| Author – Bharath B Bommai, Managing Director Ashwa Energy
Artificial intelligence (AI) is transforming every industry globally, spanning from healthcare, agriculture, food processing and self-driving cars to everyday use cases for humans. With all sectors adopting digital transformation, AI has gained momentum in the manufacturing industry. AI in manufacturing refers to the deployment of Machine Learning (ML) solutions and Deep Learning (DL) neural networks to automate complicated operations and uncover previously unknown patterns to optimise manufacturing processes through better data analysis and decision-making. Today, major manufacturers are utilising these novel technologies to improve efficiency, accuracy, and production across multiple processes.
The Advent of Smart Manufacturing
The “Smart Manufacturing” revolution brought about new opportunities for manufacturers to achieve their goals of higher productivity, cost optimisation and superior product quality with great success. At the core of this disruptive wave is the power of industrial AI and ML technologies. With data emerging as a significant asset in today’s digital age, the cost of obtaining and keeping it has decreased substantially. Manufacturers across sectors are using the potential of this data to revolutionise operations and raise profits, driven by AI and ML’s extraordinary capabilities. According to MarketsAndMarkets, the use of AI in the manufacturing industry is expected to reach USD 20.8 billion by 2028, owing to the introduction of highly beneficial Generative AI. By embracing these modern technologies, firms may improve production efficiency while also addressing the core causes of production losses and related expenses.
Use Cases of AI in the Manufacturing Industry
Bharath B Bommai, Managing Director Ashwa Energy
The integration of AI and ML technologies has helped the industrial sector grow significantly. Following are some of the major AI use cases in the manufacturing industry:
Supply Chain Management: The manufacturing industry relies heavily on supply chain management and AI has evolved major operations in this field. By leveraging the possibilities of AI solutions for manufacturing, businesses are reassessing their supply chain operations and witnessing significant improvements in cost-effectiveness, accuracy and efficiency. Additionally, AI facilitates the use of predictive analytics which further optimises inventory management, improves demand forecasting and expedites logistics. Not only this but ML algorithms are also used to analyse previous data, detect trends and accurately forecast demand changes. For example, an automotive parts company may utilise machine learning models to estimate demand for spare parts, allowing them to optimise inventory levels and reduce costs.
Cobots: Collaborative robots, also referred to as Cobots are vital for AI-powered production as they boost efficiency by working alongside human operators. These machines collaborate with human workers, traversing complex environments and detecting items using artificial intelligence algorithms. As per the reports by Market Data Forecast, the global cobot market is expected to reach USD 1.97 billion in 2024 and USD 11.30 billion by the year 2029. This is because the track record for cobots in manufacturing has been quite constant in terms of positive outcomes and benefits across industries.
Predictive Maintenance: As the manufacturing industry integrates AI, predictive maintenance technology is poised to become a key tool for ensuring continuous operation and optimising equipment longevity in the sector. With the capability to monitor and analyse massive volumes of data in real-time, AI systems can presently forecast possible equipment breakdowns, allowing manufacturers to address maintenance needs and prevent costly downtime. Moreover, these AI systems leverage machine learning algorithms and sensor data to discover trends and deviations that may predict future faults or breakdowns, allowing manufacturers to schedule maintenance activities at the best intervals. This not only increases production and efficiency but also lowers total maintenance expenses.
Quality Control: One of the essential elements of manufacturing, Quality control guarantees that goods satisfy the necessary standards and requirements. With technological advancements, the incorporation of Inception AI into manufacturing has transformed quality control operations. It analyses data and identifies patterns using ML algorithms, enabling real-time error discovery and monitoring. This technology assists manufacturers in detecting and eliminating product faults and even assists in predictive maintenance, reducing downtime and increasing productivity. The use of Inception AI in manufacturing not only improves quality control but also boosts productivity, making it an essential component of the contemporary industrial sector.
Assembly Line Optimisation: AI is extensively being used in assembly line optimisation for effectively increasing the efficiency, accuracy and flexibility of manufacturing processes. ML algorithms enhance efficiency, decrease downtime, and allow predictive maintenance by analysing historical performance measurements and real-time sensor data. Furthermore, AI-powered computer vision systems may detect faults or abnormalities in products to verify their quality. Intelligent automation reduces waste and enhances resource utilisation by adjusting production settings in response to altering demand. AI transforms assembly lines into data-driven, flexible environments through continuous learning and adaptation, ultimately increasing productivity, cutting costs, and maintaining high manufacturing standards.
Demand Forecasting: With AI, the manufacturing industry has become capable of accurately forecasting and fulfilling consumer demands. Manufacturers can analyse massive volumes of historical data, market trends, and client preferences in order to estimate future demand with surprising accuracy. In addition, this technology helps producers to optimise production, eliminate waste, and minimise stockouts, therefore enhancing profitability and customer happiness. By embracing demand forecasting AI, producers can remain ahead of the competition, react to changing market circumstances, and guarantee effective resource allocation, making it a must-have tool in today’s industrial scenario.
Future of AI Technologies in Manufacturing
AI in manufacturing holds a promising future following the development of several technologies. Machine learning, along with both supervised and unsupervised learning, will remain essential for factory optimisation. In process industries, deep learning is becoming increasingly important since it can help manufacturers analyse large, complicated data sets and enhance their whole production process. In order to improve process automation and inventory management, natural language processing will be used extensively to interpret human instructions for machines. By using smart cameras and AI-based machine vision, manufacturing environments can be monitored to improve worker safety and reduce injury rates. As these technologies continue to upgrade and enhance, they will increase reliability, efficiency and innovation in the manufacturing industry.
