Smart Manufacturing: Revolutionising Industries through Cutting edge technologies

Sustainable and Smart manufacturing techniques to enhance productivity and reduce wastage in the manufacturing sector

Industrial Revolution Through Smart Manufacturing

In these changing times when there is more importance to the environmental impact from various industries, reducing the waste generated in the manufacturing industries is a major concern as they are produced in large quantities. The deposition of industrial waste in landfills causes environmental threats. Hence to combat this issue the industries are applying various new technologies. The manufacturing sector has a vast product range, increasing the complexity, thus minimizing waste while maintaining product quality has been a challenging task for the manufacturing industries. Industries like aerospace, consumer electronics, and automotive along with other production sectors are the domains where the conventional quality control method is generally human inspection which is tedious and prone to errors (Torres et al., 2021). As it's nearly impractical to identify slight variations in the products produced in large quantities, the outcome is a significant increase in defective products and in turn increased waste generation.

Smart manufacturing using technologies like AI (Artificial intelligence) is gaining popularity among industry experts owing to its efficiency in increased quality control and reduced waste generation in the manufacturing line (Sahoo & Lo, 2022). Industries are revolutionizing all over the world using Industry 4.0, by improving manufacturing and distribution using IoT, cloud computing & analytics, and AI & machine learning (Ammar et al., 2021). The technologies utilising IIoT (Industrial Internet of Things), edge computing, AI and robots contribute to effective smart factories with increased yield, reduced human error, optimized electrical consumption and customized user demands. The main benefits of using these technologies over human supervision are effective decision-making and self-optimization for automation along with the above-mentioned advantages. IIoT and IoT offer ubiquitous connectivity between physical devices and could contribute about USD 14 trillion to the global economy by the year 2030 (Fraga-Lamas et al., 2021).

Industry 4.0

Industry 4.0 technologies are revolutionising the manufacturing sector by improving efficiency and reducing companies' emissions. Industry 4.0 includes technologies like Big data, Cloud computing, Cobots, Machine learning and the Industrial Internet of Things. Big Data is used to manage and store the large amount of data from various sensors as these data must be saved to comprehend the performance evaluation of the manufacturing process and to modify the process when necessary. Cloud computing enables data storage and security along with letting the employees access the services. This gives manufacturing facilities the flexibility to expand or descale their infrastructure according to their requirement without any infrastructure costs. Cobots are collaborative robots which aid people in completing the task while being near the operator. Machine learning is utilised in evaluating previous data and solving the present issues. The IIoT is applied to an infrastructure for communication amongst numerous sensors and also collecting data from them (Md et al., 2022). Manufacturing sector becomes more connected and intelligent with this integrated approach. Furthermore, this approach will require a lot of data to be collected and aggregated to the edge of the network which could be computed with the help of AI. Edge AI concepts are utilised to help the organisations in achieving end-to-end cybersecurity, privacy, interoperability, fast smart services and real-time valuable information along with Digital Circular Economy (DCE) concepts. This promotes transparency and supply chain visibility of the production system along with helping the stakeholders to make make right decisions by providing real-time accurate information, utilising resources effectively, improving efficiency and reducing waste (Massaro, 2023).

Future challenges

Despite Smart manufacturing techniques being a boon to the industry, it has some limitations which could affect the decision of manufacturers in adopting them. A few challenges faced by the decision makers in adopting these technologies are listed below:

● The cost of the system

● Amount of downtime of manufacturing lines

● Acceptable quality of products and potential return on investment for these systems

● Data privacy and security

● The need for skilled personnel to develop and maintain the system Manufacturers need to weigh the benefits of the system like increased yield, efficiency and reduced waste against the limitations (Naber, 2022). As the consumers today are responsible buyers and prefer sustainable products, an investment in smart manufacturing techniques should be worthy of consideration by the businesses.

Consider the potential of Smart Manufacturing for your industry

Smart manufacturing features are promising and transformative approaches in the manufacturing sector. The smart manufacturing techniques in Industry 4.0 approach include Big data, Cobots, Cloud computing, Industrial Internet of Things, Edge AI etc., which reduces wastage by automating the manufacturing process. Accidents occuring from material failure can be avoided and detection of faulty materials can be done early, reducing the waste generated from faulty products. Using these technologies material management becomes easier, reducing material failure along with reduced wastage and safer work conditions. With the integration of these technologies, supply chain transparency gets increased, and the manufacturing system becomes more optimised and profitable. Before implementing the system companies are required to assess their needs and although there is the initial cost which needs to be considered, enhanced productivity and reduced wastage can lead to more profits. Smart manufacturing is the stepping stone for the manufacturing sector towards sustainability and with the consumers being well informed towards their carbon footprint, it becomes significant to adopt these practices for a better future. For those involved in manufacturing industries, it's crucial to carefully assess the opportunities and threats presented by Smart Manufacturing technologies. By embracing innovation and sustainable practices, businesses can stay ahead of the curve and thrive in today's competitive landscape. If you're ready to begin your sustainable business transition, reach out to Carbon Mandal today and take the first step towards a brighter, more efficient future.

References

1. Naber, M. (2022) How AI inspection can reduce waste for manufacturers, Smart Industry. Available at: https://www.smartindustry.com/tools-of-transformation/artificial-intelligence/article/112 88936/how-ai-inspection-can-reduce-waste-for-manufacturers (Accessed: 25 October 2023).

2. Ammar, M. et al. (2021) ‘Improving material quality management and manufacturing organizations system through industry 4.0 technologies’, Materials Today: Proceedings, 45, pp. 5089–5096. doi:10.1016/j.matpr.2021.01.585.

3. Fraga-Lamas, P., Lopes, S.I. and Fernández-Caramés, T.M. (2021) ‘Green IOT and Edge Ai as key technological enablers for a sustainable digital transition towards a smart circular economy: An industry 5.0 use case’, Sensors, 21(17), p. 5745. doi:10.3390/s21175745.

4. Md, A.Q. et al. (2022) ‘A review on data-driven quality prediction in the production process with Machine Learning for Industry 4.0’, Processes, 10(10), p. 1966. doi:10.3390/pr10101966.

5. Sahoo, S. and Lo, C.-Y. (2022) ‘Smart manufacturing powered by recent technological advancements: A Review’, Journal of Manufacturing Systems, 64, pp. 236–250. doi:10.1016/j.jmsy.2022.06.008.

6. Massaro, A. (2023) ‘Advanced Electronic and optoelectronic sensors, applications, modelling and industry 5.0 perspectives’, Applied Sciences, 13(7), p. 4582. doi:10.3390/app13074582.

7. Torres, Y., Nadeau, S. and Landau, K. (2021) ‘Classification and quantification of human error in manufacturing: A case study in complex manual assembly’, Applied Sciences, 11(2), p. 749. doi:10.3390/app11020749.