Factories are getting smarter as companies are increasingly able to leverage AI to transform information from various aspects of the manufacturing system into actionable insights. However, many gaps still exist that should be addressed to ensure that AI can be seamlessly integrated into factory operations. AI is still in relatively early stages of development, and it is poised to grow rapidly and disrupt traditional problem-solving approaches in industrial companies. These use cases help to demonstrate the concrete applications of these solutions as well
as their tangible value.
By tapping into it, GM engineers can swiftly explore numerous high-performance design choices ready for production. Since 2016, GM has rolled out 14 new vehicle models, slashing an impressive 350 pounds per vehicle. Based on recent reports, GM is working to integrate ChatGPT and incorporate a vehicle assistant that uses AI models behind ChatGPT, tailored for drivers. It is further embracing AI for manufacturing, enhancing efficiency in its Spartanburg plant. This results in data-driven decision-making, faster design cycles, and the ability to create products that fit market needs.
Remarkable Use Cases of AI in the Manufacturing Industry
Additionally, AI solutions can be implemented in ironmaking stages such as smelting and casting, where temperature control is critical for obtaining desired output grades of steel alloys. AI is a wide-reaching technology with numerous applications in the manufacturing industry. By implementing AI, organizations gain the ability to transform their processes, from design to maintenance, production, forecasting, customer relations, and beyond. Also, the quality control modeling outputs can be improved even further by utilizing large language models to extract textual information from assorted reports and refining the data through quantitative measures. This approach can enhance the manufacturing process’s efficiency and effectiveness, producing higher-quality products.
- ” After all, the machine is still costing the manufacturer whether it works or not.
- We’ll discuss how ranking your developers with objective data will identify your top and worst producers, which empowers you to make strategic decisions that save money and time.
- The business importance of being able to predict these variables, whether there is a global pandemic or not, cannot be overstated.
- Sensors in the machines can link to models that are built up from a large data set learned from the manufacturing process for specific parts.
- In the webinar, Rick described AI use cases featuring several manufacturers he has worked with including Precision Global, Metromont, Rolls-Royce, JTEKT and Elkem Silicones.
This adept vision system identifies misaligned, missing, or incorrect components with minimal room for human error. Each oversees a different production stage—from conception to assembly to operation. It also suggests energy-saving opportunities, boosting overall production line performance. A pivotal component of predictive maintenance is the digital twin—an online replica of a physical asset. The potential of AI and machine learning algorithms in manufacturing is only beginning to unfold. Beyond their established roles in robotics and automation, AI in manufacturing is now making its mark in broader areas.
The Slow Acceptance Of Digitalization
Kellogg’s AI endeavors are firmly rooted in practicality, focusing on real business challenges and marketplace needs. The outcomes speak for themselves – Kellogg’s AI integration has led to reduced waste in the supply chain and a noticeable boost in sales. Kellogg’s has fully embraced the potential of AI across operations, from enhancing supply chain efficiency to crafting optimal flavor combinations for new products. With the integration of AI in manufacturing, companies are embracing more efficient workflows and redefining product development.
Now, the Fourth Industrial Revolution is being shaped by cyberphysical systems—intelligent computational capabilities. And one of the key types of disruptive technologies behind reshaping the value chain is Artificial intelligence (AI). Leading retailers – like Walmart, Stop & Shop, and Home Depot – are enhancing their payment and fraud detection systems, using artificial intelligence that learns transaction norms and infers risk from the context of each transaction. Verizon is the second-largest telecommunications company by revenue and the largest by market capitalization. The company is also the largest wireless provider in the United States with a reported 143 million subscriptions.
Digital twins help boost performance
Compared with high-value AI initiatives in other industries, manufacturing use cases tend to be more individualized, with lower returns, and thus are more difficult to fund and execute. Through intellectual rigor and experiential learning, this full-time, two-year MBA program develops leaders who make a difference in the world. It is important to note that more effort is needed to promote AI from the perspective of the industry and facilitate the broad acceptance of AI techniques.
An online AI technique [85] developed for automatic and unsupervised clustering of basic HRC operational steps uses real-time force/torque data to address the challenge in human cycle time variability. The specific AI method developed and experimentally tested uses dynamically trained one-class support vector machines (OCSVMs) to discover states of manufacturing process steps. This type of online algorithm demonstrates the ability to realize real-time performance without the penalty of requiring labeled data from training phases. Second, conventional throughput improvement approaches focus mainly on long-term steady-state performance analysis, which are not applicable to real-time throughput prediction and production control.
Sensors Capture Data for Real-Time AI Analysis
A real-world example of this concept is DRAMA (Digital Reconfigurable Additive Manufacturing facilities for Aerospace), a £14.3 million ($19.4 million) collaborative research project started in November 2017. Developers are building an additive manufacturing “knowledge base” to aid in technology and process adoption. The realistic conception of AI in manufacturing looks more like a collection of applications for compact, discrete systems that manage specific manufacturing processes. They will operate more or less autonomously and respond to external events in increasingly intelligent and even humanlike ways—events ranging from a tool wearing out, a system outage, or a fire or natural disaster. For manufacturers, this means exploring and implementing new technologies to streamline production and create a better-finished product.
This involves using sophisticated algorithms to identify chip design flaws early in the production process and correct them before further costly delays occur. Additionally, artificial neural networks are being trained on massive datasets related to semiconductor fabrication processes to improve design integrity and optimize cycle times throughout each step of the manufacturing flow. Integrating AI into manufacturing operations can also provide significant advantages in predictive maintenance.
Manufacturing Innovation Blog
Manufacturers leverage AI technology to identify potential downtime and accidents by analyzing sensor data. AI systems help manufacturers forecast when or if functional equipment will fail so its maintenance and repair can be scheduled before the failure occurs. Thanks to AI-powered predictive maintenance, manufacturers can improve efficiency while reducing the cost of machine failure.
AI models will soon be tasked with creating proactive ways to head off problems and to improve manufacturing processes. AI-driven predictive analytics uses historical data, market trends, and external factors to forecast demand accurately. This is crucial for manufacturers to adjust production levels, resource allocation, and inventory management. Accurate demand forecasting reduces the risk of overproduction and stockouts, leading to better cost management and improved customer satisfaction. Amid the rapid evolution of modern manufacturing, the infusion of artificial intelligence (AI) has ignited an unparalleled revolution. This article covers the impact of AI in manufacturing, spotlighting its exceptional use cases.
Challenges of Implementing AI in Manufacturing
Manufacturing is entering a period of substantial innovation and change driven by the increased integration of sensors and the Internet-of-things (IoT), increased data availability, and advances in robotics and automaton. To date, the implementation of AI in modern manufacturing has been built on the progressive development of a series of techniques over many decades, such as machine learning (ML) [2]. Further, a review of state-of-the-art AI applications helps to identify some unique manufacturing problems where AI techniques might provide solutions and thus significantly improve productivity, quality, flexibility, safety, and cost. Such knowledge and understanding are of great benefit to the practical implementation of AI in today’s highly complex industrial environments that each has its own individual requirements and context. AI technologies have made manufacturers more efficient, productive, and streamlined. This has hugely minimized the need for manual labor to operate factories, thus increasing efficiency even further and reducing operating costs drastically.
Additionally, case studies prove that integrating AI trained on company data can reduce necessary human resources, make a plant more agile and improve the bottom line. It also helps with sustainability initiatives, which have become a pain point for many manufacturers as the climate crisis looms. In the same vein, many manufacturers believe that AI is intended to take away jobs or replace humans entirely, but this isn’t the what is AI in manufacturing case. While it does take over routine tasks, it also modernizes and digitizes jobs that most young people would otherwise not want to do. In the same way you can’t take the head chef out of a kitchen, most manufacturers believe removing a steelworker from the production floor is virtually impossible. The trade-off for this expertise is a more considerable margin of error (because they’re human) and overall higher costs.
Traditional centralized manufacturing control approaches and software packages are developed and adapted case by case and lack flexibility, expandability, agility, and reconfigurability [38]. On the other hand, multi-agent-based control approaches derived from distributed AI techniques provide several important benefits such as robustness, reconfigurability, and responsiveness [39]. A network-based representation of the system using BoM can capture complex relationships and hierarchy of the systems (Exhibit 3). This information is augmented by data on engineering hours, materials costs, and quality as well as customer requirements. With this enhanced network build, companies can query and make predictions—for example, what subsystems a customer requirement might affect and the engineering efforts that are most likely to cause rework in a project based on interdependencies.
