How AI is Used in Manufacturing: Benefits and Use Cases

Some examples of this in practice include Pepsi and Colgate, which both use technology designed by AI startup Augury to detect problems with manufacturing machinery before they cause breakdowns. By using a process mining tool, manufacturers can compare the performance of different regions down to individual process steps, including duration, cost, and the person performing the step. These insights help streamline processes and identify bottlenecks so that manufacturers can take action. This means augmenting or, in some cases, replacing human inspectors with AI-enabled visual inspection. This increases accuracy and shortens the time for inspections, reducing recalls and rework and resulting in significant cost savings.

• These inventions make information-sharing faster and easier while streamlining production through automation, real-time data collection and more.
• The ideal situation would be materials in, parts out, with sensors monitoring every link in the chain.
• Using AI in the manufacturing process often obviates the need for quality control.
• At the end of this blog, it is fair enough to say that Artificial intelligence is strongly paving its way in various industries, but manufacturing businesses are experiencing tremendous growth.
• Manufacturers should start applying generative AI or other technologies to targeted initiatives to learn, develop skills, and secure early wins that can be used to build organizational momentum and gain buy-in.
• Design, process improvement, reducing the wear on machines, and optimizing energy consumption are all areas AI will be applied in manufacturing.

The technology is also assisting enterprises with data-driven decision-making, and driving innovation and productivity across the entire manufacturing lifecycle. AI in the manufacturing industry is proving to be a game changer in predictive maintenance. One big advantage of cobots over traditional industrial robots is that they are cheaper to operate as they don’t need their own dedicated space in which to function. This means they can safely work on a regular plant floor without the need for protective cages or segregation from humans. They can pick components, carry out manufacturing operations like screwing, sanding, and polishing, and operate conventional manufacturing machinery like injection molding and stamping presses. They can also carry out quality control inspections using computer vision-enabled cameras.

Why is Explainable AI in Manufacturing Industry Necessary?

Autonomous robots or robotic process automation can perform a variety of tasks in manufacturing companies, such as assembly, material handling, and quality assurance. This technology helps manufacturers improve efficiency, reduce costs, and improve worker safety. Manufacturers can use it to reduce their carbon footprint, contributing to a fight against climate change (and adjusting to the regulations that are likely to get even stricter). And since AI can significantly reduce operations costs, they invest more in process improvement resources, becoming more and more effective over time. In the manufacturing sector, artificial intelligence (AI) is having a profound impact. Let’s take the example of a manufacturing plant that produces consumer goods.

Predictive maintenance enabled by AI allows factories to boost productivity while lowering repair bills. Many more applications and benefits of AI in production are possible, including more accurate demand forecasting and less material waste. Artificial intelligence (AI) and manufacturing go hand in hand since humans and machines must collaborate closely in industrial manufacturing environments.

Envisioning the Future Power of AI in Manufacturing

It’s an exact digital representation of the part and how it will behave if, for example, a defect occurs. (All parts have defects; that’s why they fail.) AI is necessary for the application of a digital twin in manufacturing process design and maintenance. 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.

Today, much of the equipment that manufacturers use sends a vast amount of data to the cloud. Unfortunately, this information tends to be siloed and doesn’t play nicely together. Manufacturing requires acute attention to detail, a necessity that’s only exacerbated in the electronics space.

It seems only a matter of time until other manufacturing businesses realize the value of implementing this technology and reap similar rewards. A digital twin is a digital representation of a physical product in all its aspects. So, digital twins are able to simulate any physical object or process, allowing engineers to digitally create and maintain complex products, machines, factories, or even entire supply chains. In 2011, French multinational manufacturer of food products Danone implemented an ML system to create a more accurate demand forecasting platform. For a partner, the manufacturer chose a global provider of supply chain optimization software systems ToolsGroup.

It can locate empty containers, and ensure that restocking is fully optimised. The usual steps needed for manual form processing are either reduced or eliminated altogether, which at the same time minimises—or altogether eradicates—human error. This is because OCR is able to identify data directly from scanned/printed images, thereby reducing data entry time. It allows for the early detection of defects, and it also lets manufacturers gather multiple statistics that will help them improve their assembly lines going forward. There are vendors who promise a prebuilt predictive maintenance solution and all you do is plug your data in. The solution you need is based on understanding your process and tweaking based on your priorities.

A move toward an AI-enabled factory floor would require manufacturers to reskill their existing workforce and develop, build, and train AI systems. In addition, the integration of AI solutions into existing systems is a difficult task, requiring extensive data processing for replicating a human brain’s behavior. Even a minor error can result in system failure or adversely affect the desired result. Furthermore, the absence of professional standards and certifications in AI/ML technologies is restraining the growth of AI. Additionally, AI service providers are facing challenges with regard to deploying/servicing their solutions at their customers’ sites. A significant portion of organizational business processes is presently controlled by inflexible, rule-based software, which has limited capabilities when it comes to addressing crucial issues.

The attached AI system can alert human workers of the flaw before the item winds up in the hands of an unhappy consumer. For example, a pharmaceutical company might use an ingredient that has a short shelf life. AI systems can predict whether that ingredient will arrive on time or, if it’s running late, how the delay will affect production. 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. Manufacturers can potentially save money with lights-out factories because robotic workers don’t have the same needs as their human counterparts. For example, a factory full of robotic workers doesn’t require lighting and other environmental controls, such as air conditioning and heating. An AI in manufacturing use case that’s still rare but which has some potential is the lights-out factory. Using AI, robots and other next-generation technologies, a lights-out factory operates on an entirely robotic workforce and is run with minimal human interaction. RPA software automates functions such as order processing so that people don’t need to enter data manually, and in turn, don’t need to spend time searching for inputting mistakes.

The Bosch manufacturing and logistics platform helps to access and structure this data. By using AI methods, we gain useful and – compared to human analyzes – objective knowledge very quickly. At the same time, we have accumulated a considerable amount of knowledge and models for many manufacturing processes over the past few decades. Deep learning is an advanced subset of machine learning that mimics the human brain’s neural networks. It excels in tasks that demand intricate pattern recognition, enabling machines to understand and categorize complex visual and auditory data.

Models will be used to optimize both shop floor layout and process sequencing. For example, applying thermal treatment on an additive part can be done straight from the 3D printer. It could be that the material comes in pre-tempered or it needs to be retempered, requiring another heat cycle.

However, experts predict that market demand and competitive pressure will necessitate the adoption of AI in manufacturing to not only cut costs but elevate productivity. This market in Asia Pacific is further divided into China, Japan, South Korea, and Rest of Asia Pacific. The Asia Pacific, led by China, Japan, and South Korea, is considered the largest market for industrial robots. Rapid industrialization in Asia Pacific has boosted the manufacturing sector. Furthermore, Many small and mid-sized enterprises (SMEs) are present in the countries in Asia Pacific considered in this study.

Unfortunately, many companies lack the resources to translate this information to reduce costs and increase efficiency. Moreover, according to a Deloitte survey, manufacturing happens to be the top industry in terms of data generation. Manufacturers will need to adopt AI to analyze this humongous amount of data generated in the sector. Generative design is a bit like the generative AI we’ve seen in technologies like ChatGPT or Dall-E, except instead of telling it to create text or images, we tell it to design products. Manufacturing is one of the highest-risk industrial sectors to be working in with more than 3,000 major injuries and nine fatalities occurring each year.

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