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AI in Manufacturing: Benefits, Use Cases, and What’s Next

Artificial intelligence empowers manufacturers to achieve unprecedented levels of efficiency, productivity, and customization. In this article, we will explore the tangible benefits and most common use cases, and discuss what the future holds for AI-driven manufacturing.

 

Whether you’re a manufacturing veteran or a tech enthusiast, this article will help you understand the significant role AI has to play in shaping the future of manufacturing.

Artificial intelligence has the potential to transform entire industries – and manufacturing is no exception. Thanks to advances in data analytics, we now have a strong foundation for adopting AI-based technologies, which can use that data in remarkable ways.

These solutions combine data from sensors, machines, and people to help manufacturers become more data-driven in order to optimize production and maintenance processes, improve product quality, and answer the personalization needs of the end customer.

According to Accenture, the manufacturing industry stands to gain $3.78 trillion from AI by 2035.

In this article, we will explain:

How AI Is Used in Manufacturing

AI in manufacturing refers to using data in combination with machine learning and deep learning algorithms to automate tasks and make manufacturing operations faster, better, and more precise.

The main steps include collecting and pre-processing manufacturing data, developing and testing AI models, and putting them into production. These algorithms are then plugged into various applications that aim to improve everything from product quality and manufacturing processes to overall operational efficiency.

Benefits of AI in Manufacturing

Artificial intelligence brings a wide range of benefits to manufacturers – from improving the production process to enhancing customer experience.

In addition, manufacturers can use AI-based technology to address sustainability concerns, mitigate the risks of supply chain disruptions, and optimize resource use in the face of shortages.

In this article, we will focus on three important topics and associated use cases.

Expected benefits across various manufacturing organizations include:

  • increasing operational efficiency
  • optimizing the supply chain
  • improving the product and customer experience

Increasing Operational Efficiency

The ability to increase operational efficiency is one of the main benefits AI brings to manufacturers. By minimizing or automating repetitive tasks, AI solutions allow employees to focus on high-value activities instead. This means people spend less time and resources on low-value tasks, increasing overall speed and productivity.

Likewise, by implementing machine learning capabilities and predictive analytics, manufacturers can predict failures and proactively address potential issues. This results in a more agile manufacturing process that minimizes downtime and removes dependencies.

Optimizing the Supply Chain

By augmenting data analytics with machine learning, manufacturers can foresee market developments and business risks better than ever.

Not limited to just internal data, they can also analyze external factors to model hypothetical outcomes based on different scenarios.

This precision applies to everything from demand forecasting to efficiency loss. It allows manufacturers to optimize every link of the supply chain – making it more resilient and customer-centric.

Improving the Product and Customer Experience

Today, AI is the critical ingredient for improving customer experience across industries – and manufacturing is no exception.

By combining manufacturing data with signals from the market and running them through machine learning algorithms, manufacturing leaders can get a better understanding of what their customers need and want. They can then customize and personalize their products to match the customer’s preferences.

However, customer experience goes far beyond the product, and AI is the perfect partner to help every step of the way. Thanks to predictive maintenance and superior quality control, AI supports a smooth customer experience with minimal failures or interruptions. And with continuous customer feedback, machine learning models can learn and continuously refine and improve the overall experience.

AI in Manufacturing Use Cases

As we mentioned, there are many different applications of AI within manufacturing. So, you might be confused about where to start.

That’s why we’ve grouped the different use cases based on which benefits they feed into. This grouping will help you pick the best option for your business context.

1. Factory Automation

Factory operators play a major role in the smooth running of the factory – no matter how advanced the system is. These experts rely on their knowledge and experience to manually adjust the equipment or material and troubleshoot unexpected issues.

Their work is crucial – but also prone to human error. That’s why factory automation is used to optimize the manufacturing process within a facility.

By embedding AI capabilities into factory machines and equipment, manufacturers can benefit from automation, which allows them to optimize the overall production process.

2. Process Automation

Although process and factory automation sound similar, they focus on different aspects of the manufacturing process. Process automation has a broader scope that goes beyond the factory to include activities that impact the overall results.

For example, let’s take a case where you transform raw material into a product. Here, you might use process automation to optimize the ordering and delivery of said materials to your factory building.

By using AI algorithms, manufacturers can automatically allocate resources, schedule tasks, and optimize processes based on various factors such as demand, availability, and performance metrics.

3. Predictive Maintenance

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. However, there is still room to perfect it – and AI can do a lot to help.

According to McKinsey & Company, AI-based predictive maintenance can boost availability by up to 20% while reducing inspection costs by 25% and annual maintenance fees by up to 10%.

But even though many organizations gather massive amounts of data on their production, they don’t manage to transform it into useful information, let alone action. That’s where AI and ML algorithms come into play.

These technologies analyze the data and create models that describe how components of a complex system interact. They are continuously trained with new data and can give predictions and alerts about anomalies, abnormal patterns, or equipment failure.

This improvement in technology means that you can predict failures with more certainty, preventing production stops, which will cost you money and customers.

4. Demand Forecasting

AI empowers manufacturers to analyze vast volumes of data like never before. AI algorithms combine historical sales data with external factors such as weather conditions, market trends, and economic indicators to make highly accurate demand forecasts.

The forecasts can also be done on a granular level, helping organizations optimize for specific products and locations. In addition, real-time data from various sources allows manufacturers to quickly adapt and respond to changes in demand.

Furthermore, AI algorithms quickly provide simulations of numerous scenarios to help manufacturers make data-driven decisions about demand management.

5. Waste Reduction

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.

With machine vision, manufacturers can detect defective materials or components before they go into production and optimize their quality control system.

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.

And their efficiency increases as they continue to learn until they are able to recognize and cluster hundreds or even thousands of waste types.

6. Efficiency Loss Forecasting

Resource planning, human labor, production process – you name it – when it comes to achieving business goals, it is all about optimization.

AI algorithms can analyze historical data from a range of sources to understand where efficiencies happen and provide accurate forecasting on future deviations.

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.

This ability also helps organizations streamline processes and reduce downtime in the long run.

7. Quality Control

Manufacturers today have an opportunity to fully automate their quality control process. As a result, they minimize the risk of faulty products entering the market and prevent the drop in quality in the first place.

AI systems continuously monitor and analyze data from the production line to provide alerts when they detect quality issues. They also offer insights and recommendations to ensure continuous improvements in quality control.

One of the biggest benefits of AI-based systems is their ability to learn over time. By combining data from various resources and considering certain deviations, AI models can identify potential quality issues and provide forecasts.

8. Product Development

AI has enormous potential when it comes to product development, both for physical and digital products. And here are a couple of examples to support that statement:

  • AI-based technologies can be used to estimate the success of business plans, project duration, resource allocation, and budget needs.
  • Specialized AI programs for software development can examine existing software designs and help software architects create an ideal scenario.
  • AI can now support product security with authentication techniques like CAPTCHA or facial recognition.

Another side of AI’s massive potential lies in physical products. By quickly running thousands of simulations, AI solutions can transform various stages of the manufacturing process, from ideating and prototyping to product testing. And going forward, we can expect massive growth in this market.

9. Product Personalization

Similar to retail, AI plays a major role in product personalization for manufacturing. Customers want customized products, and manufacturers have to keep up if they’re going to survive.

AI and ML technologies analyze massive amounts of data from the market to predict preferences that influence product designs. These technologies are essential for the concept of mass customization.

They help manufacturers adapt production lines to answer individual customer needs and craft unique products while maintaining the efficiency of a well-established process.

10. Product Visibility and Search

AI significantly contributes to enhancing product visibility and searchability by generating high-quality product data. This data is derived from various sources such as customer feedback, online reviews, market trends, and real-time sales data. AI algorithms analyze this data to produce structured and accurate product information, facilitating efficient product searches.

By tagging and categorizing products based on their features, AI simplifies the search process, leading to quicker and more accurate results. This not only reduces the time taken for customers to find the right products but also improves the overall customer experience by making it more personalized and convenient.

What’s Next for AI in Manufacturing

It’s only the beginning of the AI-based revolution, making it an exciting time for manufacturing.

One thing to observe is the focus on generative AI and how it will affect various industries. An important question to ask here is whether it already has a huge impact on manufacturing or if actual use cases are yet to be discovered.

In order to understand the amplitude of its impact, organizations are already testing genAI-based solutions in various departments.

For example, we are already working with customers on implementing solutions for product description automation with generative AI. This refers to the automated creation of detailed and unique product descriptions using artificial intelligence.

Further on, we’re also helping customers improve data quality and product attributes with generative AI.  This specifically means implementing solutions that are able to aggregate and analyze data from relevant sources (including competitors’ websites), to add new attributes or extract attribute values from different sources.

There is no doubt that in the coming years, we will see more and more organizations turning to AI-powered solutions to stay relevant and competitive. Artificial intelligence has already proven its potential in the manufacturing sector, and it’s only a matter of time before it becomes an essential tool for every manufacturer.

Here at NETCONOMY, we’ll definitely keep an eye on the existing AI-based innovations, as well as the evolving role of generative AI in manufacturing – and work with our customers to create valuable solutions.

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Authors and Contributors

Nenad Pantelic | Data Science Consultant

At NETCONOMY, Nenad is responsible for projects that involve business analysis, requirement engineering and specification for data, AI and business intelligence. With his extensive experience in data science, Nenad helps customers understand their challenges and find proper technology solutions to reach business goals.

Mila Petrovic | Head of Marketing

Mila is experienced in developing positioning and messaging strategies, and running marketing projects within the technology and software industry. Passionate about innovation, customer behavior and brand building. Inspired by the power of content marketing and effective storytelling.