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Artificial Intelligence has become somewhat of a commodity for many industries, and manufacturing is no exception. This technology is steadily overtaking the industry, and numerous reports confirm this growth with impressive numbers. For instance, the market size of AI in manufacturing is projected to reach $68.36 billion by 2032, compared to $5.07 billion in 2023.
So how exactly does AI in manufacturing help business owners gain a competitive advantage and improve their processes and should we worry that AI will replace people? This article strives to answer these and other questions.
The definition and state of AI in manufacturing
AI in manufacturing implies the use of Artificial Intelligence technology and its subsets (robotic process automation, machine learning, etc.) to facilitate, optimize, and automate common manufacturing processes. Its use helps resolve the biggest manufacturing challenges (like poor inventory management or labor shortages) and helps companies reduce operating costs through smarter, data-based strategic planning.
Numerous reports confirm the popularity of AI in manufacturing sector:
- Over 40% of North American manufacturers adopt AI;
- The AI in manufacturing market is projected to see a GAGR of 3.3% for the 2024 – 2030 period;
- In 2023, 35% of manufacturers already used AI.
Based on these and other reports, it’s safe to assume that AI will only become more prevalent in the industry and that more business owners will implement it in their current processes. So now it’s time to discuss the biggest benefits that this technology brings to the manufacturing industry specifically.
The benefits of using AI for manufacturing
Business owners love Artificial Intelligence across multiple industries, but some adopt it simply because it’s trending. In these cases, AI won’t bring much benefit (instead, it might cause even more financial losses) since it was not implemented for a specific goal. This is why it is important to understand how exactly AI can help your business and what benefits you can expect from its adoption.
Increased efficiency
There are several challenges in the manufacturing industry that may impact the level of efficiency: too many mundane tasks, complex calculations, lack of data-based decision-making, irregular maintenance checks, etc. AI can help with all these issues, thus significantly boosting productivity and allowing employees to focus on more strategic and complex tasks, aimed at business growth and development.
By automating mundane processes and supporting every strategic decision with the data, manufacturers can greatly reduce downtime and empower their employees. And happy and motivated employees equal better performance and increased productivity, so it’s a win-win.
Improved quality
Quality control is a must for any manufacturing business but manual quality checks leave room for defects and errors. Automation of quality control significantly minimizes (and even eliminates) a chance of defects in a production line and ultimately leads to better quality of manufactured goods. As well, automated quality control results in improved customer satisfaction and reduced number of returned products, which is another challenge that manufacturers face.
Enhanced security and safety
Maintaining a sufficient safety level in the manufacturing environment is crucial. The implementation of AI can greatly aid with this in the following ways:
- Real-time monitoring of environment
- Detection of anomalies
- Automation of hazardous or dangerous tasks
- Employees’ health monitoring
As you can see, there are many ways how AI can help in improving safety at your manufacturing facility. The ultimate goal here is to detect the biggest challenge and research how exactly you should adopt AI to resolve it.
Better decision-making
Data should be at the heart of any decision-making process so that you don’t rely on guesswork and are guided by real-time accurate insights. However, when it comes to Big Data, standard data processing methods and techniques are not enough. This is where AI steps in. Artificial Intelligence technology is the primary option for Big Data processing as it is capable of analyzing massive amounts of data in mere minutes. As well, AI-powered tools are designed to generate user-friendly and informative reports tailored to specific requests of each organization.
Top use cases of AI in manufacturing industry
How is AI used in manufacturing?
In general, AI can be applied to almost any process within a manufacturing facility. To give you a more specific idea of its use, we will discuss the most prominent use cases of deploying Artificial Intelligence in manufacturing.
Cobots for assistance
Cobots, or collaborative robots, are a new generation of industrial robots designed specifically to work alongside people. Their main features are increased safety and flexibility and lower initial investment. Needless to say, the adoption of cobots has become a growing trend in the manufacturing industry.
Examples of tasks performed by cobots in the production environment are:
- Welding and gluing product parts
- Picking and packaging products
- Material transport
- Quality control
As you can see, most of these tasks are repetitive and simple and often do not require human assistance or control. Considering that most industrial cobots are affordable, their deployment can greatly increase the efficiency of your operations and free employees from mundane processes that eat up too much time.
Predictive maintenance
One more important use of AI in manufacturing is predictive maintenance. Since the maintenance and repair costs are always high, it is more beneficial for companies to reduce them through proactive and timely maintenance. Artificial intelligence can help detect any issues or defects at early stages and also helps prevent various breakdowns through computer vision and real-time data processing.
Another way AI helps with predictive maintenance is through digital twinning, which means creating a real-time digital replica of a physical facility. Companies can interact with digital twins, apply various scenarios to them, and analyze how a facility would behave in different situations. Digital twins can also highlight existing or potential problems within a facility and suggest ways of workflow optimization.
Automation of quality checks
We’ve already mentioned that one of AI’s benefits in manufacturing is improved product quality. It can be achieved through automated quality control, which has become a game changer for many manufacturing companies.
By using computer vision, AI models analyze both equipment and products, can instantly spot defects or damage and notify employees immediately. This reduces a chance for human error and greatly aids in defining improvement areas.
Automation of supply chain management
Supply chain management is a big and essential part of any manufacturing business and includes several stages:
- Planning
- Sourcing
- Manufacturing
- Logistics
- Returning
Each stage encompasses its own specific processes and challenges. Machine learning in manufacturing helps streamline and organize these processes and adds transparency to the whole supply chain in the following ways:
- ML-based demand forecasting and more accurate stocking
- Logistics optimization through smart route planning
- Inventory automation and optimization
- Automation of warehouse operations
- Smart quality checks and instant defect detection
And obviously, these are not all examples. AI makes supply chain management more autonomous, transparent, and organized, enabling business owners to instantly access any needed information and seamlessly share it across departments.
Development of new products
Though the creation of new products with AI is slightly overlooked, it is becoming increasingly relevant among manufacturers. While some may argue that the creation of something innovative is a purely creative process that machines are not capable of, AI, in fact, can greatly help with the idea – here is how.
First, AI can perform extensive market research and collect all essential data (like modern trends, user demographics, competitors’ analysis, etc.) and present it in a clear report. Employees can then use this report to come up with an idea of a competitive and user-centric product. In this way, AI greatly saves time and effort on data collection and analysis.
Better order management
One more area of manufacturing where AI is widely used is order management. This technology helps with several main aspects, such as:
- Smart analysis of customers’ preferences and personalized recommendations
- Smart inventory management
- Detection of fraudulent activity
- Dynamic pricing changes
Overall, AI helps make the order management process more data-driven and user-centric while also eliminating the biggest bottlenecks that are common for the process.
The main concerns and challenges of using AI in manufacturing
Despite the benefits that AI can bring to manufacturers, many still hesitate to adopt it. In fact, only 10% of surveyed companies from the list of 3,000 (a report by MITSloan Management Review) said they were gaining financial value from AI adoption. So why do business owners see AI as more of a financial loss and what stops them from adopting it?
Lack of big data sets
It is common knowledge that for accurate results, AI models need to be trained on massive data sets. Hence, if your company lacks them, AI won’t provide the expected results and naturally will cause more financial losses than benefits.
In order for manufacturing AI to help your business grow, you need to prepare large data sets and ensure that the data is in the suitable format. On top of that, the data pools have to be maintained and updated continuously so the data fed to AI is always relevant. One of the most effective ways to achieve this is to use a MES (manufacturing execution system) that automatically sorts various data and collects it from different sources.
Lack of expertise
AI is a very powerful tool but it calls for people with strong expertise in AI and data science to handle it properly and to configure your AI model exactly as you need it. Hence, manufacturers need to be ready for the fact that the adoption of AI comes together with hiring AI and data science experts who will not only create the model but also train and configure it.
Integration with existing software
It is a common case that a manufacturing facility has legacy software that hasn’t been optimized in a long time. Obviously, when you implement AI, it needs to be integrated with your existing digital environment – this is where problems start.
If your software is outdated and can’t support AI implementation, you will have to update and optimize it. This, in turn, leads to additional costs, which some business owners may not be ready to face.
How to implement AI in a painless manner: a checklist
Let’s wrap up this article with actionable recommendations on implementing Artificial Intelligence in your business. Though each company will require an individual plan, below we list several general steps that you can customize according to your business goals.
Define the problem and the value that you expect from AI
The first step that some business owners overlook is the definition of the existing issue, definition of the value expected from the AI implementation, and definition of KPIs. This step is essential as it helps you understand whether you really need AI or can use another, more affordable solution. Also, during this step you set clear KPIs that you will use to monitor the efficiency of the AI solution.
Hire or consult experts
As already mentioned, the implementation of AI for manufacturing calls for experienced professionals who will properly design and set up your AI model. Our best recommendation is to hire outsourced ML and data science engineers from a reliable provider. In this way, outsourced specialists will be more cost-affordable than assembling an in-house team and they will bring in their vast knowledge and skills. Another option is to simply request a consultation so an expert helps you plan your AI implementation strategy.
Gather and prepare the data
Either independently or with the help of external experts, you will need to collect and prepare the data that you will use to train the AI model. If it sounds too complex, don’t worry: today, there are plenty of specialized tools available and designed specifically for collecting and processing the data from multiple sources.
Prepare the environment
Before adding AI to your arsenal of tools, make sure that your current digital environment is ready for this integration. Hence, run a quick review of your current digital assets and optimize and update any necessary apps and services so they don’t disrupt the work of the AI model but instead, work in parallel with it.
Implement and observe
Once everything is ready, you can implement your AI model. An important thing to remember here is that after its implementation, you need to constantly monitor not only the model’s performance but also the performance of the whole system. Through continuous monitoring and testing, you will be able to timely detect bugs and issues and resolve them before they cause significant damage.
Expert Opinion
AI in manufacturing is progressing relatively slowly compared to other industries such as entertainment, finance, agriculture, or healthcare. This can be observed from the demand for AI solutions; manufacturing companies are rarely clients. In my opinion, there are two main reasons for this:
First, manufacturing requires significant investments in production equipment. Consequently, companies are more conservative in investing in AI solutions because they have a smaller budget allocated for such innovations. Second, manufacturing requires precise estimates, which AI models often fail to provide due to their probabilistic nature and tendency to hallucinate.
However, the trend is that AI is becoming more precise, and we are seeing an increasing number of domain-oriented AI solutions that are both more precise and reliable. Therefore, I believe that AI in manufacturing will become increasingly popular in the future.
I am already familiar with two promising AI solutions for manufacturing:
1) AI for creating 3D models of products from images. Having experience in making 3D models, I know it is a time-consuming process, and AI can help make it faster and more precise.
2) AI for predicting overheating during the manufacturing process. This solution can improve safety during manufacturing.
These two examples demonstrate that AI can be very useful in various aspects of manufacturing. Often, the only limit is the imagination of developers and startup founders.
Data Scientist at SoftTeco
Roman Kyrychenko
Summing up
AI in manufacturing industry is a powerful tool that can significantly transform one’s business and make it more adaptable to modern market demands. But as any other software solution, AI calls for a high level of expertise – otherwise, it won’t deliver expected results and will most probably cause financial losses. Hence, if you consider using AI in manufacturing, we highly recommend reaching out to a knowledgeable service provider to guide you through the process and help with the development and setup of the AI model.
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