IoT in manufacturing:

8 use cases, technologies, and examples

IoT in manufacturing:
 8 use cases, technologies, and examples

September 19, 2023

The state of IoT in the manufacturing market

Nowadays, there is a gamut of innovative technologies that manufacturing companies can use to enhance operational efficiency. Artificial intelligence, 5G, various automation solutions, and cloud platforms are poised to change manufacturers’ business models for good. However, IoT lies at the core of Industry 4.0 and can generate the most value in manufacturing.

Scheme title: IoT in manufacturing market size, 2021 to 2030 (USD billion)
Data source: — IoT in manufacturing market size to reach USD 1.52 trillion by 2030

$5.5 - $12.6 trillion

will be added in value globally by IoT in manufacturing by 2030

  • McKinsey


the largest share of all IoT applications in factory settings by 2030

  • McKinsey

8 IoT use cases in manufacturing

IoT offers manufacturers numerous opportunities to innovate their operations and gain a competitive edge. Here are eight examples of how businesses in the manufacturing industry can leverage IoT technology:

On-time DepartureALERTNeedMaintenanceALERTLift DetectedALERTHighVibrationALERTRemotely manage equipment, set limits, and reduce costs with global visibility and automation toolsUse near real-time vehicle and asset management data with condition-based alerts for quality control and to reduce costs in transportation and supply chainShare data across management, field workers, and staff to improve operations and performanceReduce machine downtime with predictive maintenance alertsSensors, alerts, and notifications can help protect workersMonitor the health of roboticsGet cross-channel inventory and tracking visibilityEmploy telematics, routing, and fleet management and other tools to help save time and moneyGet near real-time data about assets, components, and operating systems, thus helping eliminate inefficiencies

Scheme title: IoT inside manufacturing, transportation, and supply chain

Data source: — The tech race and IoT. How technology can sharpen your competitive edge

Predictive maintenance

Instead of replacing machinery parts at regular intervals to minimize the risk of failure, technicians can use predictive maintenance to monitor the condition of equipment and replace parts only when necessary.

By utilizing smart sensors similar to those used in smart offices, edge computing, advanced machine learning algorithms, and computer vision in manufacturing, industrial companies can accurately predict equipment failure and make real-time adjustments to ensure that production flow is uninterrupted.

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Examples of IoT in manufacturing


Tenaris, a global manufacturing organization that produces steel pipes, has joined forces with ABB, a multinational producer of automated electrical equipment, to streamline the maintenance of more than 400 electric motors that drive rolling mills at their Italian plant.  ABB installed smart sensors on each of the plants’ motors that send performance data to a dedicated platform so that the Tenaris engineering team could monitor the condition of all motors in real time. On top of that, when certain metrics go above the established threshold, the system automatically sends notifications to the maintenance team. Ettore Martinelli, Tenaris Maintenance Engineering Director, claims that ABB’s solution does a perfect job at detecting excessive motor vibrations, which is often a sign of failure. Similarly, smart sensors have proven to be effective in detecting voltage anomalies that indicate the likelihood of a short circuit.
Main reasons for LMS software switch

Image title: Main reasons for LMS software switch

Data source: — ABB Ability™ Condition Monitoring brings predictive maintenance to an Italian factory that never sleeps

IoT solution architecture

A сommon IoT solution consists of sensors and computing devices interconnected through a dedicated network. While there is no universally accepted standard for an IoT solution architecture, in most cases, the architecture includes the following components:

CCTVKeyless lockLighting controlRobotic assemblyEnvironment dashboardIoT devicesTemperature sensorGPS/proximityMotion/speed sensorElectric actuatorSmart servo motorsHydraulic motorSensors & actuatorsAggregatesFilterdataProcessesIoT gatewayCloudExternal serversInternetWeb & mobile applicationsIndustrial panel PCsHuman machine interface (HMI)CCTVKeyless lockLighting controlRobotic assemblyEnvironment dashboardIoT devicesTemperature sensorGPS/proximityMotion/speed sensorElectric actuatorSmart servo motorsHydraulic motorSensors & actuatorsIoT gatewayAggregatesFilterdataProcessesCloudExternal serversInternetWeb & mobile appsIndustrial panel PCsHuman machine interface (HMI)


Cameras, temperature sensors, motion detectors, and other types of IoT sensors form the foundation for any successful industrial IoT solution. Depending on the application, they are responsible for detecting movements, collecting environmental data, or providing insights into equipment conditions.

Edge devices

Edge devices are computing capabilities that sit between embedded systems and cloud or premises-based servers. Edge devices can process data locally, analyze it in near real-time, and make decisions without sending it to the cloud for analysis.

Cloud solutions

Cloud solutions are used to store, process, and analyze large amounts of data collected from sensors. Importantly, the cloud plays a pivotal role in making AI-based data analytics accessible on a large scale.

Embedded systems

Embedded systems are single-purpose computing devices that can significantly enhance production efficiency by handling day-to-day tasks on their own. A machine vision system responsible for quality control is a common example of an embedded system in the manufacturing context.

Human-machine interfaces

Human-machine interfaces (HMIs) are used to communicate data between machines and humans. For instance, HMIs can be used to visualize the performance metrics of connected equipment or interact with a machine vision system during an inspection.

Best IoT cloud platforms we use

The future of IoT-based systems in the manufacturing sector is closely associated with cloud computing. Cloud-based IoT platforms are designed to capture data and provide insights that can be used to make more informed decisions. Here are some of the best IoT cloud platform solutions for manufacturers:


AWS IoT offers a broad range of services that make it simple to connect devices to the cloud and process data securely on a global scale. AWS provides an easy-to-use interface so you can quickly get your connected solution up and running.
Data services
  • AWS IoT Events for event monitoring
  • AWS IoT Analytics for data analysis
  • AWS IoT FleetWise to process vehicle data
  • AWS IoT SiteWise to process facility data
  • AWS IoT Twin Maker for building digital twins
Control services
  • AWS IoT Core for connecting IoT devices
  • AWS IoT Device Advisor for device validation
  • AWS IoT Device Defender for data security
  • AWS IoT Device Management to control IoT devices
Device services
  • AWS IoT Device SDK to connect devices to AWS
  • AWS IoT ExpressLink to maintain hardware modules
  • AWS IoT Device Tester for automated testing
  • AWS IoT Greengrass to manage edge devices

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IoT implementation challenges and best practices



Focusing on a single application



Unlike many other technology implementations, IoT systems can prove truly efficient only when applied in multiple use cases. In other words, it’s better to implement many IoT tools than pick the most potent technology and focus solely on it. Importantly, this doesn’t mean that you need to implement all possible IoT use cases at once but to take a holistic approach. 

Lack of change management



One of the most common traps that manufacturing organizations fall into is treating IoT digital transformation as a solely IT project. The implementation of the first use cases implies a rather serious working process transformation for many employees and, most importantly, a shift in their attitude to work. This is why change management should be among the top preparatory measures when embarking on the IoT transformation journey.




For the majority of manufacturing organizations, any digital transformation initiative is a sign of a new beginning. Understandably, it’s very tempting to ensure 100% readiness in terms of technology, talent, resources, etc. But in fact, it’s far more feasible and effective to approach transformation head-on, failing and learning as you go. Not to say that you don’t need to prepare at all. Instead, the preparation should center around establishing a dedicated transformation team, which utilizes proven transformation management practices.

Lack of security



The implementation of IoT solutions in the manufacturing process requires a certain level of security to protect it from external threats. This means companies must use secure authentication protocols when connecting devices, develop comprehensive access control policies, and monitor all data flows. It’s also critical to ensure that these security measures are regularly tested and updated accordingly.

The new era in manufacturing led by IoT

The new era in manufacturing led by IoT

The IoT adoption in manufacturing environments is revolutionizing the industry. It enables factories to optimize production processes, improve asset utilization, increase uptime, and anticipate disruptions.  With the help of Itransition’s expertise in IoT development and data science, factories can embrace the Industry 4.0 revolution with confidence. Our team will provide you with a tailored solution to make sure that your factory is ready for an era of unprecedented transformation. Contact us and start your journey toward digital transformation today.

The new era in manufacturing led by IoT


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