How Artificial Intelligence Can Improve The Manufacturing Industry

Introduction 

Manufacturing refers to the process of producing goods from raw materials on a large scale for the purpose of sales. As an industry, manufacturing is important for the production of goods for local use as well as export. Manufacturing is impacted by various factors such as cost and availability of raw materials, availability of skilled labor as well as market factors such as demand for goods. In 2021, world manufacturing output amounted to about $16.35tn, which was an increase of 20% from 2020 when the industry suffered due to restrictions imposed as a result of the COVID-19 pandemic. Output varies greatly across the globe, with most manufacturing concentrated in countries located in East Asia (especially China) and the Pacific followed by the US while the least manufacturing output is seen in Sub-Saharan Africa. Although projections show that manufacturing might slow down along with global trade through 2023 as a result of tensions between world powers, manufacturing is expected to pick up in the mid 2020’s. 

Currently, the manufacturing industry faces several challenges to its operations. Top on the list include shortage of labor and skills necessary to maintain supply as well as sustainability of accessing raw materials and the production process. In addition, rising inflation across the globe affects companies and changing laws due to conflicts and crises hamper business practices. 

Artificial Intelligence And Manufacturing 

Currently, widespread automation of manufacturing processes has created a foundation for the introduction of more advanced technologies in improving the manufacturing process. Already AI spending in manufacturing has reached $2.9bn and is expected to approach $78.8bn by 2030 for research centered on robotics and autonomous machines has the capability to produce a more optimal production process which retains human precision and skills while increasing the speed of productions in a safe environment. . In comparison, AI has been said to have the capacity to deliver a value of up to $1.2-2tn in manufacturing. Various steps of the manufacturing process are potential areas for the integration of AI technologies:

Robotics: Robots are quickly becoming an essential part of factories all over the world. With the capacity to operate for much longer hours than humans and with less errors, manufacturing plants equipped with robots are able to increase production speeds without compromising the quality of materials produced. These machines are powered by AI technologies and are able to recreate human-like precision and accuracy while reducing the demand on the workforce.

Autonomous monitoring: Machines with ML capacity are able to learn from observation what is expected of the production process and thus detect faults or errors that reduce the quality of the product. Already car companies are employing such technologies in monitoring the production process and detection of abnormalities to ensure high levels of precision in manufacturing. In addition, technologies such as computer vision can be applied for quality assurance purposes to avoid costly production errors and unwanted delays 

Predictive maintenance: Using the IoT in combination with ML techniques, feedback from machinery and output of the production line can be analyzed to predict possible faults and propose ways to fix them before they even occur. Predictive maintenance helps to reduce system downtime, poor quality products and overall cost of maintenance of factory equipment and systems. By anticipating and avoiding potential problems, workers are able to save time and energy that would otherwise be wasted on system breakdowns.

Safety: Surveys have shown that poor use of protective equipment by workers is responsible for a high proportion of accidents in the workplace leading to loss of manpower and serious injury or death among employees. Using computer vision and other technologies, it is possible to detect whether or not a worker is well suited up and alert workers to safety precautions. This will hopefully improve the safety profile of manufacturing plants and reduce the occurrence of accidents. 

Design Optimization: The design process can be explored in various ways using several combinations of steps to achieve the same product. With its high capacity for analysis, AI can be used to test the viability of several possible processes to determine which methods will be most cost effective for a company. By doing this, companies can design factories and production lines to suit the quickest and easiest production process. In addition, virtual reality capabilities can be used to test products in advance and allow for adjustments to be made even before physical prototypes are created. This will go a long way in saving cost for companies while ensuring that high quality products are made available.

Management: AI technologies can be applied in integrating the entire manufacturing process into a cohesive format for complete and adequate monitoring of the process. By providing avenues for managing materials and simultaneously measuring output, plants can decide whether or not they’re adequately managing their resources to produce the highest possible output for maximum efficiency and profit generation. Such systems can even make forecasts using market trends to predict peak demand periods and drive higher production levels to enable adequate supply. AI can further be applied to help companies manage and process orders and payments more quickly while providing personalized options to customers. 

The applications of AI in manufacturing are virtually inexhaustible. AI holds much promise in improving the manufacturing process at the different stages of production from the design of products, to the building, storage and sales of finished materials. It is however important that human staff are included in the digital revolution of manufacturing and that systems are built to maintain high levels of precision and safety for workers while achieving the aim of producing the best quality of merchandise to customers. 


References 

https://www.macrotrends.net/countries/WLD/world/manufacturing-output

https://www.birlasoft.com/articles/17-use-cases-of-ai-in-manufacturing

https://www.v7labs.com/blog/ai-in-manufacturing

https://www.manceps.com/manufacturing

https://www.altexsoft.com/blog/ai-manufacturing/

https://dataconomy.com/2022/08/artificial-intelligence-in-manufacturing/



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