What are the current challenges the manufacturing industry faces? A lack of experienced engineers and technicians in the field is just one example, but, unfortunately, there are several other challenges. Thankfully, leveraging artificial intelligence (AI) has helped many manufacturing organizations address and solve these problems.
Current challenges faced by the manufacturing industry
So, what are the challenges faced by the manufacturing industry today? Below find some of the main issues manufacturers are working to address:
- Labor shortage – lack of experienced engineers and technicians, especially on-site
- Increasing demands for product quality improvement and cost reduction
- Shorter product life cycle
- Intensifying competition due to the globalization of the business environment
- Diversification and internationalization of employees
Within this blog, we’ll take a closer look at each and how AI is helping to solve these challenges.
Lack of experienced engineers and technicians on site
It’s no secret that the skills required for product design and development are difficult to learn and become an expert in. Similarly, the skills to perform inspections, operation and maintenance of manufacturing equipment, and the assembly of products safely, quickly, and with high accuracy cannot be acquired overnight. Talented engineers and technicians with these skills are entering a period of mass retirement, and because of this, the labor shortage is becoming serious. Retirement extension and re-employment systems are being prepared, but, passing on these techniques and skills to the next generation is an important issue.
Increasing demand for improved product quality and reduced costs
This increasing demand for improved product quality and reduced costs can be said to be an “eternal issue” for the manufacturing industry. Most of the traditional activities, such as small group activities and cost reduction activities, are done to address this challenge. However, with the rapid changes in the business environment over the recent years, rather than solving this problem, the hurdles are increasing. Gone are the days when a new product is released and sold efficiently. It is essential to accurately analyze and understand the purchasing behaviors of consumers at the product planning stage. In other words, there is a demand to bring high-quality, low-cost products that meet consumer needs to the market.
Shorter product life cycle
The life cycle of bringing a product to the market has shortened, which means the production of parts must be mass manufactured to see a quick return on investment (ROI). Because of this, the optimization of the entire production process is crucial.
Intensifying competition due to the globalization of the business environment
In today’s manufacturing industry, overseas production has become the norm. We are now in an era where products cannot only be sold in developed countries, and the target markets for the manufacturing industry are expanding to include more diverse regions than ever before, including emerging countries. Under these circumstances, it is necessary to develop products that meet the market needs of each region and to conduct marketing activities in a more detailed manner.
Here’s an example that proves just how important it is to plan products that match the region. Not too long ago, when the Japanese economy was suffering from the “lost two decades,” two major LCD TV manufacturers were vying for market share in India. One company’s product was cheaper and had the top share. The other company did thorough research on the colors of TV screens Indian consumers preferred and discovered that they tend to prefer bright reds over plain reds. Fortunately, the company’s product was able to change the color balance of the screen with only a slight modification of the software, so when the modification was made and the new product was introduced to the market, the reputation was “a little expensive, but the color is good.” One year later, the company succeeded in taking the top market share.
The sense of color is subtly different for everyone, and it also differs by ethnicity and region. For example, in China, the word “red” means a color close to vermillion, and those living in Europe and Japan have different preferred color temperatures, meaning popular lighting colors are different. These factors must be taken into consideration when planning products that require image quality, such as LCD TVs and digital cameras.
Diversification and internationalization of employees
The manufacturing industry is global. This means that employees working for these global manufacturing companies are geographically spread across the world living in different countries and coming from very different backgrounds. Production activities must be carried out despite differences in values, thinking, and languages. Because of this, manufacturers need to overcome these language and cultural barriers to ensure employees and teams work well together.
Solving manufacturing issues with AI
Although some of these challenges appear daunting, artificial intelligence technology has been proven to – or will in the future – help mitigate the problems listed above. Let’s take a look at how AI can be leveraged in the manufacturing industry.
Labor shortage and AI
Manufacturing organizations are addressing the labor shortage by transferring the knowledge and skillset of talented engineers and technicians with the help of AI. For example, during the learning process of inspections, an experienced inspector is shown a variety of inspection screens and asked to judge whether the product is good or bad. The results are then uploaded to the edge AI, built with Edge Computing, to perform the actual product inspections. This puts less of a burden on the older skilled engineers and technicians and provides an advantage to the manufacturing company as they now have this critical judgment saved via AI.
Improving quality, reducing costs, and AI
It may seem simple, but to improve quality, it is essential to not produce defective products. To prevent this, it is important to enhance equipment maintenance as well as inspection work. By arming manufacturing equipment with self-check functions leveraging artificial intelligence, failures can be discovered and predicted automatically. This technology has been rapidly progressing over the last few years. As a result, equipment maintenance can be carried out by fewer people, reducing labor costs. Furthermore, real-time performance can be achieved by using Edge Computing.
Shorter market cycles and AI
To respond to the shortening of the market life cycle, it is necessary to reduce the production lead time. Production efficiencies largely depend on the lead time of parts, making it essential to manage the delivery of those parts. This is often considered a combinatorial optimization problem, which is known as the act of trying to find the combination of variables that optimizes a value from among many options under various constraints. Without AI, this can be extremely challenging to determine, which is why many manufacturing organizations are leveraging artificial intelligence to help.
Globalization of the business environment and AI
In the LCD manufacturer example mentioned previously, it is unclear whether big data analysis was used. But, in similar instances, having access to that data can make a huge difference. Currently, AI is being leveraged by many manufacturing organizations who are running big data analysis to support fine-tuned marketing in various countries – including developing countries – proving that big data analysis and AI enables smarter marketing activities.
Diversification/internationalization of employees and AI
With AI, speech recognition technology and automatic translation tools significantly help manufacturers support essential communication between colleagues, which is extremely helpful in enabling teams to collaborate efficiently. This is especially important as many manufacturing employees are geographically spread across the globe.
AI in other fields
AI is not just leveraged across the manufacturing industry. Other applications include leveraging AI for bioinformatics for drug discovery, material informatics, alloy design, and material discovery. There is also an example of using AI for optimized tire shape design.
AI is permeating the manufacturing industry
Above we have introduced several examples of AI applications in the manufacturing industry. AI is a highly versatile technology, and if applied well, it can produce great results. There may be other situations where the above may apply.
For manufacturers, there are many processes spanning from product planning to development, manufacturing, and sales. As each process is connected – albeit in a complicated fashion – it is not easy to respond quickly to rapid changes in the recent business environment. Artificial intelligence is one tool that is helping alleviate challenges within the manufacturing industry, especially when run via Edge Computing.
