AI in Manufacturing: Uses and Benefits

GE already invested over $1 billion in this system, and by 2020 Predix will process over 1 million terabytes of information a day. That’s why the Manufacturing Leadership Council launched the Manufacturing in 2030 Project to enable manufacturers to envision what future manufacturing might look like. Several manufacturing companies are also launching AI robots and AI software to support the production line and reduce the production costs of their manufacturing systems. AI is used in manufacturing to enhance productivity and efficiency, improve quality control, and lower costs. Canon, a global leader in automation and workforce management, leverages machine learning algorithms and AI to transform workflows in innovative ways, driving business process transformation.

In this process, data is collected from various sensors installed in the plant. The data is further processed through big data solutions, which analyze and suggest different mechanisms to improve manufacturing efficiency. AI-based systems aid in various actionable solutions, such as predictive maintenance, production planning, field service, and material movement, which are derived from big data technology. Siemens (Germany) uses big data in AI-based smart boxes that are integrated with sensors and a communications interface for data transfer. By analyzing the data, the AI systems can draw conclusions on the machine’s condition and detect irregularities to provide predictive maintenance. These systems also improve the reliability of power grids by making them smarter and providing the devices that control and monitor electrical networks with AI.

The Fusion of AI Intelligence and Manufacturing: An Overview

The ability to operate a factory at peak performance 24/7 without the need to pay human operators has a massive impact on a manufacturer’s bottom line. Meanwhile, reducing the workload that needs to be carried out by employees is an effective way to stave off the labor shortage. Manufacturing companies usually accept that mistakes are inevitable with orders coming in all the time, multiple logistics companies involved, outdated IT systems, and inventory scattered across numerous locations. This article looks broadly at where AI has the most significant impact on the manufacturing industry.

Artificial intelligence (AI), as well as manufacturing, go hand-in-hand since machines and humans must work together in industrial manufacturing environments. Data collection and analysis are already being used in many industries to predict consumer behavior and generate highly personalized communications to customers or potential customers. A recent study at Indiana University found that machine learning algorithms could even accurately analyze Twitter feeds to scan public sentiment and determine stock market movements.

What is AI in Manufacturing?

For all of the technologies that we’ll discuss that have applications in manufacturing industries, artificial intelligence is not the most accurate way to describe them. AI is a very broad subject that has many different methods and techniques that fall under its scope. Robotics, natural language processing, machine learning, computer vision, and more are all different techniques that deserve a great deal of attention all on their own.

AI systems can reduce the cost of inspection by reducing the lead time and cost of inspections by inspecting only higher-risk areas. Akira AI provides dashboards to track the factors detected much earlier, improving the overall yield. AI-driven inventory management employs real-time data to fine-tune inventory levels based on demand fluctuations, lead times, and supplier capabilities. This ensures that warehouses aren’t flooded with excess stock or barren due to inadequate supply. Quality control has always been a cornerstone of manufacturing, ensuring that products meet stringent standards before reaching consumers. However, the traditional approach to quality control faces inherent challenges that are being overcome by the infusion of Artificial Intelligence into manufacturing processes.

Process and Industry Characteristics

AI systems find faults, streamline inspection processes, and boost overall effectiveness by processing enormous volumes of data, leading to higher-quality products and greater customer satisfaction. Powered by cutting-edge technologies like Big Data and IoT in manufacturing, smart facilities are generating manufacturing intelligence that impacts an entire organization. Today, the manufacturing industry can access a once-unimaginable amount of sensory data that contains multiple formats, structures, and semantics. On the way from sensory data to actual manufacturing intelligence, deep learning received a lot of attention as the leading innovation in computational intelligence. Deep learning techniques enable people to automatically learn from data, detect patterns, and make decisions. We can distinguish different levels of data analytics, including predictive analytics, prescriptive analytics, diagnostic analytics, and descriptive analytics.

The intersection of AI and industry brings forth complex challenges that demand careful consideration, transparency, and a commitment to fairness. The introduction of AI often raises concerns about the displacement of human workers. It’s crucial to understand that AI is not a substitute for human expertise but a tool that can amplify human capabilities. Rather than displacing jobs, AI recalibrates roles, enabling workers to engage in higher-value tasks that require creativity, problem-solving, and adaptability. These are just a few examples of how AI is being used in manufacturing today.

Robotics and automation

When artificial intelligence is paired with industrial robotics, machines can automate tasks such as material handling, assembly, and even inspection. After detecting an issue and classifying it, they use automated protocols to prevent the problem from escalating and trigger alerts. You can go a step further, taking advantage of the power of predictive maintenance and estimating the probability of the machinery failure (with regression approach) or even its time (with classification approach). Not just that, but such solutions let managers monitor the current machine status of all their systems. By tracking data in real time like this, they can imitate real-time responses, as well as quickly understand the forecasted state of damage.

AI in manufacturing and maintenance boosts efficiency and reduces costs in numerous ways. Connected facilities collect vast amounts of data throughout every cycle, every day. AI in factories helps to make sense of this data and facilitate informed, data-driven decisions that can make a major impact on productivity, uptime and the bottom line. In conjunction with data scientists and other personnel who are equipped to understand the data provided by industrial sensors, AI can help make quicker and more effective decisions. AI can correct errors as they occur or, because it is not fallible like humans, create products that are virtually error-free to improve product quality.

Using AI for supply chain management

Simplilearn is one of the world’s leading providers of online training for Digital Marketing, Cloud Computing, Project Management, Data Science, IT, Software Development, and many other emerging technologies. In manufacturing, for instance, satisfying customers necessitates meeting their needs in various ways, including prompt and precise delivery. Operators in factories rely on their knowledge and intuition to manually modify equipment settings while keeping an eye on various indications on several screens.

• Thus, because computer vision systems are trained on so many datasets, they can provide images and assessment with defects such as poor image quality and textured surfaces.
• Machine learning puts data from different sources together and helps you understand how the data is acting, why, and which data correlates with other data.
• Handling these processes manually is a significant drain on people’s time and resources, and more companies have begun augmenting their supply chain processes with AI.

Network experts can help de-risk your company’s adoption of AI and other advanced technologies via hands-on technical assistance, as well as connecting you with grants, awards and other funding sources. MEP Center staff can facilitate introductions to trusted subject matter experts. For areas like AI, where not all MEP Centers have the expertise on staff, they can locate and vet potential third-party service providers. Center staff help make sure the third-party experts brought to you have a track record of implementing successful, impactful solutions and that they are comfortable working with smaller firms. Let the MEP National Network be your resource to help your company move forward faster.

AI for manufacturing, in numbers

According to studies, manufacturing companies lose the most money due to cyberattacks because even a little downtime of the production line can be disastrous. The dangers will increase at an exponential rate as the number of IoT devices proliferates. 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. A digital twin can be used to track and examine the production cycle to spot potential quality problems or areas where the product’s performance falls short of expectations.

Among large industrial companies, 83% believe AI produces better results—but only 20% have adopted it, according to The AspenTech 2020 Industrial AI Research. Domain expertise is essential for successful adoption of artificial intelligence in the manufacturing industry. Together, they form Industrial AI, which uses machine learning algorithms in domain-specific industrial applications. This way, the manufacturers can prevent overproduction, which has various negative implications. Aside from avoiding environmental issues and financial loss, it allows the manufacturers to save precious storage space.

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