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Industry 4.0: How AI is Revolutionizing the Manufacturing Industry

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We are experienced leaders looking into the future. We recognize the vast potential of AI but understand there are a lot of challenges ahead. We want to be a support vehicle for enterprises and the technology providers leading us into the future - the glue that sticks between the buyers and the sellers of AI.

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The manufacturing sector is under immense pressure. Labour costs are rising, while customer demand for faster, more personalized products is increasing. At the same time, a new technological wave is causing havoc in the industry: artificial intelligence (AI). AI has already begun to revolutionize manufacturing, and its impact will only grow in the coming years. AI has the potential to transform how we manufacture goods and services by making factories smarter and more efficient.

In this blog post, we will look at how industrial AI is changing the manufacturing landscape and discuss some of the benefits it provides businesses.

AI in Manufacturing

AI has accelerated manufacturing progress in recent decades, making factories less labour-intensive and more efficient than ever. The introduction of machine learning was a watershed moment for this industry, as the machinery, which had previously been entirely dependent on programming, would now be able to make its own data driven decisions.

AI has numerous applications in manufacturing today, ranging from advanced prediction to quality assurance to waste reduction. For planning, scheduling, optimization, robotics, and machine vision, we employ artificial intelligence. AI not only increases capacity and space for business growth for manufacturers, but it also gives us hope for a greener and more comfortable future.

How Industrial Artificial Intelligence is Revolutionizing Manufacturing Operations

You should already know the enormous potential that artificial intelligence has - but what about its practical applications? We've gathered a few examples to show how manufacturers can benefit from machine learning and put these algorithms to use.

Production Enhancement

Manufacturers have sought to optimize their production in accordance with the infinite growth principle since the industrial era. The fundamental imperative is to produce more, faster, and at a lower cost. To reduce waste and costs, artificial intelligence can identify inefficient processes in terms of production volume or energy use. Furthermore, robotic assembly lines powered by AI can boost productivity by reducing human errors and speeding up manufacturing processes.

Without careful planning, optimization would be impossible. Before automation was possible, manufacturers would primarily rely on static Excel files to plan their production - but this method would freeze the planning for some time, making them unable to react to changing variables such as supply chain downtimes. However, with the help of AI algorithms, they can automatize planning and respond to changes in real time.

Maintenance Prediction

Every minute of a downtime is costly. When this happens, the factory's manufacturing capacity shrinks or even drops to zero, causing financial damages. Furthermore, returning to previous levels of efficiency takes time. Even the shortest production halt can result in lower quality, rendering the first batch of the product unfit for the market.

Manufacturers would schedule regular maintenance to avoid such scenarios. However, with AI, they no longer have to rely on regular maintenance. Based on a large number of machine data points that track equipment efficiency, intelligent systems can detect and identify mechanical or electrical failure before it becomes a full-fledged downtime.

They use automated protocols to prevent the problem from escalating and to trigger alerts after detecting and classifying it. You can take it a step further by utilizing the power of predictive maintenance to estimate the likelihood of machinery failure (using a regression approach) or even its time of failure (with classification approach).

Waste Reduction

The increase in emphasis of sustainable production on global markets is causing manufacturers around the globe to slowly prioritize waste reduction.

For starters, Artificial Intelligence can be used for research, allowing companies to develop new materials with desirable properties that are biodegradable or fully recyclable. Furthermore, it can assist them in optimizing resource utilization to reduce waste.

AI can achieve this goal through efficient material treatment on the production line, as well as downtime reduction through preventive maintenance. This is because a large portion of industrial waste consists of low-quality products that are unfit for market use, and downtimes can contribute to a periodical quality decrease. Defects in machinery or the manufacturing process can also be detected by artificial intelligence.

Energy Efficient Manufacturing

Manufacturing consumes a significant amount of energy globally, so improving energy efficiency is one of the most important roles of AI in this sector today. We'll need to switch to fully renewable energy sources sooner or later to stop climate change, but in the meantime, we can try using energy in a more thoughtful and sustainable way.

AI systems can assist factories in detecting inefficient processes that waste energy, such as machine defects that cause leaks, improper heating system regulation, or inefficient lighting. For example, depending on the weather and how the windows are distributed, some areas of the factory may heat up more than others. Based on these variables, an intelligent control system can activate and regulate the air conditioning and heating, reducing energy waste while improving comfort.

The same is true for lighting. Switching to energy-saving LEDs is critical, but factories can go a step further and automate the process. Intelligent light distribution and maintenance-free brightness adjustment are two AI powered features that can cut electricity consumption in half. In addition, they reduce the number of human errors as well.

Detecting Defects in Quality Control

The product's quality is determined by a variety of factors, ranging from design to the state of the machinery. Equipment defects, metal fatigue, human errors, and production breakdowns can all have a negative impact on it. Manufacturers may use AI to avoid these issues, such as preventive maintenance, which we have already discussed in the previous paragraphs. However, the algorithms may still not be efficient enough to prevent all events that result in quality loss.

With the help of machine learning algorithms, manufactures can keep an eye on the quality of their products and machineries in the most efficient way possible. Computer vision is rapidly evolving, and it is already capable of advanced defect detection without the need for additional manufacturing and quality engineers. How does it work? The sensing device sends the image to an interpreting device, which analyses it using AI algorithms (most commonly, neural networks) trained on datasets containing defect examples and images of flawed/unflawed products.

Computer vision technology can detect holes, abrasions, scratches, and other undesirable shapes. With its assistance, factories can improve product quality and lifespan while also improving customer experience and reducing waste.

Improving Safety on the Production Floor

Manufacturing companies can use AI in a variety of ways to improve production floor safety. Lighting automation is the first example of such an application, as mentioned previously in the context of energy efficiency. They can use it to respond to real-time lighting demand, brightening up specific areas as needed. This category also includes tracking defects and leaks with preventive maintenance algorithms.

Computer vision can be used by manufacturers to detect potential issues in the facility. When the algorithms detect an anomaly, they send an alert via text message or app to the appropriate personnel who can investigate the problem. The system may also trigger automated safety responses.

Visual inspection using machine learning algorithms can also detect whether workers on the production floor are wearing safety equipment and following health and safety regulations. If this is not the case, they can send a message to a supervisor or sound an alarm. The technology can also monitor workers' fatigue levels and take appropriate action if they appear exhausted.

Supply Chain Management

Forewarned is forearmed – this expression is very applicable in the manufacturing industry. To keep production optimized, manufacturers should not only monitor changes in supply chains or order deadlines, but also plan for various scenarios.

The pandemic has demonstrated that manufacturers underestimated the power of simulation. Many businesses failed because of the market crash as they were unprepared for the unstable supply chains.

Modern advanced planning and scheduling systems enable factories to simulate an infinite number of cases and create scenarios for such occurrences. Analyzing all the possibilities manually would be impossible even with a large, qualified team of researchers. Manufacturers can quickly answer the "what if" question using AI - all they need is a large, high-quality dataset.

Predicting Demand

Manufacturers all over the world have used enterprise resource planning (ERP) systems to optimize resource utilization and maximize profit.

Even though these systems have empowered businesses by allowing for advanced optimization, they are far from perfect. Because their calculations are based on constant parameters and the infinite capacity principle, manufacturers are unable to make realistic predictions. They typically ignore changing variables such as fluctuating demand. This forces companies to play it safe rather than adapt to a changing market.

With the pandemic, many manufacturers have realized that such a planning model will not get them very far. Even during a relatively stable period, product demand can fluctuate, and planning systems must account for these changes. Modern APS systems powered by artificial intelligence continuously update the production plan based on real-time data, reacting to these changes.

This way, manufacturers can avoid overproduction, which has a variety of negative consequences. In addition to avoiding environmental issues and financial losses, it allows manufacturers to save valuable storage space. They can plan production ahead of time using machine learning models that take demand into account. Forecasting techniques may include neural networks, regression analysis, SVR, or SVM.

AIʻs Advantages in Manufacturing

The examples above demonstrate that AI has enormous potential in the manufacturing sector. Of course, manufacturers will benefit from its implementation, but so will the economy and the environment.

To summarize, implementing AI-based innovations provides businesses with the opportunity to:

  • Improve the manufacturing process

  • Improve process parameters

  • Cut operational costs

  • Lessen waste

  • Lessen the carbon footprint

  • Improve safety

  • Enhance product quality

  • Enhance inventory management

  • Avoid downtime

The Future of AI in Manufacturing

According to the predictions, artificial intelligence will continue to automate manufacturing processes, reducing labour demand while increasing output. In the long run, it may reduce the length of the work week and create new job opportunities.

Contrary to popular believes, evolving AI does not reduce the number of manufacturing job openings. Manufacturers may not require as many employees on the production line as they did previously; however, as they transition to a data-driven business model, they will seek out more analysts and data scientists.

With the industrial sectors' current contribution to waste production and energy consumption, making manufacturing processes more sustainable is a priority - and AI and machine learning can help us get there with their pattern-identifying ability. Manufacturers can use it to reduce their carbon footprint, helping to combat climate change (and adjusting to the regulations that are likely to get even stricter). And, because AI has the potential to significantly reduce operational costs, they are investing more in process improvement resources, which are becoming increasingly effective over time.

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