Learning from the most advanced AI in manufacturing and operations

Some manufacturers are turning to AI systems to assist in faster product development, as case with drug makers. AI systems can keep track of supplies and send alerts when they need to be replenished. Manufacturers can even program AI to identify industry supply chain bottlenecks.

• One McKinsey study found that image recognition programs may increase defect detection rates by up to 90% compared to human inspection.
• Predictive maintenance is the best-practice strategy that identifies and rectifies possible equipment failures before they happen.
• Yet across industries, manufacturing business leaders are finding that data is finally “waking up” to the nuances and fundamentals of their business operations.
• Our AI services and applications for manufacturing helps to achieve smart manufacturing operations and reduce cost overheads.
• The AI software is trained on a dataset of images of car parts that have been labeled as defective or not defective.

AI-powered demand forecasting tools provide more accurate results than traditional demand forecasting methods (ARIMA, exponential smoothing, etc) engineers use in manufacturing facilities. These tools enable businesses to manage inventory levels better so that cash-in-stock and out-of-stock scenarios are less likely to happen. In the sphere of manufacturing, AI development services are varied and innovative.

Artificial Intelligence in Manufacturing: Real World Success Stories and Lessons Learned

Businesses may fundamentally revolutionize their processes by implementing AI in their manufacturing plants. In order to understand the amplitude of its impact, organizations are already testing genAI-based solutions in various departments. It is not surprising that manufacturing is one of the biggest waste-producing industries. Reasons for that vary from inefficient planning to defective products caused by human error. Predictive maintenance is an “older” and more familiar concept in manufacturing. It refers to the use of sensors to monitor equipment and predict possible failures before they happen.

The last one was the importance of having use cases—a handful of use cases that matter to them. Having a clear sense of what those use cases are and making sure that the momentum and impact from that was important. Momentum is extremely important here, and leaders realize the value of having a strong momentum here to keep the engine running. Therefore, we’re starting with an early win to build up the momentum to gradually become more sophisticated over time.

Top 11 case studies of artificial intelligence in manufacturing

This innovation employs deep neural networks to spot defects that escape conventional vision systems and human scrutiny. This technology overhaul streamlined inspections, boosting efficiency by over 30% and elevating product yield by an impressive 97%. This shift also optimally utilized factory floor space by retiring legacy inspection setups, paving the way for other lines and solutions.

This addresses the challenges of limited work area real estate and slow printing needs. There are several general practices to deliver the best AI product; most of them you can find in an execution plan for an AI project we published. Manufacturers can sometimes reduce dependency on distant but cheap manufacturing facilities. AI in manufacturing can solve the logistic problem by producing serial parts in-house or at near-shore facilities with 3D printing, thus, managing inventories more efficiently. For example, the system can alert supervisors when equipment operators show fatigue signs.

Why is AI important in the manufacturing industry?

A constant challenge of manufacturing are overstocking (leading to wastage and lower margins) and under-stocking (causing losses in sales, revenue, and customers). According to McKinsey, using AI to automate processes can enhance yield by up to 30% and reduce scrap rates and testing costs. Of all the technologies highlighting this year’s predictions, data digitization and analysis represent the most mature of the trio. Plant-wide lattices of IIoT devices can capture information on vibration, temperature, humidity, quality check results, cycle times, just about anything you can register and quantify with a sensor. OEMs continue to struggle to create a full smart glasses package that delivers quality of experience alongside acceptable design, form factor, and price.

Before starting production, AI-based product development can create simulations and test the exact production features and circumstances using AR (augmented reality) and/or VR (virtual reality). As its name implies, the technology directs hardware to add layer by layer to create particular objects with the help of data computer-aided-design (CAD) software or 3D object scanners. Contrarily, a traditional way to create an object often requires removing material through milling, machining, carving, shaping, or other means. AR/VR provides an even more intuitive environment, so the more that companies can present in virtual and augmented reality, the more effective they are going to make technicians and engineers,” Tutt says.

Autonomous robots and machine learning-powered predictive analytics means companies are able to streamline processes, increase productivity and reduce the damage done to the environment in many new ways. Cameras and sensors identify discrepancies in products, allowing for immediate corrective actions. This real-time defect detection ensures that only high-quality goods reach consumers, reducing waste and rework costs. For instance, our client, a global manufacturer of heavy construction and mining equipment, faced challenges with a decentralized supply chain, resulting in increased transportation costs and manual data resolution. To address this, we developed a data-driven logistics and supply chain management system using AI-powered Robotic Process Automation (RPA) and analytics.

E.g. fluid dynamics simulations are very time-consuming to compute, but it’s super important to understand the aerodynamics of an airplane component. AI simulations reduce the time it takes to run the simulation itself, so you get the results faster and can go back to designing a smarter system. In a recent example, Subaru researched the connection between fluid flow at multiple crank angles to tumble intensity at the top dead center. The Simcenter Engineering Services leveraged flow feature images from in-cylinder analysis to train the AI to predict this tumble intensity. As a result, the interpretation time was drastically reduced from hours to just a few minutes using AI.

AI and ML technologies analyze massive amounts of data from the market to predict preferences that influence product designs. Moreover, these systems can combine historical data with external factors to identify the root cause of the deviation, such as equipment malfunctions, suboptimal workflows, or supply chain issues. But even beyond product quality and waste reduction – AI plays a significant role in creating a more sustainable manufacturing industry. Companies can now introduce AI-powered waste sorting systems that are more efficient than any human could be. The forecasts can also be done on a granular level, helping organizations optimize for specific products and locations.

By enhancing manufacturing processes, gen AI can reduce downtime, improve output, realize cost savings, and boost end-user satisfaction. No wonder 82% of organizations considering or currently using gen AI believe it will either significantly change or transform their industry (Google Cloud Gen AI Benchmarking Study, July 2023). Quality assurance is the maintenance of a desired level of quality in a service or product.

AI helps reduce unnecessary energy consumption through efficient scheduling of processes within high-resource times with less delays or long response times. Using predictive maintenance to schedule repair works to quieter hours and understanding downtimes contributes to lower operational costs too. In 2023, Artificial Intelligence (AI) is becoming increasingly essential to the day-to-day operations of manufacturers all over the world.

The reason for this is that AI helps manufacturers do quality inspections faster, more accurately, and at cheaper prices. Manufacturers can teach AI systems what attributes are acceptable in their products. Following that, the machines analyze each product using computer vision technology and autonomously decide whether it fulfills the predefined requirements.

Read more about Cases of AI in the Manufacturing Industry here.