potential failure has been discovered, the predictive maintenance system provides a message to the maintenance team, informing them of the potential for degradation and advising them on how to avoid it. Predictive maintenance capabilities is demonstrated in our smart factory demo.

Having visibility across the manufacturing supply chain is essential:

End-to-end supply chain visibility is still a long way off for 52 percent of supply chain managers. The outlook for IoT-driven manufacturing supply chain management solutions, on the other hand, is very positive:


 According to IDC, by 2020, 80 percent of supply chain exchanges will take place over cloud-based networks. According to the same source, the shift to smart, IoT-enabled supply chain management solutions is expected to boost supply chain productivity by 15% and cost efficiency by 10%.


Smart supply chain management solutions give producers real-time visibility over the location, status, and condition of any object (whether it’s a single inventory item on a warehouse shelf or a truck transporting supplies) at every point in the manufacturing supply chain.


The ability to transition from knowing whether a particular SKU is available to knowing the status of each item in that SKU is an even greater benefit of IoT applied to manufacturing supply chain optimization.


Manufacturers could only acquire generic information on the availability of an SKU using traditional supply chain management systems, for example:


SKU X has 1,123 items in Warehouse 3.


With IoT in the manufacturing supply chain, businesses may collect information about the location as well as the attributes (such as manufacture date, shelf life, and so on) of each unique SKU item. Consider the following example:


Warehouse 3 has 1,123 SKU X products, including:


12 days ago, 1,000 things were made.


22 days ago, 123 things were made.


IoT is used to monitor the conditions in which the objects are stored and transported, in addition to tracking their position and attributes. 

The condition of items could only be monitored once they reached at the delivery site before IoT came into play. Materials, components, and goods may now be tracked in transit, which is especially useful for manufacturers of breakable and perishable goods (e.g. pharmaceuticals, food, glassware, modern nanomaterials, etc.).


Consider a pharmaceutical firm that uses a third-party logistics service provider to deliver an order to a distribution location. The temperature inside the containers is monitored by sensors affixed to the containers. 


Assume that the temperature inside the containers is beginning to rise due to a cooling system failure. The deviation from the acceptable threshold is ‘detected’ by a temperature sensor connected to the container’s inner wall. 


The IoT solution tells the manufacturer that the delivery conditions have been breached, as well as the driver, who resets the cooling system, preventing the transportation of medicines from spoiling.

Remote and outsourced operations visibility:

High shipping costs, rising demand for customization, the global supply chain’s complexity, and a scarcity of local talent (thus the need to outsource) all necessitate the distribution of shop floor operations.


When a company creates or buys a manufacturing plant in another city, state, or nation, it must still adhere to the same manufacturing and production requirements (material testing, industrial automation, predictive maintenance, and other). Compliance with manufacturing standards, which is impossible to check using traditional means, can be tracked using IoT.


For example, IoT-based predictive maintenance and early detection of probable failures allows maintenance actions to be planned ahead of time and removes the need for a local repair staff. 


Similarly, IoT-driven utilisation monitoring systems allow manufacturers to keep an eye on the efficiency of their production operations without having direct access to the shop floor (by delivering real-time equipment efficiency measurements).


Industrial smart, connected gadgets are another illustration of how IoT fosters distributed operations (SCPs). Hardware, sensors, networking, embedded intelligence, and cloud software are all part of smart, connected devices.


For example, industrial smart, connected products located at a manufacturing affiliate in Texas allow enterprise managers in California to get real-time data on a variety of SCP operating (e.g., changes in the temperature of transponders, critically high rotation speed of a milling machine’s spindles, etc.) and condition (e.g., temperature, vibration, etc.) parameters. Possible overload circumstances and breakages, as well as infractions of standard operating rules, are reported to the supervisors.

Small and medium-sized businesses have the same prospects as large corporations:

IoT provides comparable transformational prospects for small and medium-sized businesses by driving improvements in business and industrial processes.


A medium-sized corporation with affiliates in Illinois and Texas, for example, is geographically scattered, and so faces similar issues of distributed manufacturing as a major company with multiple affiliates in the United States and Mexico. IoT enables digital transformation for SMEs by relying on cloud computing and ubiquitous, sometimes open-source software.


The Industrial Internet of Things (IIoT) assists manufacturing companies in maximising efficiency by ensuring production uptime, lowering costs, and eliminating waste.


Manufacturers may gain a better understanding of their manufacturing and supply chain processes, increase demand forecasting, reduce time to market, and improve customer experience by leveraging IoT data. 


However, given the scope and complexity of Industrial IoT efforts, successful IIoT adoption necessitates careful orchestration across all IIoT application design and execution segments.

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