Three examples of how AI might be used in the industrial sector

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Three examples of how AI might be used in the industrial sector A number of problems, including securing human resources and passing on technology, have emerged in the manufacturing industry recently as a result of the acceleration of the dropping birthrate and ageing population. Attention is being drawn to the application of AI as a remedy for these problems facing the manufacturing sector. The adoption of AI is becoming more and more crucial as DX initiatives develop. Therefore, we will outline the advantages of implementing AI in the manufacturing industry and provide some examples in this article.

issues facing the manufacturing sector

Three examples of how AI might be used in the industrial sector The “manufacturing powerhouse Japan” was built on the manufacturing sector of Japan’s economy, which supported rapid economic expansion. The manufacturing sector has, however, suffered a number of difficulties recently, and some even claim that there are indications of a decrease. Let’s first define the difficulties the manufacturing sector is facing.

Due to the ageing population and dropping birthrate, there is a shortage of human resources.

There are worries about a future decline in the working population in modern Japan due to the population’s ageing and dropping birthrate. The working-age population, including those between the ages of 15 and 65, reached its peak in the 1990s and has been declining ever since, according to the Ministry of Economy, Trade, and Industry’s “Structural Changes in the Economy and Society and Policy Issues Until 2050.” It is still going on. By 2050, the proportion of people who are working age, which peaked at about 70%, is predicted to be in the low 50% range.

It is anticipated that competition for scarce human resources will increase in the future as the working population declines throughout Japanese society. Young people, who make up the majority of workers, are unlikely to be hired because the stigma associated with the “3Ks” (hard, dirty, and dangerous) is still very much alive, especially in the manufacturing sector.

stagnation in technological progress

High-tech manufacturing in Japan has contributed to the country’s status as a “manufacturing superpower.” Particularly in the manufacturing sector, Japan has cutting-edge technological skills.

The tide of ageing is now sweeping even among the engineers and artisans who have supported the high level of technology. The aforementioned factors make it difficult to find the next generation of human resources, and the shadow of technological stagnation hangs over its advancement. First of all, it is challenging to transfer the skills in many situations, and many technologies in the manufacturing sector rely on the intuition and experience of trained craftsmen.

IT not being used enough

Japan uses IT less frequently than developed nations and has introduced ICT (information and communication technology) at a rate that is 10% to 20% lower than that of the US and Germany. One of the causes of the decline of the Japanese manufacturing industry is that small and medium-sized businesses’ adoption of IT has not advanced smoothly.

SMEs must aggressively adopt cutting-edge technology like robots, IoT, and AI in order to increase productivity on a country-by-country basis. Many SMEs, however, lack the financial resources to invest in machinery and are wary of implementing AI since they take pride in their own “technology” and human experience.

Benefits of implementing AI in the manufacturing sector

What kind of changes will the manufacturing sector experience as a result of the actual implementation of AI? The advantages that the manufacturing sector can gain from the introduction of AI are described in the sections that follow.

lowering labour expenses and properly allocating resources for people

AI’s introduction allows for the automation of some tasks and the saving of labour in others. In addition, AI leverages historical data to support ideal inventory levels for inventory management, which is crucial for the industrial sector. This results in ideal staffing and cost savings because it frees up human resources to concentrate on other crucial tasks.

aggregation and automatic manufacturing process data identification

It is vital to adapt the production system in the event of high-mix, low-volume manufacturing based on predictions and assessments made using personal experience. Individualized tasks are common for tasks that depend on certain people, which occasionally creates a situation where you can’t handle it unless you are that person. Such tasks might be manageable for AI if it gathers a lot of data.

Making sophisticated predictions, such as “which items are required in what quantities,” is feasible by gathering data on the production process and integrating AI with machine learning and deep learning. Additionally, even for high-mix, low-volume production, you will be able to create flexible strategies.

In addition, by gathering a lot of image data and performing automatic identification activities, AI might be able to replace even professional ways like visually inspecting good products.

A rise in productivity

The use of AI will increase productivity by requiring less work and time to complete diverse activities.

Technical capabilities and production planning in the manufacturing sector frequently are predicated on individual skills. Particularly in small and medium-sized businesses, this trend is apparent. Based on specific differences in product specifications and prior data, AI can be used to create an ideal production plan that takes cost reduction into account.

In addition, product inspections, which are crucial in the industrial sector, are another area where AI-based diagnostic imaging technology exhibits its strengths. Even intricate inspections can be processed quickly, and checks can be finished right away. It eliminates human errors that can happen visually, such as errors in counting the number of units and the product number, and it maintains constant quality.

Case Studies and Use Scenarios for AI in the Manufacturing Sector

Here, we will give a prime example of a manufacturing sector that benefited from the adoption of AI. Make use of it as a guide to help you grow your own company.

Work on inspections is automated

The “ReNom” AI development platform is offered by Grid Co., Ltd. The business wants to make it simple for anyone to create artificial intelligence models and freely mix complex algorithms. ReNom will make it possible to address on-site issues with images and numerical data while minimising work that can only be completed by particular individuals.

Manufacturing ReNom was actually introduced by Company A, and they were successful in automating difficult inspection procedures. Up until this point, inspectors from Company A have visually inspected the assembled parts as they were being put together. However, creating an AI-based automatic inspection system was difficult in order to address the anticipated future manpower shortage.

As a result, Company A introduces ReNom to automate the assembly process’s last inspection. Using an AI model, we created an image judgement system and automated inspection tasks. This has reduced the need for labour and work, and by creating the AI model internally, we were able to further cut back on outsourcing expenses.

Encourage sane behaviour

With the help of NEC, JFE Steel, a significant local steel producer, has been successful in creating safe behaviour support technology. The fundamental tenet of JFE Steel is “safety comes first,” and with the recent rise in the proportion of young employees at steelworks, guaranteeing safety is a concern more than ever.

Although AI image recognition technology has been used in the past to identify people and ensure safety, it was thought to be challenging to implement at steelworks. This is due to the fact that people are hard to spot in steelworks due to the various lighting conditions depending on the location, the wide variety of installed equipment, and the fact that employees work in different postures.

In order to learn from a vast number of worker photos, JFE Steel and NEC employed deep learning. Human detection accuracy is considerably increased by deep learning, which makes it possible to learn and make decisions similarly to humans. We have finished a safe behaviour support system that issues a warning when a worker enters a restricted area and immediately stops the production line by enabling high-precision human detection using AI.

application of a production planning system

A significant domestic beverage producer named Suntory is collaborating with Hitachi to create an AI-based system for production planning.

Given that consumer trends and climatic change have a significant impact on beverage sales, beverage producers must develop thorough production plans and be able to adapt rapidly. Veteran workers at Suntory have already created and modified production strategies based on empirical information. Planning work had the issue of depending on the individual in addition to requiring a high level of skill and a great deal of time.

Additionally, Suntory had been planning on an area-by-area basis up until that point, and while it had been successful in producing the ideal number of units for each area, it had not been successful in creating an ideal production plan that efficiently made use of all of its production resources.

Suntory will therefore create an AI-based solution for production planning. Production plans may be created and modified quickly thanks to the automatic extraction of inventory status such as shortages, surpluses, and shortages. Production planning tasks that previously required many personnel to labour an average of 40 hours per week were shortened to only one hour.

What is required to introduce AI?

The typical procedure leading up to the development of AI is as follows.

  1. assembling an AI team
  2. issue identification
  3. Data gathering and analysis as necessary
  4. Repeating machine learning and deep learning will automate and streamline activities.

The first step in introducing AI is to choose the appropriate staff and teams. The team needs members who can collaborate with the industry in addition to AI experts.

The next step is to define and communicate your vision, such as “what and how to improve using AI,” after identifying the problems your business is currently experiencing. The process of issue clarification is crucial because if the vision is not fixed, the introduction of AI may stall altogether.

Then, we gather and examine the data required to address the issue, create an AI model, and enhance the work. The data in the production becomes more crucial, especially in the manufacturing sector. In essence, steps 2 through 4 above are repeated to increase AI accuracy and gradually enhance operations.

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