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Challenges of AI in Manufacturing

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Automation has been a part of manufacturing for centuries. The first industrial revolution introduced steam machines that enabled mass production and an improvement in product quality. Moreover, automation reduced overall operating costs and kept improving quality for decades.

AI technology is the latest trend that uses data to improve efficiency and productivity, as well as product quality and employee safety. The adoption of AI technology found an application in most industries due to the many benefits it provides. However, implementing AI into existing processes comes with all kinds of challenges we’ll cover in this article.

1. Shortage of Data Scientists

Even though AI has been around since the late 50s, the lack of computing power meant that it was simply a concept for decades. However, as computers became more powerful, the need for AI solutions exploded in the past decade.

The sudden increase in demand for AI solutions led to a shortage of experienced workers. Data scientists and AI experts became extremely sought out, and since there is only a handful of them available, the shortage of professionals became a real problem. 

Professions such as ML engineers, data scientists, software developers, BI analysts, and others became very hard to find. Moreover, even when businesses found the people they needed, most of them couldn’t afford to hire them. Given that most AI projects require multiple teams and experts, it’s clear that AI development is available only to the largest companies.

The manufacturing sector is having the hardest time of all industries, as most data scientists and engineers see it as a monotonous practice. As a result, the sector is expected to see a massive workforce shortage in the next decade. The digital transformation in manufacturing is in full swing, but most small and medium businesses are having a hard time finding employees.

2. Technological Updates

Most manufacturing plants and businesses require the use of many different machines, tools, and systems in production. Some companies update their technologies regularly, while others still use old machinery and outdated software in production. 

 

There are no standards, which makes every manufacturing system unique and full of challenges. Before companies adopt advanced AI-powered manufacturing software, they have to hire engineers and data scientists to review existing technologies and find the best way to improve them through AI and ML.

Their job is to find compatible components, update frameworks, and connect existing devices using IoT technology. Of course, the process can take months to complete, and even then, the cost of introducing new protocols and technologies is often too much for some businesses. We expect this technology to become more affordable over time, but that won’t happen for another few years.

3. Data Quality And Management

 

ML systems and AI software read through endless amounts of data to identify issues and provide better solutions. With that said, the quality of the data they work with has a huge impact on results. Working with manufacturing data is particularly hard, as most systems run into errors or generate outdated data. 

The quality of the data fed to the ML system depends on all kinds of factors. Changes in temperature, operating conditions, and vibrations can all have a negative effect on data quality. Traditional manufacturing plants usually develop and produce products across multiple proprietary systems, which means that the data is distributed across multiple databases and in different formats. That alone makes data analytics way more complex, and data scientists have to find new ways of handling information.

Advanced AI solutions can help automate AI data preparation to increase data quality. However, the process requires a lot of experts and creative ideas in some cases. The goal here is to create a unified data storage through data filtering. Unified data is key to training AI models, so there’s a lot of preparation involved.

4. Edge Testing

Edge computing proved to be very useful for processing and filtering local data to increase overall quality. It’s a practice that reduces the amount of data used by ML systems to help save storage space. It’s an important practice for IoT systems and other channels since they generate massive amounts of data. Too much data leads to storage issues and many other problems down the line.

 

Edge technologies can monitor and control each manufacturing process in real-time. Instead of using historical data to improve business processes, AI and ML now use real-time data to determine what actions to take to improve efficiency. This approach leads to the development of predictive models using edge devices such as local gateways, servers, and machines. Once set up, the smart system can improve all manufacturing processes.

5. Making Decisions On The Go

The ability to make business decisions on the go is redefining manufacturing practices. The goal is to improve product quality and meet customers’ needs. Some decisions have to be made in seconds to prevent outages, product defects, and financial losses. 

Of course, none of this would be possible without an AI solution. Its ability to make decisions in milliseconds can save companies millions of dollars. However, the system has to feed the software with high-quality data in real-time to work. That is the only way AI can find issues in real-time and notify manufacturers before the damage is done.

6. Transparency

It’s hard to trust technologies that require a complete transformation of existing systems. AI adoption usually includes many unforeseen challenges. Without the right people and an excellent understanding of AI systems, the transition can be difficult and expensive.

That difficulty is what makes business owners hesitate to adopt AI. But it’s not all bad. Advanced AI models today are built on transparent data science practices that provide a detailed overview of all processes. That way, the people involved can track data inputs and see how the ML model processes information. 

Understanding how prediction models work and why they work in a certain way can help increase trust in AI models and help drive businesses forward. 

Conclusion

The constant development of new AI solutions made the entire process of manufacturing digitalization much more manageable. However, some challenges still exist, but the benefits make the transition well-worth the effort.

With the right data scientists and a powerful AI solution, your current manufacturing systems can reach a new level of automation. You will be able to produce better products, increase productivity, and streamline your entire operation. AI is the future, and the sooner you jump on board, the bigger the head start you will have compared to your competition.

Author bio

Travis Dillard is a business consultant and an organizational psychologist based in Arlington, Texas. Passionate about marketing, social networks, and business in general. In his spare time, he writes a lot about new business strategies and digital marketing for DigitalStrategyOne.

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