How AI is Used in Manufacturing: Benefits and Use Cases

ai in factories

The benefits are improved effectiveness, predictability, and efficiency of manufacturing operations and yields. Manufacturers are using AI (Artificial Intelligence) to provide high-quality services and products. Explainable Artificial Intelligence in manufacturing improves efficiency, workplace safety, and customer satisfaction by automating tasks.

Computer vision technology can detect holes, abrasions, scratches, undesirable shapes, and so on. With its help, the factories can maximize the product quality and its lifespan, improving customer experience and reducing waste. Today, AI technology has gained rapid adoption in the discrete industry compared to the process industry. The manufacturing sub-sectors such as automotive/OEMs, heavy machinery, semiconductors & electronics are the dominant end-users that leverage the AI technology. The process industries such as energy and power, food and beverages, and pharmaceuticals are the major end users utilizing AI technology in daily operations. AI technology is transforming the energy grid by offering new ways to monitor and optimize its performance.

The Key Applications of Artificial Intelligence in Manufacturing

Predictive maintenance is a proactive approach to equipment upkeep that uses data analytics to gather machine data and interpret the data’s “story” through machine learning. AI-powered quality control doesn’t stop at identifying defects; it fosters a culture of continuous improvement. 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. The concept of predictive maintenance isn’t new, but AI injects a new level of accuracy and sophistication. Through continuous analysis of sensor data, AI algorithms can predict equipment failures before they occur, enabling timely maintenance and reducing costly downtime.

He advised enterprises on their technology decisions at McKinsey & Company and Altman Solon for more than a decade. He led technology strategy and procurement of a telco while reporting to the CEO. He has also led commercial growth of deep tech company Hypatos that reached a 7 digit annual recurring revenue and a 9 digit valuation from 0 within 2 years. Cem’s work in Hypatos was covered by leading technology publications like TechCrunch and Business Insider.

How Appinventiv is Empowering Manufacturing with Custom AI/ML Solutions

Artificial intelligence in the manufacturing industry typically falls into four broad categories, depending on the technology’s rigidity and requirement for human involvement. These technological advances relegated many tedious, rote, and unsafe tasks to machines instead of people. While they eliminated some jobs, however, they also created new ones—many of which demanded more technologically astute operators. Amid the excitement of AI’s potential, it’s crucial to underscore the importance of balance. A harmonious integration requires a holistic approach that marries AI’s capabilities with human ingenuity. This balance ensures that the promise of AI is realized without compromising the integrity of human values and ethics.

  • AI-powered systems can analyze energy usage patterns, identify areas of inefficiency, and recommend energy-saving measures.
  • Manufacturers generate more data than any other business sector, but they also waste the most data.
  • Silicon wafers are a type of semiconductor used in the production of microchips that go into the electronic gadgets we use daily such as cell phones, computers, televisions, and more.
  • AI’s prowess lies in its ability to sift through this data and extract meaningful insights.

False justification can lose a customer’s trust and reduce revenue by making wrong decisions and actions. We should use the correct tool and the correct way to represent the explanation of the System. The System provides different explanations for different user groups, for the end-user and the developers, according to their prior knowledge and experience. The explanation that the System provides should be understandable to the targeted recipient. For example, the System explains the model’s inner workings and algorithm to debug the System easily.

The manufacturers may not need as many employees on the production line as they would in the past – however, as they’re moving towards a data-driven business model, they will search for more analysts and data scientists. The manufacturers can use computer vision to detect potential issues in the facility. Once the algorithms identify an anomaly, they send an alert via text message or app to the authorized representatives who can investigate the issue. For instance, they can handle a variety of order types from various sales channels, issue purchase requests automatically, and improve the transparency of order and inventory management using inventory tracking sensors. This technology aids in streamlining processes and improving the effectiveness of the order management procedure. Manufacturers must be adaptable to shifts in the market, demand, customer expectations, and manufacturing techniques to manage orders effectively.

ai in factories

Computer vision is also replacing the spreadsheets and clipboards that have been so intrinsic to inventory counts over the years with a platform that now displays automatically the information required in real time. It matters because manufacturers—as part of the industry 4.0 evolution—are in general embracing automated product assembly processes. This allows it to make more accurate predictions on the future quality of a material or product, thus allowing your company to reach an error-free production. Using V7’s software, you can train object detection, instance segmentation and image classification models to spot defects and anomalies. Moreover, because computer vision systems are trained on thousands of datasets, they can override AOI shortcomings, including image quality issues and complicated surface textures to arrive at a precise assessment. With over two decades in the industry and as Infor CloudSuite consulting experts, Datix is the ERP consultant of choice for manufacturers and distributors.

Virtual and Augmented Reality

Then comes the cold phase where already molten glass is channeled to the special metal alloy plates called bushings. To become fibered, glass has to pass through holes in bushings (with an average diameter of about micrometers). The break of a fiber occurred when the glass was running through these holes and the pulling force was applied to it. The company set up a camera that uninterruptedly monitored fibers as they left a bushing.

Some forecasts estimate that the opportunity in artificial intelligence will be worth trillions of dollars. If you’re looking to invest in AI manufacturers, you can consider some of the stocks above or take a look at other AI stocks, machine learning stocks, or AI ETFs. Cobots or collaborative robots are also commonly used in warehouses and manufacturing plants to lift heavy car parts or handle assembly.

Applications of AI for Predictive Maintenance

Quality control is one area where AI systems consistently outperform manual testing processes done by humans. AI machines are also able to optimize production and figure out the root cause of a problem when there is an error. Due to the shift toward personalization in consumer demand, manufacturers can leverage digital twins to design various permutations of the product. This allows customers to purchase the product based on performance metrics rather than its design. Though there’s been a lot of talk about AI taking over humans’ jobs, widespread use of AI will create the need for new roles and operating models.

ai in factories

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