AI in Manufacturing: Use Cases, Solutions, and Implementation

As you may well know, artificial intelligence is spreading its effects across different industries like wildfire. The manufacturing industry is one of the most recent sectors to be experiencing its transformative effects. 

Long before AI came into the picture, architects, engineers, and industrialists devised innovative ways to produce products en masse. The application of AI in manufacturing is just the latest innovation in that area. 

But it’s simply different from all the tech revolutions that have come before it. 

The use of AI in manufacturing redefines factory and conceptualization processes as we know them. In this article, we’ll examine AI’s numerous transformative impacts on manufacturing. We’ll also examine some real-life examples of these use cases before concluding with some practical implementation steps for deploying AI solutions into your existing processes. 

Let’s go! 

Top 6 Practical Use Cases of AI in Manufacturing 

In a bid to improve manufacturing precision, efficiency, and innovation, AI has been deployed in several use cases across different manufacturing industries. The following are some of those AI applications in manufacturing: 

1. Product Design, Development, and Customization 

Before any product becomes a reality, manufacturing firms go through a series of brainstorming sessions that lead to conceptualizing the product’s idea. Usually, humans are in charge of these sessions, and they combine ideas from different past and existing products together with data describing what the market wants. 

However, they’re dealing with so much data that it is somewhat inefficient for humans to analyze and glean insights from the existing data. This is exactly where AI shines. By analyzing enormous amounts of data, AI can accelerate product design and prototyping by considering numerous ideas and prototypes. 

Not only that.

AI is also capable of screening these different ideas for cost, manufacturability, energy usage, desirable characteristics, supply availability, and a host of other options that determine if the product is worth producing and how the manufacturing process will be. Furthermore, machine learning algorithms can also be leveraged for competitor analysis, analyzing customer preferences, and monitoring market trends. 

In addition, these AI and ML algorithms can mimic the engineering design processes to ideate hundreds of design options for each product in a fraction of the time it normally takes for engineers to come up with those options. The AI solutions usually used for these processes are often called generative design software. One of the earliest use cases for these generative design software was in computer-aided design (CAD). From engineers and architects to product designers in the manufacturing industry, many professionals use these tools to analyze everything from prosthetics to car parts. Unsurprisingly, the manufacturing industry is witnessing heavy market movers like Siemens, Dassault, and Hexagon playing a huge role in digitalizing the manufacturing process using these tools. 

The takeaway? 

Conceptualizing a product before moving to the manufacturing phase requires a lot of creative effort. AI can speed up that process and achieve even better results by analyzing the data gathered from previous products and generating new and stimulating designs in record time. 

2. Quality Control and Predictive Maintenance 

Apart from helping to bring new product ideas to life, AI can also ensure that those products are safe for use according to the highest quality standards. They play a major role in quality control and assurance processes, ensuring that products are free of defects and anomalies and meet the necessary quality standards. 

How do they do this? 

By ensuring high levels of accuracy and consistency in the manufacturing processes. When it comes to product testing and quality control, AI can be used in computer vision algorithms to spot equipment damage and product defects. The concept behind this use case is that the models are trained with images and videos of the good and defective products and equipment. With this information, it becomes easy for the model to identify any defective one that comes along. 

The same logic can be applied to factory floor processes and machinery. That’s why AI can also be used in predictive maintenance — speculating if a piece of machinery is due for maintenance or likely to develop faults based on previous performances. This means AI can assess how manufacturing machinery is vibrating on the factory floor or how hot they are. Basically, any key indicator of the machine’s performance can be followed automatically to determine if it’s due for maintenance way ahead of time so that it doesn’t churn out products of inferior quality or defects at any time. 

In summary, AI helps ensure that manufacturing processes produce quality products by identifying defective products. It also helps with predictive maintenance by predicting when a machine will have downtime, the possible root causes, and potential ways to solve that problem. 

3. Supply Chain Management and Efficient Demand Forecasting 

AI can also help companies make informed decisions about coordinating the production flow of a manufacturing process. Efficient supply chain management and demand forecasting are largely based on data because they involve optimizing inventory levels, proper route planning, cost reduction, and risk mitigation. 

Data is at the centre of all this. AI algorithms and Big Data Analytics can help with autonomous planning in a way that requires no human intervention. We’re talking about scheduling manufacturing lines and maximizing throughput even in highly volatile conditions. 

On other fronts, overstocking and understocking are some of the common problems plaguing supply chain management in several manufacturing processes. These conditions result in waste, reduced customer satisfaction, and ultimately, low profits. However, with the predictive capabilities of AI models, it becomes way easier for manufacturing companies to forecast their production capacities and customer demands through stocktaking. In doing this, AI models also factor in essential supply and demand indicators like pricing and seasonal demands to ensure that the manufacturing outfits know how much they need to produce as close to perfection as possible.

This combination of internal data—such as historical sales data—and external data—such as weather conditions, holidays, and emerging events—makes the predictions more accurate than humans could ever project. This is the secret to efficient supply chain management and demand forecasting. 

4. Task and Process Optimization 

Manufacturing assembly lines are key to the efficiency of manufacturing processes. The successive process of putting together the identical items that make a product already determines how efficient the manufacturing process will be. 

Human-developed manual processes may involve stationing certain individuals at different points along the production lines. However, this archaic approach doesn’t always yield the best results. In addition to the possibility of human errors, keeping people in redundant and repetitive processes may also waste scarce human resources. 

On the other hand, AI can help formulate new setups and analyze the performance of each process. AI algorithms can also help spot areas for improvement in existing manufacturing processes and suggest tweaks that can improve productivity. This also applies to equipment layout on the factory floor. 

Furthermore, AI also helps manage resources better by first taking charge of some of those routine processes we referenced earlier. This saves energy and helps direct the manufacturing firm toward its sustainable goals, thus cutting costs and downtime to improve output and optimize resource allocation. 

5. Employee Empowerment 

Although awareness surrounding the digitalization of manufacturing processes tries to reduce human input, the success of manufacturing firms still lies in the hands of humans. We just have to ensure that humans steer both machines and AI models in the right direction. The key to success, therefore, is finding the perfect way to allocate skilled workers where they are needed most and then empowering them to perform their functions as well as possible. 

So, how does AI ensure this? 

For starters, AI can help with workforce planning and management through insight-driven employee data analysis. 

Where do certain employees perform best in the manufacturing process? 

What do they need to perform excellently? 

What skillsets do they need to become better versions of themselves? 

AI can provide data-driven answers to these questions, allowing manufacturers to consider them and help employees put their best foot forward. For instance, AI-assisted visual detectors can help quality assurance specialists do their jobs better by making it easier to spot defects in products and machines. 

However, that’s not the only way AI empowers employees. 

Remember that AI helps with predictive maintenance by monitoring manufacturing equipment? It can also monitor and analyze data from sensors to detect potential hazards and ultimately enhance workplace safety. Furthermore, AI-assisted machines can help employees acquire the skills needed to cope with the increasing technological advancements in these evolving manufacturing roles. 

The possibilities are simply endless. 

6. Autonomous Mobile Robots and Cobots 

We hinted at them slightly earlier, but collaborative robots, or Cobots for short, are a standalone emerging use case of AI in manufacturing. These robots are designed to collaborate with human operators to perform specific tasks within a shared workspace. They’re basically intelligent assistants on the manufacturing floor who assist the employees on the ground with various tasks. 

Autonomous mobile robots are also slightly similar. However, they differ in that they can move and operate independently without human supervision. 

Consequently, manufacturing companies now have more hands in the manufacturing process with fewer employee costs. Major companies like Amazon are already deploying these AI agents and using them for warehouse management and order fulfillment. 

It’s such an interesting time in manufacturing!  

Below, we cite some actual real-world examples of these use cases and the solutions they provide in the different industries in which they have been deployed. 

Examples of AI in Manufacturing: Real-life Solutions in Use (Table) 

The following are some examples of AI in manufacturing: 

Debut Infotech’s X-Step Implementation Procedure for Integrating AI in Manufacturing

Your business can also integrate AI solutions for manufacturing processes, just like the examples in the table above. All you need to do is leverage the help of AI development services like Debut Infotech and our custom implementation procedure, as described below. 

The following are some vital steps to follow when implementing AI in manufacturing use cases:

1. Understanding Your Manufacturing Needs

Are you looking to optimize your supply chain or automate your machine maintenance processes?  

Maybe you’re looking to build AI-driven robots to assist workers on the factory floor. Depending on your specific manufacturing needs, the implementation procedure might have some subtle differences that’ll make all the change you need to drive growth. That’s why it is important to deeply understand your business objectives and technical requirements before venturing into AI development. 

2. Assessing Your Data Requirements

Your manufacturing needs also greatly shape your data requirements. While AI solutions built for predictive maintenance require sensor, historical, and operational data, AI solutions for building cobots might need more task-specific and environmental data. 

You want to make sure you get that right from the outset, as data largely determines the efficiency of these solutions. 

More importantly, you want to make sure the data is clean, well-structured, and ready for effective model training. 

3. Developing a Custom Strategy 

Our AI development companies expert often handle this task. They have a wealth of experience in choosing the right AI methodologies and technologies to bring your ideas to life. They then develop a custom AI strategy that aligns with your specific business context, chosen data characteristics, and expected results. The goal here is to choose a development approach that aligns with the factors determined earlier. 

4. Design and Development 

With all these factors considered, you can now start designing algorithms and building models tailored to your unique challenges. At this stage, it is important to prioritize robust, scalable, and efficient AI systems to guarantee both productivity and longevity. More importantly, you must also focus on systems that will integrate seamlessly with your existing technological infrastructure. 

5. Training and Optimization

After developing the custom AI tools, you can start preparing them for everyday use by training and optimizing them using the appropriate training data. This could be historical data relating to your manufacturing processes. Using this kind of data can enhance the accuracy and efficiency of the AI solutions you’re implementing in your manufacturing process. 

6. Deployment and Support 

Now, to the factory floor! 

You can now carefully deploy AI solutions to your manufacturing environment, ensuring seamless operation with existing machinery and employees. But it doesn’t just end there!

You must continue to monitor its functioning and perform necessary optimizations to maintain its efficacy and continuously adapt to evolving manufacturing needs. 

Conclusion 

The combination of AI and manufacturing presents an enormous opportunity for businesses and manufacturing firms to increase their productivity levels to the moon. With the help of AI, most of the pressing challenges and inefficiencies experienced in this sector can be reduced to the barest minimum. 

For instance, Siemens is using AI-powered Senseye predictive maintenance technology to reduce downtime and equipment failures. Audi, on the other hand, is reducing production costs using the Edge Cloud 4 Production Concept. Other applications and use cases include autonomous mobile vehicles, cobots, and supply chain management systems. 

If you would also like to experience these transformative effects, you should check out some of our implemented AI use cases to get an idea of how you should proceed. 

We’re here for you!  

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