AI has a wide range of uses in the manufacturing sector since industrial IoT and smart factories produce a lot of data every day.
Machine learning (ML) and deep learning neural networks are used in manufacturing to optimise production processes through better data analysis and decision-making.
Predictive maintenance is a typical AI use case in the manufacturing industry. Businesses may more accurately predict and avoid machine failure by using AI to analyse production data. As a result, costly downtime in industrial operations is reduced. Other possible applications and advantages of AI in manufacturing include better demand forecasting and decreased raw material waste. Since industrial manufacturing environments already necessitate close cooperation between humans and robots, AI and manufacturing make sense together.
The idea of "Industry 4.0," the drive towards more automation in production environments, and the huge data generation and transmission in those environments all depend on AI. To ensure that businesses can extract value from the massive amounts of data generated by manufacturing machines, AI and ML are crucial tools. Applying AI to this data to optimise the manufacturing process can result in cost reductions, safety enhancements, supply-chain efficiency, and a wide range of other advantages.
Manufacturers may monitor their facilities in real-time using crucial Fourth Industrial Revolution (4IR) technologies including machine learning, automation, advanced and predictive analytics, and IoT (Internet of Things). Examples of AI in Manufacturing: Transforming Industries. AI's impact on manufacturing is revolutionary. The result has been a 20% reduction in forecasting errors, 30% fewer sales are lost, and 50% less effort for demand forecasters.
Why Artificial Intelligence is Important for the Future of Manufacturing
Every manufacturer searches for novel ways to increase profits, decrease risks, and boost overall production effectiveness. This is necessary to secure their survival and a prosperous, sustainable future. 4IR technologies, particularly those that are AI- and ML-based, hold the key.
Massive amounts of factory-floor data may be processed and interpreted by AI technologies in order to find trends, analyse and forecast customer behaviour, uncover anomalies in production processes in real-time, and more. These solutions assist manufacturers in getting complete visibility of all manufacturing operations across all locations and regions.
Machine learning algorithms enable AI-powered systems to continuously learn, adapt, and advance. For manufacturers to succeed in the wake of pandemic-induced fast digitization, these competences are essential.
What's Causing the Need for AI Adoption?
- extremely volatile revenue
- Need to continually identify ways to reduce costs
- quick turnaround times
- more oversight and inspections
- Learning and flexibility in the workplace
- Demands for the supply chain and manufacturing capacity
- increased demand for customised or small-batch products
The Future of Manufacturing can find flaws at every stage of production such as Use preventative maintenance to save downtime, across the supply chain, react in real time to changes in demand, Verify the precision with which complex products like microchips were manufactured, Lower the cost of single- or small-run products to enable more customization, and By changing, you can raise employee satisfaction.
Accessibility to historical datasets is the key factor accelerating AI advancement. Healthcare organisations and governmental organisations create unstructured data that is accessible to the research domain since data storage and recovery have gotten more affordable. Rich datasets are becoming available to researchers, ranging from historical trends in rainfall to medical imaging. Information scientists and academics are being encouraged to develop more quickly by the next-generation computing architectures, which have access to large datasets.
Additionally, advancements in ANN (Artificial Neural Networks) and profound learning have accelerated the use of AI in a number of sectors, including aerospace, healthcare, manufacturing, and automotive. ANN assists in offering modified solutions by identifying similar patterns.
Tech firms like Google Maps have been implementing ANN to enhance their routes and address customer input. ANN is used to develop precise and accurate versions in place of traditional machine learning algorithms. Digital image processing techniques, for instance, are the result of recent developments in computer vision technology such as GAN (Generative Adversarial Networks) and SSD (Single Shot MultiBox Detector). For instance, by using these approaches, low-light or low-resolution photos and films can be improved to HD quality. The basis for digital image processing in security and surveillance, healthcare, and transportation, among other industries, has been created by ongoing computer vision research. It is believed that these new machine learning techniques will change how AI versions are developed and used.
In order to get a competitive advantage in the market, vendors are concentrating on growing their clientele. For instance, Advanced Micro Devices established a strategic partnership with video game producer Oxide Interactive LLC in April 2020 to create graphics technology for the cloud gaming industry. In order to meet the real-time requirements of cloud-based gaming, both businesses have planned to develop a set of tools and approaches.
Due to the aforementioned factors, Market Research Future projects that the artificial intelligence (AI) in manufacturing market, which was valued at USD 2.45 billion in 2022, will grow by 47.1% CAGR to USD 53.69 billion by 2030.
