Manufacturing companies must reduce their environmental impact to align themselves with the green economy goals for sustainable development. This involves a multidisciplinary approach using artificial intelligence (AI) and Internet of Things (IoT) from design to deployment, continuously improving and innovating. In the journal Bulletin of the Transilvania University of Brasov. Series V: Economic Sciences, researchers explored how “Industry 4.0” would bring a transformative impact, thus gaining a competitive edge and remaining adaptive and current in the global market.
Study: Revolutionizing Manufacturing: Harnessing AI and IoT for Smart Configurations. Image Credit: Wright Studio / Shutterstock
Background
The manufacturing sector needs to be continuously innovative to sustain the fourth industrial revolution and smoothly transition to the fifth industrial revolution. They would need to develop smart solutions to face challenges pertaining to energy efficiency, turnaround times, data distribution over networks, and real-time security of the digital distribution system.
In order to face the challenges detailed above, manufacturing companies would need to undergo a digital transformation and migrate from a traditional manufacturing factory to a smart factory, which encompasses basic components such as IoT, AI, and digital twins.
Smart manufacturing factory
The first step in the transformation to a smart factory would be to replace the traditional technologies used in manufacturing factories with digital technologies. Replacing with digital technologies necessitates the use of a few intelligent components like sensors, IoT devices, 5G networks, simulation software, robots, and digital twins.
In manufacturing factories for process automation, 5G networks play a vital role in achieving low latency, high reliability, and blazing speeds. They ensure communication with physical factory locations and their infrastructure integrating that with the cloud.
An IoT device is used to monitor and check the equipment’s health. The device comprises a processing unit, a sensing element, and a communication protocol. Data collected by the sensing element reaches the processing unit, where the machine learning (ML) algorithms are run to analyze and deduce insights from data and take corrective action.
The digital twin is a virtual model that clones a physical object. The remarkable feature of the digital twin is to establish a connection between the physical object and virtual model to provide cognitive feedback and facilitate real-time measurements, analysis, corrections, feedback, and multiple process simulations. Cyber-physical manufacturing systems with computers in loop increase operational efficiency.
The Bosch example
The researchers of the present study proposed a model of the smart manufacturing factory implemented in Bosch. The critical details of the proposed model are as follows:
Bosch connects its entire logistics and production chain using the I4.0 protocol. 5G communication technology is used to fix problems in their production lines before they occur and network collision detectors are installed as a safety measure.
They use big data software connected to the cloud to connect all production and logistics and real-time data is accessible. The data are then used in energy efficiency calculations of the machines. The data thus obtained is also used and run through advanced ML models to generate accurate predictions.
Bosch also uses data mining, enabled by artificial intelligence and unique data visualizations, to extract high-quality insights. In addition, several sensors are employed in the assembly process, where every step is monitored by cameras from which data is continuously transmitted. Data messages are transmitted at about 1 million every 24 hours to ensure that no defective components leave their production lines.
Moreover, robots assist in moving packages and equipment from one physical location to another. The suppliers, consumers, and partners are all connected to their cloud, customers’ questions are addressed quickly, and interoperability is achieved with all the stakeholders in the system.
According to the researchers, this two-way communication brings about many benefits, including automatic service requests raised for devices, data updated and backed up regularly, remote configuration of the systems, automated error notification, disaster management and recovery, and machinery health monitoring and diagnostics.
Conclusion
The advancement in information technology led to a smart configuration of the manufacturing industry based on AI, IoT, and big data, which allow flexibility, independent decision-making, automatic control, and increased efficiency and productivity. In Industry 4.0, the real and digital worlds are combined, which allows process implementation where people and robots work together.
Smart manufacturing enables manufacturers to remain agile and tweak their business models as needed. This technology-based approach helps identify opportunities to use data analysis to improve performance and automate operations.
The Bosch example can be useful for other companies based on innovation, creativity, digitization, and flexibility to improve their competitive advantage. Bosch applies innovation to promote performance and supports open-source standards to increase activity values. According to the authors, it is also a win for the entire manufacturing industry.
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