The manufacturing industry today is at the latest stage of its evolution, known as Industry 4.0. It essentially refers to using automation and big data and leveraging coming-age technologies such as cloud computing, IoT (Internet of things), and AI (artificial intelligence).
AI in the manufacturing industry is largely a part of this rapidly evolving trend towards fully automated production. AI systems offer multiple solutions for the manufacturing industry. They can transform the way companies run their production lines, enabling better productivity by providing real-time insights, enhancing human capabilities, and facilitating product and design innovation.
In this blog, we explore 5 main ways manufacturing companies can leverage AI for better efficiency.
1. Accurate Demand Forecasting
One way to improve production efficiency is by accurate forecasting and demand prediction for manufacturing companies. AI-powered systems can be an excellent way to do this, as they can test various models and possible outcomes.
Apart from this, automatic logistics companies can
Make use of machine learning algorithms to leverage data, discover meaningful patterns and get real-time insights.
Use these real-time insights to predict demand accurately and determine which products to prioritize accordingly.
2. Predictive Maintenance
One of the major expenses in the manufacturing industry is the ongoing maintenance of production line machinery and equipment. Studies also suggest that unplanned downtime can cost manufacturers a whopping $50 billion annually, and 42 percent of this unplanned downtime is caused by asset failure.
This makes predictive maintenance a must-have solution for manufacturers across the board. It allows them to easily predict the next failure of a part, machine, or entire system.
Predictive maintenance uses advanced AI algorithms in the form of ML (machine learning) and artificial neural networks to systematically formulate predictions regarding asset malfunction.
3. Hyper-personalized Manufacturing
Rapid advances in AI and automation offer excellent solutions for the manufacturing industry to focus on personalization and making products and services relevant to individual consumers.
Various transformative technologies combined with advanced automation allow manufacturers to enhance their existing operations greatly. It enables the manufacturing industry to offer flexible manufacturing flows, rapid response systems, realize oversights, and direct connectivity of smart factories with its customers/suppliers, which is essential to realize hyper-personalized manufacturing.
With such advanced digital technologies, automatic logistics companies can become more agile and responsive by integrating their manufacturing operations into business systems for efficiently managing supply chains.
4. Automated Material Procurement
Material procurement professionals in the manufacturing industry offer a crucial service of purchasing the goods for their organizations at the right price and the right time. One of the
reasons that make procurement difficult is the quality of intelligence-driven by traditional analytics.
Procurement, however, needs much greater visibility into its data, and AI offers exactly that. AI in manufacturing can auto-discover odd patterns in various purchase transactions using algorithms and then highlight them to humans. It can also observe and learn which patterns are accepted as worth monitoring through feedback loops later.
Further, AI systems can use this knowledge to identify and predict anomalies in live transactions, thus allowing the companies to take timely action and make procurement function more cognitive.
5. Quality Assurance
Quality assurance refers to the process of maintaining a desired level of quality in a service or product. Since assembly lines in manufacturing are data-driven and autonomous networks, they’re based on a set of fixed parameters and algorithms that offer guidelines to come up with the best possible end-products.
AI systems are equipped to detect the differences from the usual outputs using robust machine vision technology since most defects are visible. If the end-product is of lower quality, AI systems can trigger an alert so that manufacturing companies can make adjustments accordingly.
Technological advancements such as machine learning, cloud computing, and big data have some significant implications for the way manufacturing companies functions.
AI in manufacturing is the next big step in this evolution and will play a critical role in helping to achieve better productivity and visibility across manufacturing operations.
However, to successfully navigate the change, manufacturers need to understand which AI solution best suits their own unique sets of challenges and pick up the right manufacturing AI technology accordingly.
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