In this piece we are looking at Industry 4.0 or the transition to digital for businesses and looking at how your business can become more efficient in the way it works, and how the data that can be produced can assist this. It is effectively a modern day “Industrial revolution” Like when we went from steam to electricity power way back when.
IoT has enabled significant advancements in various sectors. One notable example is the use of IoT devices in predictive maintenance. Large blue chip companies have implemented IoT sensors in their equipment to collect real-time data allowing them to detect potential failures before they occur, minimise downtime and optimise machine maintenance schedules.
Applications of IoT in smart factories have been growing and Companies have utilised IoT technologies to create interconnected systems that enhance automation, production efficiency, and quality control. By connecting machines, sensors, and software, they enable real-time monitoring, data analysis, and remote control, leading to improved productivity and reduced costs.
In agriculture, IoT devices are deployed for precision farming. Companies such as agricultural vehicle manufactures have developed IoT-enabled solutions that gather data on soil moisture, temperature, and crop growth, allowing farmers to get ahead of the curve on specifics like irrigation, fertilisation, and pest control. This has improved resource utilisation, enhances crop yield, and promotes sustainable practices.
These examples demonstrate the successful deployment and management of IoT devices in Industry 4.0, enabling data-driven decision-making, operational optimisation, and transformative changes in various sectors.
As we delve deeper into the how the use of IoT is changing the landscape of which we work and conduct or business, the introduction of AI and Machine learning on machines through the use of IoT devices has. These technologies bring automation, predictive analytics, and intelligent decision-making capabilities to IoT device management, enabling efficient operations and improved productivity. Here we can look at some key ways AI and machine learning are integrated into IoT device management.
- Predictive Maintenance: AI and machine learning algorithms can analyse large volumes of data collected from IoT devices, including sensor data, historical maintenance records, and other relevant information. By identifying patterns and anomalies, these technologies can predict equipment failures and schedule proactive maintenance before breakdowns occur. This approach minimises unplanned downtime, optimises maintenance schedules, and reduces costs.
- Anomaly Detection: AI-powered anomaly detection techniques can continuously monitor the data generated by IoT devices in manufacturing environments. Machine learning algorithms can learn normal patterns of device behaviour and identify deviations that indicate potential issues or abnormalities. This proactive monitoring helps detect faults, malfunctions, or security breaches in real-time, allowing interventions and preventive actions.
- Process Optimisation: By combining AI and machine learning with IoT device management, manufacturers can optimise their production processes. Real-time data from IoT devices, such as sensors on assembly lines or robotic equipment, can be analysed to identify bottlenecks, inefficiencies, or quality issues. AI algorithms can then recommend process improvements or automatically adjust device settings to enhance productivity and product quality.
- Energy Management: IoT devices play a crucial role in monitoring energy consumption in manufacturing facilities. AI and machine learning can leverage this data to optimise energy usage by identifying areas of inefficiency and suggesting energy-saving measures. This integration helps reduce energy costs, minimise environmental impact, and improve sustainability in manufacturing processes.
How do you know you are getting the best value with IoT devices
To select the right IoT devices and platforms, companies should consider factors such as scalability, interoperability, security, data analytics capabilities, vendor reputation, and long-term support. It would always be advisable to Conduct thorough research of the device capability to see whether it is fit for the purpose you want. With so many devices offering similar technologies, testing kit by doing proof of concept testing would be the best way to find the best device to fit your project specification. Also consulting with experts in the field, getting their viewpoint on the solution to meet your goals.
Devices are becoming smarter and more effective, with the two Low powered wide area networks using gateways of LoRA WAN and Sigfox. Now with the roll out of NBIoT and Cat1M which are using IoT sim cards connected to the cellular infrastructure in the UK. The main telecoms operators are rolling out cat 1M and NBIoT to run the small packets of data offerings of IoT devices. The benefit of using the latter connectivity is that we can install devices without the need for gateways and heavy IT infrastructure while still enabling security.
We are all part of this change and how we adapt is down to the individual companies that take steps to optimise their ROI. With environmental impact and the strategies towards net zero now becoming ever prevalent in tenders and contract bids, now is the time to implement IoT devices into your workplace management systems.
Stuart Smith is a Technology Specialist at Iris-IOT, where he works to develop technology to make businesses more efficient and reducing their carbon footprint.
