Manufacturing AI Use Cases and Trends An Executive Brief Emerj Artificial Intelligence Research

Thanks to AI, what ‘happens outside the factory’ is no longer a passing thought for those responsible for the inside. In fact, manufacturers are looking for any external data points they can gather – about their vendors, supply chain, purchases, and inventory levels over time – that will let them estimate their inventory levels a month from now. To address unplanned downtime, the first tool in a manufacturer’s business arsenal is a concept called predictive maintenance, and one particularly conducive to the advantages of AI adoption. The enemy of all manufacturing operations is a breakdown (or “unexpected downtime” as it’s referred to in heavy industry).

British Petroleum leverages AI to transform its operational efficiency and cost-effectiveness. Their use of AI in geological data analysis streamlines the identification of potential drilling sites, ensuring higher accuracy and better resource allocation. AI in the oil and gas industry facilitates the identification of complex patterns and correlations within the data, enabling more accurate predictions of reservoir behavior. These models also assist in generating high-resolution ChatGPT reservoir models, which are crucial for simulating various extraction scenarios and determining the most efficient recovery methods. Organizations can integrate their existing QC system with automation and robotics to improve the speed of building, scanning and deciding whether to accept or reject products. This requires manufacturers to invest in educating the workforce about AI, exploring its value and benefits whilst also outlining limitations and risks.

“Actionable insights help plant staff make better operations and maintenance decisions that improve efficiency and increase flexibility,” said Tom Logan, senior manager of technology integration at Mitsubishi Power Americas. But AI usage is happening more in some parts of the world than others, with the U.S. lagging behind. 51% of European manufacturers are implementing AI, compared with Japan at 30%, and the U.S. at 28%. The two most common use cases for AI in manufacturing, according to Capgemini, are maintenance and quality control. Safeguarding industrial facilities and reducing vulnerability to attack is made easier using artificial intelligence-driven cybersecurity systems and risk detection algorithms. Using AR (augmented reality) and VR (virtual reality), producers can test many models of a product before beginning production with the help of AI-based product development.

Real-World Examples of AI in the Oil and Gas Industry

Dropbox offers an array of cloud-based products that enable file storage and sharing as well as digital project collaboration. Dropbox Dash is the company’s AI-powered search tool that summarizes and organizes content from various sources into a single dashboard so users can access and share information as needed. By assembling large sets of transaction and consumer data and deploying AI to analyze it, it can assess the likelihood and identify instances of policy abuse, fraud and chargebacks. On the Riskified platform, AI analysts monitor traffic without supervision, and are able to report anomalies and suspected organized fraud, which can be tremendously expensive to e-commerce companies. Artificial intelligence is proving to be a game-changer in healthcare, improving virtually every aspect of the industry. It has an AI-powered video platform that is trained to understand contextual clues from live gameplay, which allows coaches to review game events.

• Artificial intelligence in education holds immense potential to address the gaps that global education systems are struggling with and revolutionize the entire industry with its diverse use cases (detail later).
• Legacy systems are common in manufacturing companies for many reasons, including unclear ROI for upgrades and the overhead of implementing newer tech, but AI might not be able to integrate with older systems.
• For decades entire businesses and academic fields have existed for looking at data in manufacturing to find ways reduce waste and improve efficiency.
• For example, in May 2024, OpenAI introduced ChatGPT Edu, a version of ChatGPT designed for higher education institutions with enhanced security and privacy measures.
• If the accuracy of a digital AI tool is 99%, it can tremendously boost human productivity in many applications.

Such features will only accelerate the industry’s growth as it lowers the barriers to travel, meaning AI is set to be a boon for the industry. IBM’s (IBM 0.04%) Watson AI is one of the programs that can perform this kind of analysis, integrating data from 100 models and incorporating information from 250,000 weather stations worldwide. We’ll discuss what AI means in the context of travel, review several AI applications for travel, and look at what’s next for AI and the travel industry. Multimodal generative AI models can generate text descriptions for sets of images, Gupta said. This capability can be applied to caption videos, notate and label images, generate product descriptions for e-commerce, and generate medical reports.

Pharmaceutical Industry

In a related application, organizations are deploying AI-powered systems that coach employees as they work. The technology, experts explained, has the capability to monitor and analyze actions in near real time and provide feedback, thereby coaching or guiding workers through the process. Predictive maintenance powered by AI helps in anticipating equipment failures, thereby minimizing downtime and preventing costly disruptions.

Applying AI, they simulate reservoir behavior to maximize extraction efficiency and recovery rates. Real-time AI analytics enhance safety measures by identifying and mitigating potential hazards, showcasing ExxonMobil’s examples of ai in manufacturing dedication to innovative operational excellence. You can foun additiona information about ai customer service and artificial intelligence and NLP. In the oil and gas industry, AI-driven enhanced reservoir characterization and modeling has revolutionized the way companies manage and optimize their reservoirs.

This approach lacks strategic consideration, the understanding that all technological implementation should be designed around serving the businesses specific needs. In the race to make the most of generative AI, some companies are leading the charge and are not just adopting this technology but defining its future. Three of the top generative AI companies that push the boundaries of AI transformation include OpenAI, Microsoft, and Google. The efficiency gains from AI integration translate into cost and time savings, allowing resources to be redirected to more critical tasks and opportunities. The global AI market for the food and beverage industry is set to reach $35.42 billion by 2028. Airbus, with Neural Concept’s tech, cut aircraft aerodynamics prediction time from one hour to 30 milliseconds using ML.

With AI for schools, education, and virtual classrooms, the technology takes up most value-added tasks. Along with creating a tailored teaching process, AI for education can check homework, grade tests, organize research papers, maintain reports, make presentations and notes, and manage other administrative tasks. AI for education can help generate bit-size learning through low-storage study materials and other lessons in digital format.

In 2020, online gaming witnessed a significant surge due to the global COVID-19 pandemic, which forced game enthusiasts to be homebound and find new ways to satisfy their gaming appetites. While the growth trend has normalized, online gaming is still popular, with over 2.5 billion active gamers worldwide. Looking ahead, AI holds immense power to redefine the industry’s future, driven by NPCs (more details later). Statista predicts that by 2027, a significant majority of residents in the US (64%) and UK (70%) will be classified as gamers in the future, reflecting the market’s growing significance. AI in gaming propels effective game development and delivers more adaptive experiences, ushering the industry into a new era of innovation, experience, and limitless possibilities.

Electronics Industry

The startup’s AI, based on convolutional neural networks, learns to replicate the expert’s actions and monitors the assembly process in real time to ensure each step is performed correctly. It provides immediate feedback when it detects errors, such as misplaced components or incorrect wiring, that allows workers to correct mistakes without supervisor intervention. Rapta also continuously trains workers by offering live, visual guidance to accelerate workforce development while maintaining quality control throughout production. It uses AI-driven demand forecasting and mathematical optimization to streamline production planning, inventory management, and logistics. Further, the Demand Planning (DP) tool provides AI-powered forecasting for real-time decision-making. Plan Optimus enables manufacturing companies to align supply chain strategies with business objectives to reduce costs and enhance responsiveness.

Smart contracts automate transactions and agreements, reducing fraud and improving efficiency. Leveraging real-time analytics, AI can pinpoint inefficiencies and highlight areas for enhancement, fostering a culture of continuous improvement. Additionally, predictive analytics can forecast market trends and consumer behavior, providing a competitive edge and facilitating proactive strategies. This comprehensive utilization of data transforms raw information into actionable insights, ensuring sustainable growth and operational excellence. After manufacturing, robotics and artificial intelligence in food processing can assemble the components of a packaged meal, such as frozen meals.

Once clients have this information, they can use the platform to generate, test and implement messaging campaigns and features like personalized product feeds. McDonald’s is a popular chain of quick service restaurants that uses technology to innovate its business strategy. Two of the company’s major applications for AI are enabling automated drive-thru operations and continuously optimizing digital menu displays based on factors like time of day, restaurant traffic and item popularity. Advanced sectors like AI are contributing to the rise of the global travel technologies market, which is on track to exceed $10 billion by 2030.

Here are some examples of how artificial intelligence is being used in the travel and transportation industries. Here are a few examples of how artificial intelligence is changing the financial industry. Well develops a personalized health plan for each customer by collecting data on pre-existing conditions, ongoing health concerns and gaps in general health knowledge. Based on personal and external health data, users receive coaching, tips and rewards to encourage them to keep improving their individual health. Along each user’s health journey, Well offers guidance for screenings, questionnaires, prescriptions, vaccinations, doctor visits and specific conditions.

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AI and robotics are essential for taking this sector to the next level because of their usefulness, reliability, and client experience. This process entails a variety of stages, such as packing and safety training, that are usually performed in a production facility. Let’s explore the profound impact of AI in the food industry, highlighting its benefits, applications and potential to address global challenges and cater to the rapidly evolving demands of today’s consumers. He cited a company EY worked with that built protective sheets for kitchen countertops and was experiencing massive product recalls.

Instead of relying on scheduled maintenance or waiting for problems to occur, manufacturers can use GenAI solutions to forecast issues and carry out maintenance only when necessary, reducing unplanned downtime. In addition, AI-generated insights can recommend reliable fixes, helping maintenance teams address problems faster. Manufacturing companies can use generative AI to quickly create multiple prototypes based on particular goals, like costs and material constraints, optimizing the product design and development process.

Marketing content creation is one of the top multimodal generative AI use cases seeing relatively substantial traction, Gupta said. Multimodal models can integrate audio, images, video and text to help develop dynamic images and videos for marketing campaigns. Vishal Gupta, partner at advisory firm Everest Group, observed that current multimodal AI models predominantly focus on text and images, with some models including speech at experimental stages.

Predictive maintenance significantly reduces the chances of unexpected machine failure by constantly monitoring equipment. Predictive maintenance is the use of artificial intelligence (AI) technology and the “Internet of Things” (IoT), the digital connection and communication between multiple objects, to predict maintenance needs. Essentially, it monitors information from machines and devices and then uses AI technology to predict when and where maintenance will be needed.

A. AI in the education sector refers to the use of artificial intelligence technologies to enhance learning experiences, personalize education, automate administrative tasks, and provide intelligent tutoring systems. Together, education and AI help create personalized educational content and improve overall educational outcomes. Artificial intelligence in education offers personalized learning experiences, automates administrative tasks, and provides real-time data analysis.

The company has AI software products for a wide range of industries, including public sector organizations, media and entertainment, and talent acquisition, each with specific needs and contexts. Its media software includes a media management tool that gleans insights from analyzing existing content and then delivers recommendations for content creation. Machine learning (ML), a branch of AI, is the mostly widely used application in the manufacturing sector, enabling companies to modulate the production process and enhance quality.

A clear example of a digital champion is FANUC, one of the largest manufacturers of industrial robots in the world. The whitepaper explores how the manufacturer exemplifies an AI-led future, developing robots capable of building, inspecting and testing themselves. With 22 advanced sub-factories, ChatGPT App FANUC’s approach to artificial intelligence embodies effective AI implementation. Digital champions are manufacturers who have strategically identified and implemented digital solutions across their operations to serve business needs and enhance the connectivity of their value chain.

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