Posted on May 21, 2024 by JayAr Vizmanos

AI in Manufacturing: 5 Use Cases

By utilizing predictive maintenance, it is now possible to make all these goals. In any case, the future of all businesses will soon be dependent on computer vision systems that are AI. Thus, because computer vision systems are trained on so many datasets, they can provide images and assessment with defects such as poor image quality and textured surfaces. Internet of Things is a term used to describe a dynamic global network of connected physical objects embedded with software, sensors, or other technologies exchanging data with other devices. The basic elements of IoT include a central control hardware, multiple connected devices, data cloud, and user interface.

Delivery management currently dominates AI adoption, ensuring secure and efficient goods transportation. AI streamlines warehouse operations and stacking through coordinated storage and robotic systems. Furthermore, by analyzing sales data, consumer records, market trends, and social media, AI enables precise revenue estimation and product creation. The benefits they’ve found from automation include a reduction in operational costs by up to 40%; an increase in the manufacturer’s control over processes; improved employee performance; and significantly lower downtime. A. AI enhances product quality and reduces defects in manufacturing through data analysis, anomaly detection, and predictive maintenance, ensuring consistent standards and minimizing waste.

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Data was gathered from sensors installed on prototypes in testing, as well as from cars in use. A Big Data analysis detected weak points and errors in prototypes and cars that were already sold. Engineers had an opportunity to eliminate vulnerabilities in prototypes before mass production, which lowered the number of future recalls. By doing this, BMW saved lives, built brand value, and reduced warranty costs. A large sugar manufacturer suffered from high humidity levels and poor quality of raw materials, which influenced the taste of sugar. A Big Data solution quickly improved the quality of the product and created a unified sugar standard despite the external factors.

• As seen on Google Trends graph below, the panic due to lockdowns may have forced manufacturers to shift their focus to artificial intelligence.
• The global artificial intelligence in manufacturing market size is estimated to be valued USD 3.2 billion in 2023 and is anticipated to reach USD 20.8 billion by 2028, at a CAGR of 45.6% during the forecast period.
• GM will sell UVeye’s technology to its dealer network to update its vehicle inspection systems.
• In this landscape of interconnectedness, efficiency is paramount, and the role of Artificial Intelligence (AI) emerges as a beacon of optimization and innovation.
• Artificial intelligence (AI)-driven automation reduces cycle times, eliminates human error, and optimizes production procedures because it can adapt, learn, and make choices in real-time.

Manufacturing robots or AI-based technologies can help manufacturers manage their orders more efficiently in several ways. It is true that setting up AI in industrial businesses requires a huge capital investment but the ROI (return on investment) you can expect from AI is significantly higher. Once intelligent machines start to deal with day-to-day operations, businesses will be able to save lots of functional costs. AI for manufacturing can boost efficiency and streamline operations by ensuring that all aspects of the production process work together at optimal capacity. While entrusting your entire business to AI might not be the preferred approach for everyone, there are alternative options available.

Impact of AI on the Manufacturing Sector

Due to their transparent inner-working model process becomes easily interpretable.Explainability is essential in the manufacturing industry. Ensuring that AI’s impact is equitable requires deliberate actions to increase transparency and enforce fairness across manufacturing processes. In the past decade, we’ve witnessed nothing short of an AI revolution in the industrial sector. This revolution is only predicted to accelerate in the coming years, driven by emerging innovations like the metaverse, generative AI, and advanced robotics.

• As AI takes up the manual jobs done by human employees, it lifts the weight of time-consuming, tiring, and repetitive work from manual workers.
• The factory’s combination of AI and IIoT can significantly improve precision and output.
• With smart factory platforms like L2L, your workforce can reap the benefits of more streamlined, less frustrating processes, while you can see increased productivity, efficiency, and profits in months — not years.
• However, only six percent of them can claim that their operations are already fully digitized.
• Only a few companies have used AI in products and services, but investment in AI is growing rapidly.

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