13 AI in Manufacturing Examples to Know

This technology is widely used in security systems, access control, and personal device authentication, providing a convenient and secure way to confirm identity. AI is integrated into various lifestyle applications, from personal assistants like Siri and Alexa to smart home devices. These technologies simplify daily tasks, offer entertainment options, manage schedules, and even control home appliances, making life more convenient and efficient. Platforms like Simplilearn use AI algorithms to offer course recommendations and provide personalized feedback to students, enhancing their learning experience and outcomes.

By tapping into the potential of big data, IoT, AI, and ML in the automotive industry, artificial intelligence has completely transformed how vehicles are designed, manufactured, and driven. From autonomous vehicles to advanced safety systems, the advantages of AI in the automotive industry are enormous. Major companies including GE, Siemens, Intel, Funac, Kuka, Bosch, NVIDIA and Microsoft are all making significant investments in machine learning-powered approaches to improve all aspects of manufacturing. The technology is being used to bring down labor costs, reduce product defects, shorten unplanned downtimes, improve transition times, and increase production speed.

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Two months later, Roche and Owkin, a machine learning platform for medical research, partnered to speed up drug discovery, development, and clinical trials. The consortium aims to break down the divide between machine learning research at MIT and drug discovery research by bringing researchers and industry together to identify and address the most significant problems. To get a better sense of the future of AI in the sector,, PharmaNewsIntelligence dives into current AI use cases, the best uses for the technology, and the future of AI and machine learning.

The company is also examining how natural language processing models can analyze textual data from various sources to predict demand fluctuations and optimize inventory levels. Traditional quality control methods rely on human inspectors, which is time-consuming and prone to errors due to fatigue and subjective judgment. AI-driven systems use machine learning algorithms and computer vision to analyze large amounts of data to detect small defects that might escape human observation. You can foun additiona information about ai customer service and artificial intelligence and NLP. AI also ensures compliance with regulatory standards, minimizes safety hazards, and enhances brand reputation by consistently delivering high-quality products.

Many AI applications used in retail fall under the category of machine learning since making predictions is a key part of how retailers use technology. A maintenance companion, which helps shop floor personnel with maintenance tasks by digitizing paper instruction manuals and using AI to provide step-by-step, real-time instructions based on the problem at hand. Several firms use AI and machine learning to analyze images on social media, taking note of prints, shapes and colors to help their manufacturer clients figure out what’s going to sail and what’s going to sink. These companies also use AI to help brands figure out pricing strategies and steer clear of trends that are on their way out. Additionally, AI mitigates data breach risks by improving data security, reducing human bias in assessments, and enabling personalized learning, which helps create a more equitable and efficient education system.

By elevating their overall efficiency through data, the business necessity of AI at 3B was clear. These manufacturers implement technologies like AI across operations, using the technology to offer multilevel customer interactions and tailored solutions. Digital transformation is not treated as a siloed issue or an emerging project but a business imperative, to remain competitive, productive and efficient as economic demand shifts from products to software.

At the end of 2016 it also integrated IBM’s Watson Analytics into the tools offered by their service. However, as AI in gaming gets more integrated, discussions around AI ethics, data privacy, and businesses can become critical, requiring organizations to implement responsible AI. No wonder, in the future, governments may impose stern regulations to use explainable AI for gaming. Over the years, artificial intelligence in video games has emerged as a transformative force, constantly pushing the boundaries of what is possible in the virtual world and reshaping the way we develop, experience, and enjoy games. Therefore, to deal with such challenges, game developers should ensure that the game characters do not promote offensive content or harmful actions. And if it is the demand of the game, it must display a warning message or age limit consideration to prevent the implementation of such content in real life.

Quality Assurance

Predictive analytics leverages historical and real-time data to forecast future demand and optimize supply chain operations. By accurately predicting consumer demand, companies can maintain optimal inventory levels, reducing both overstock and stockouts. This technology also enhances supply chain efficiency by anticipating potential disruptions and adjusting logistics accordingly. AI-powered quality control ensures each meal meets stringent standards, while predictive maintenance keeps the machinery running smoothly. These advancements not only enhance efficiency but also reduce food waste and improve overall product quality. The food industry has undergone a significant transformation in recent years due to the widespread adoption of AI and robotics.

According to recent reports, it’s expected that about 33 million vehicles with self-driving capabilities will be on the roads by 2040. Machine learning has had fruitful applications in finance well before the advent of mobile banking apps, proficient chatbots, or search engines. Given the high volume, accurate historical records, and quantitative nature of the finance world, few industries are better suited for artificial intelligence. There are more uses cases of machine learning in finance than ever before, a trend perpetuated by more accessible computing power and more accessible machine learning tools (such as Google’s Tensorflow). While GE and Siemens are heavily focused on applying AI to create a holistic manufacturing process, other companies that specialize in industrial robotics are focusing on making robots smarter. The German conglomerate Siemens has been using neural networks to monitor its steel plants and improve efficiencies for decades.

We spoke to Caroline Gorski, Group Director of R2 Data Labs at Rolls Royce, about these data points on Emerj’s AI in Business podcast. If a car manufacturer is seeing warped steering wheels at the end of their ChatGPT assembly line, one of their machines might be overheating. Whatever the diagnosis, they’ll be making determinations based on censor, streaming, and particularly visual data to help answer that question.

These AI systems can generate several versions of an email, customizing product recommendations or promotional offers for different audiences. GenAI goes beyond traditional static analysis tools in bug detection, doing more than just catching syntax errors—it also identifies potential vulnerabilities and logic flows before they escalate into bigger problems. Software development teams can use generative AI coding solutions to scan their codebase for security weaknesses that could compromise confidential data. These AI tools flag risky areas and suggest ways for fixing them, delivering a proactive approach to debugging and preventing costly errors.

From design and manufacturing to sales and maintenance, the applications of artificial intelligence can be noticed throughout the life cycle of a vehicle. Long-term, the total digital integration and the advanced automation of the entire design and production process could open up some interesting possibilities. Customization is rare and expensive while high-volume, mass produced goods are the dominant model in manufacturing, since currently the cost of redesigning a factory line for new products is often excessive.

Research studies by Capgemini show that there is an increasing trend of AI uses in the manufacturing sector globally, where nearly 29% of use cases are observed in maintenance and 27% in quality. Within smart manufacturing, AI-powered digital twins have emerged as a critical technology. As a digital counterpart of a real-world system, digital twins provide a digital representation using data from sensors and Internet of Things (IoT) devices to mimic a physical object or system in real time. Predictive maintenance analyzes data from connected equipment and production equipment to determine when maintenance is needed. Using predictive maintenance technology helps businesses lower maintenance costs and avoid unexpected production downtime. Appinventiv, as a leading provider of education app development services, carries deep insights into the education sector.

GenAI healthcare tools reduce the time clinicians spend on paperwork by pre-filling documentation and suggesting relevant updates based on patient data. With GenAI, marketing teams can quickly write blog posts, social media updates, and product descriptions in bulk. These tools can also translate content into multiple languages, ensuring message consistency across different markets. Beyond text, GenAI can also create visuals, such as vivid images or infographics for ads. These solutions suggest code snippets in real-time, provide smart autocompletions, and even refactor code to make it more efficient. GenAI is beneficial in handling repetitive tasks, like setting up standard functions or offering ready-to-use code blocks.

Many manufacturers are eager to implement AI quickly to take advantage of potential benefits and improve the organization’s competitive advantage. Unfortunately, doing too much too soon can result in a poor implementation that doesn’t deliver ideal results. Some manufacturers might find integrating AI into existing operations to be a complex process.

Product Development

Mobile robots that possess intelligent machine vision can see in 3D, pick-and-place complex components from bins, and inspect each other’s work. Vision-guided collaborative robots, or Cobots, safely operate near human counterparts, assuming repetitive assembly tasks, heavy material handling, and other dull, dirty, and dangerous jobs. “Organizations can benefit using AI for the automation of repetitive tasks, which reduces manual efforts and increases accuracy,” said Moe Asgharnia, CIO at accounting and consulting firm BPM.

Hotels and airlines use similar technology to adjust their prices according to demand and better predict customer needs, such as changes in flight plans. Additionally, airlines use AI for predictive maintenance, reducing the risk of mechanical problems. Hopper, for example, uses machine learning algorithms to predict where airline prices are headed so users know when to buy.

Although this application of AI is potentially transformative, Earley warned that the technology isn’t reliable enough to use without human oversight or review. AI systems, such as ChatGPT, don’t always have all the data sets needed to reach accurate and complete conclusions, he explained, and they often make assumptions that aren’t correct. A March 2024 pulse poll of 250 technology leaders by professional services firm EY found that 82% of tech business leaders plan to increase their AI investment in the next year. Implementing AI and ML requires specific knowledge, and manufacturing companies will need to invest in data scientists, analysts and other algorithm and automation experts. However, the rapid growth of AI across industries means it can be difficult to find people with the right expertise to fill these roles. AI assists in promptly detecting oil spills and hydrocarbon leaks by analyzing sensor data and satellite imagery.

AI in the automotive industry encloses a broad range of technologies, such as computer vision, machine learning, NPL, speech analytics and robotics. These technologies help develop self-driving cars, advanced driver assistance systems, personal assistance, etc. Additionally, AI helps optimize production processes, reduce costs, improve supply chain management, and make the automotive industry more efficient.

Automates Administrative Tasks

Derek Driggs, an ML researcher at the University of Cambridge, together with his colleagues, published a paper in Nature Machine Intelligence that explored the use of deep learning models for diagnosing the virus. For example, Driggs’ group found that their own model was flawed because it was trained on a data set that included scans of patients that were lying down while scanned, and patients ChatGPT App that were standing up. The patients who were lying down were much more likely to be seriously ill, so the algorithm learned to identify COVID risk based on the position of the person in the scan. Advances made in 2023 by large language models (LLMs) have stoked widespread interest in the transformative potential of gen AI across nearly every industry and corner of the business.

• This approach lacks strategic consideration, the understanding that all technological implementation should be designed around serving the businesses specific needs.
• Access to high-quality training data is a major bottleneck for AI research and a competitive advantage for bigger firms that they’re eager to maintain, says Warso.
• The company builds a variety of autonomous vehicles designed to meet the needs of drivers, including individuals, rideshare drivers and large trucking companies.
• This proactive approach reduces unplanned downtime and extends critical machinery’s lifespan, leading to significant cost savings.
• These could encompass anything from personalized marketing campaigns, email blast calendars, and guest sentiment analysis to trends analysis from analytics.
• A well-known autonomous driving tech company, Waymo uses AI-powered solutions to enable self-driving capabilities in its delivery vans, taxis, and tractor-trailers.

AI is not the only answer to these new challenges, but it is often a pivotal part of the solution. In practice, established methods still deserve their place in development and manufacturing, and they can be used to reinforce AI’s support of human intelligence, ingenuity, and innovation. Reaching the ultimate goal of lights-out manufacturing—closer and closer to the vision of AI—will still require tremendous effort and a substantial boost in IT infrastructure. AI will also shape the future of pharmaceuticals by improving candidate selection processes for clinical trials.

Discover in detail AI’s role in addressing educational challenges, boosting engagement, and improving learning outcomes. Also, dive into real-world examples like Duolingo and Coursera to understand how the technology is making a real impact. The use of AI in manufacturing operations in coming years is only expected to accelerate.

AI also helps Toyota know what cars people want to buy so they can make just enough without making too much. • Sensors in a robotic surface finishing cell can be used to build a model of a part in the cell, which could eliminate the need for expensive part-holding devices. To eliminate the possibility of collision, tool paths need to account for uncertainties in part models created using sensors, which requires AI. • When the system enters an error state, digital twins can be used to diagnose the problem and recommend the necessary recovery actions. • Digital twins provide information to task planners and schedulers to make decisions based on the state of the manufacturing system. Lastly, an effective Generative AI strategy necessitates a culture of innovation and experimentation.

• AI and robotics are transforming agriculture, improving production, sustainability, and efficiency.
• In October 2023, Forbes Advisor conducted a survey of 500 educators across the US to gather insights on their experiences with the cons and pros of AI in education.
• Airbus, with Neural Concept’s tech, cut aircraft aerodynamics prediction time from one hour to 30 milliseconds using ML.
• AI-enabled systems use sensors to assist with steering and pedestrian detection, monitor blind spots, and alert the driver accordingly, enabling them to take preventive measures to stay protected against road accidents.

This is not just limited to students; upskilling and training the existing business workforce can boost morale and spark a company-wide commitment to innovation and digital transformation. Also, with their 24/7 availability, these tools extend support beyond traditional class hours, catering to students’ needs whenever they arise. By offering personalized guidance, these technologies promote self-directed learning examples of ai in manufacturing and empower students to interact actively with educational materials. This personalized approach contributes significantly to their academic growth and achievements. The application of AI in education allows users to create and update information frequently to keep the lessons up-to-date with time. The users also get notified whenever new information is added, which helps prepare them for upcoming tasks.

Supporting experimentation approaches for AI use cases where failure is condoned and success is celebrated will help in this, driving adoption across the organisation. For this reason data lakes require substantial data management, with cataloguing necessary to keep track of all available information and make the information accessible to users. “To create value with AI, 3B is not only aiming for the low-hanging fruit, but also the problems with larger value potential.” said Dimitri Laurent, Global Operations Director and Glass Science and Technology leader at 3B. “For manufacturers, AI promises to be a game-changer at every level of the value chain.” said Badr Al-Olama, Head of the Global Manufacturing and Industrialisation Summit (GMIS).

Its suite of AI tools performs tasks like text generation, arithmetic and results predictions. It can also integrate other datasets in response to user input, such as summarizing information on a page, fixing grammar errors and analyzing large text-based data sets to generate insights. Domino Data Lab helps enterprises expedite the development and deployment of data science work. The company provides tools for building and productizing generative AI, including model fine-tuning for privately training and refining commercial and open source models, and prompt engineering for using any gen AI service securely. Tiendapers can order baked goods, fresh produce, frozen food, dairy products, pantry staples and other items through Instacart’s platform and then schedule a delivery or pickup time. The company uses AI in a variety of ways to enhance both online and in-person shopping.

The benefits of AI agents include faster and more accurate task completion, increased efficiency, and improved customer experiences. AS with every new technology, there are also potential drawbacks, such as the possibility of errors or unintended consequences. One of the biggest benefits of AI in travel is the increased efficiency of tasks such as booking, inquiries, and customer service.