AI in Manufacturing: How It Could Change Future Factories

AI systems can analyze defect data, identify root causes, and provide insights for process enhancement. Manufacturers can iteratively refine their processes, minimizing defects over time and enhancing overall quality. At the core of AI lies machine learning, a field that empowers machines to learn patterns and insights from data. Machine learning algorithms analyze vast datasets, identify trends, and make predictions without being explicitly programmed. This capability lends itself remarkably well to the multifaceted nature of manufacturing operations.

This technology integrates large amounts of data from sensors embedded in machinery. ML models are considered as black-box systems given their complexity and intransparency of input-output relation. This reduces the comprehensibility of the system behavior and thus also the acceptance by plant operators. Due to the lack of transparency and the stochasticity of these models, no deterministic proof of functional correctness can be achieved complicating the certification of production equipment. Given their inherent unrestricted prediction behavior, ML models are vulnerable against erroneous or manipulated data further risking the reliability of the production system because of lacking robustness and safety. In addition to high development and deployment costs, the data drifts cause high maintenance costs, which is disadvantageous compared to purely deterministic programs.

Modernizing Demand Forecasting, Supply Chain, and Predictive Maintenance

Organizations may attain sustainable production levels by optimizing processes with the use of AI-powered software. More correctly than humans, AI-powered software can anticipate the price of commodities, and it also improves with time. Explore the latest industrial maintenance best practices, trends and news from ATS and learn from industry experts and leading manufacturers. Connected cars equipped with sensors can monitor real-time information about traffic jams, road conditions and accidents to help plan better delivery routes and notify authorities in emergency situations. Manufacturing companies can be hit with significant overruns due to inefficient inventory management. Manufacturers can use AI technology to manage their order records, add/delete inventory, and make changes.

They’re built with special technology and have a camera to watch what’s happening on the floor. This helps speed up the creation of the company’s next generation of products. Smart robots can read documents, sort information, and put it in the right place automatically.

A more efficient and innovative design process (generative design)

Unlike some other industries, generative AI technologies like ChatGPT seem less likely to have an impact on manufacturing. For instance, our client, a global manufacturer of heavy construction and mining equipment, faced challenges with a decentralized supply chain, resulting in increased transportation costs and manual data resolution. To address this, we developed a data-driven logistics and supply chain management system using AI-powered Robotic Process Automation (RPA) and analytics. The RPA bots automated manual processes, resolving errors and enhancing supply chain visibility by 60%, ultimately improving operational efficiency by 30%. One notable use case of AI in manufacturing to ensure quality assurance is visual inspection. With the help of the technology, manufacturers can employ computer vision algorithms to analyze images or videos of products and components.

Many industrial robots include machine vision, which allows them to maneuver precisely in chaotic environments. They allow for automation of monotonous tasks, the elimination of human error and reallocation of labor to higher-value jobs. One flaw in an equipment component can lead to major disruptions in the entire manufacturing process.

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