Today’s manufacturing sector is faced with various challenges such as labor shortages and intensifying international competition. Artificial intelligence is drawing attention as a technology that can solve these problems, and more and more companies are introducing and using it. This time, we will explain how artificial intelligence can be used in the manufacturing sector with examples and points to be considered when introducing it.
What is AI?
AI (Artificial Intelligence) is a technology in which software artificially reproduces some of the intellectual behaviors of humans. Processing is carried out automatically and flexibly by following a model learned using a method called deep learning.
Classification of Artificial Intelligence |
What can be achieved with artificial intelligence |
| Learning/Analysis | Artificial intelligence can make predictions and decisions by learning and analyzing large amounts of data (big data). Operations that used to take a very long time using traditional technology or required human expertise can now be done quickly and easily with artificial intelligence. Another advantage of AI is that it enables the use of big data that has not been fully utilized in real business in the past. |
| Automatic Response/Control AI, | AI, together with the learning and analysis systems mentioned above, can respond and control automatically. For example, AI is used in real business environments, such as automating query response tasks in call centers and automating the operation of equipment that requires the knowledge of skilled engineers. |
What can be achieved with artificial intelligence can be generally divided into two categories:
Artificial intelligence can make predictions and decisions by learning and analyzing large amounts of data (big data). Operations that used to take a very long time using traditional technology or required human expertise can now be done quickly and easily with artificial intelligence. Another advantage of AI is that it enables the use of big data that has not been fully utilized in real business in the past.
Automatic Response/Control AI, together with the learning and analysis systems mentioned above, can respond and control automatically. For example, AI is used in real business environments, such as automating query response tasks in call centers and automating the operation of equipment that requires the knowledge of skilled engineers.
Production challenges that can be solved with AI.
We will reveal the problems that can be solved by introducing and using AI in the manufacturing sector.
Eliminating the labor shortage
The labor shortage due to the decreasing birth rate and increasing population is also affecting the manufacturing sector. Many companies are not content with just managing their current operations and are overwhelmed.
However, with the introduction of AI, it is possible to automate and improve the performance of tasks that have previously only been performed by humans. If you can do your job with fewer people, you have more flexibility to undertake new initiatives such as DX and smart factories that will strengthen your competitiveness.
Improved productivity
With the introduction and use of AI, it is possible to automate various tasks that were previously performed by humans. Companies can greatly increase their productivity by delegating tasks that can be done to AI and allowing employees to focus on high-value tasks that only humans can do.
In addition, AI can process large amounts of data that humans cannot handle. Further productivity gains can be expected by obtaining optimal production plans and preventing equipment problems through highly accurate demand forecasting and failure prediction.
Improving quality
The quality requirements of the manufacturing industry are increasing every year, and quality control in production facilities is becoming more important than ever. However, the truth is that many problems with quality control rely on human hands, such as human error in visual inspection and differences in inspection accuracy.
AI is free from human error and variability and always performs visual inspections with consistent accuracy. In addition, by learning and analyzing large amounts of data, it is possible to identify the causes of defects and predict quality. Thus, AI helps to prevent defects and leaks, thus improving quality.
Talent inheritance
Due to labor shortages and other factors, developing the skills of skilled engineers has become a problem throughout the manufacturing sector. In addition, non-production tasks such as production planning and equipment maintenance are increasingly dependent on individual workers, and many companies are concerned about the inability to collect and share information.
AI can learn technology and knowledge from the movement of talented engineers and optimize production planning and equipment maintenance based on historical data trends. By making good use of AI, we can solve the problem of skill inheritance.
Reducing the burden on workers
In manufacturing facilities, there are many tasks that put a heavy burden on workers, such as moving heavy objects and simple tasks that take long hours. By transferring these tasks to machines or AI-equipped robots, the burden on workers can be reduced. Moreover, we can expect the effects of preventing accidents by allowing AI to take over dangerous tasks.
Current Status of AI Introduction in Manufacturing
According to a research report conducted by Google Cloud in seven countries, the percentage of manufacturing companies using AI in their daily operations is highest in Italy (80%), Germany (79%), France (71%), and the United Kingdom (66%), the United States (64%), Japan (50%), and South Korea (39%), indicating that Japan has a lower rate of AI adoption in the manufacturing industry than other developed countries. The study targeted companies with 500 or more employees, so AI adoption is likely to be even lower in small and medium-sized enterprises (SMEs) with fewer employees.
One reason why AI has not advanced far in the manufacturing sector is the lack of knowledge and human resources to address AI. In fact, according to the 2022 Manufacturing White Paper , almost half of the companies cited “lack of knowledge in using digital technology” and “challenges in using digital technology” as a reason for the manufacturing sector to adopt digital technology. “There is a lack of human resources that can play a significant role.”
However, in recent years, AI solutions that do not require advanced expertise have become available, and the barriers to introducing and using AI are decreasing; therefore, small and medium-sized organizations are likely to rapidly adopt AI.
Examples of the use of AI in the manufacturing industry.
Presenting examples of the use of AI in the manufacturing industry.
Productivity improvement/quality prediction
Optimizing production for manufacturing facilities is one of the main efforts to increase productivity and profit margins. However, even when actual efforts are made to improve production, there are many cases where the process ends up recording defective products or where the cause of the error cannot be fully identified.
Artificial intelligence can learn and analyze large amounts of data, determine the cause of errors based on data trends, and predict quality. For example, in the manufacturing industry that combines products such as cosmetics, food, pharmaceuticals, etc., factors that have a major impact on quality are analyzed by analyzing data such as quality records, raw material information, composition information, etc. and equipment operating information. Detect defective products. AI is used for purposes such as predicting possible situations. Based on the information obtained through artificial intelligence, production can be optimized by avoiding conditions that negatively affect quality during production.
Failure prediction/anomaly detection
In production facilities, equipment maintenance is carried out to prevent production lines from stopping due to equipment failures or anomalies. However, equipment maintenance requires regular inspection and maintenance, which puts a heavy burden on the personnel in charge.
Artificial intelligence can predict equipment failures and detect anomalies from data captured by sensors, such as failure records, operating records, vibrations, sounds, current values, and AE waves (elastic waves). By predicting which equipment is likely to fail and when, inspection and maintenance can be performed with minimal effort, and the equipment maintenance burden can be greatly reduced.
Demand forecasting
For companies that manufacture to stock, demand forecasting based on past sales results is a particularly important task. However, customer needs and consumption trends have changed dramatically in recent years, making demand forecasting difficult, leading to problems such as deterioration in forecast accuracy and uniqueness.
Artificial intelligence can perform highly accurate demand forecasts from data such as past sales results, orders, inventory, order information, and product information. By forecasting demand from different perspectives, such as product and customer, and making production and order plans based on the results, you can reduce overproduction and lost opportunities. Demand forecasting using Artificial intelligence is particularly effective in industries such as food production, where there are many factors that affect demand.
Information Sharing
Artificial intelligence is also used to collect and share information within the company. For example, if your company’s design standards, past performance, and experienced personnel information are stored in an AI chatbot database, each responsible person will be able to optimize the efficiency of each employee as needed.
In addition, by using an Artificial intelligence chatbot on your company’s website and automating various customer queries, customers can get the information they need faster. This not only reduces the number of people needed to answer questions but also has the advantage of eliminating individual differences in response content and response methods.
Quality control
Many visual inspections are currently performed in production facilities, and human errors and differences in inspection accuracy are a problem. In some cases, automation has been implemented using image inspection equipment, but in most cases, it is only possible to distinguish black and white, and the disadvantage is that it is not as flexible as humans in decision-making.
Artificial intelligence can generate image decision models from a large number of sample images and perform automatic visual inspection. Unlike traditional image inspection equipment, it can also reproduce uncertain judgments made by humans, so it can also be applied to visual inspection, such as in the food manufacturing industry, etc., where individual differences are large. In fact, AI-based image evaluation technology is used to detect scratches, foreign objects, and discoloration in various visual inspections.
AI solutions for the manufacturing sector
Otsuka Shokai offers AI solutions that help automate and streamline various operations in the manufacturing industry.
Point Data AI Analysis Service
“dotData AI Analysis Service” analyzes large amounts of data using AI, makes predictions and decisions based on data trends, and provides small and medium-sized business consultants based on the analysis results. “Point Data” used in this service is an advanced data analysis tool that automates the most difficult data science processes using Artificial intelligence and allows you to get analysis results quickly without the need for complex data processing. You can start making improvements faster by using it to predict quality, failures, demand, etc.
The analysis and predictions made using “Point Data” are performed by Otsuka Shukai’s data scientists, so users can get the latest Artificial intelligence analysis results by simply providing data. We also provide diagnostic services to identify problems, check the necessary data for analysis, and recommend systems and IoT for data collection, so even users who are new to AI can use our services with confidence.
Tayoreru AI chatbot service
“Tayoreru Artificial intelligence Chatbot Service” is a service that uses the latest AI that excels in natural language processing. AI chatbots can interpret the natural expressions people use in conversations and provide appropriate responses.
When deploying a chatbot, it is important to create training data tailored to the purpose of use and allow the AI to learn from it. With Tayoreru AI chatbot service, in addition to entering the management screen and creating a chatbot, you can also benefit from the knowledge of Otsuka Shokai, which has actually used nearly 80 chatbots. It also has comprehensive maintenance functions and usage log management functions after the start of operations, so you can optimize it for users to use while going through the PDCA cycle.
MMEye / MMEye Box
“MMEye / MMEye Box” is an AI image evaluation service that automates visual inspection and improves inspection quality in themanufacturing sector. AI image evaluation can be performed in real time using end terminals on the production floor. Using AI technology and unique preprocessing technology, it is possible to make complex decisions with high accuracy and make uncertain decisions about products with individual differences, just like humans.
In addition, automatic image generation technology can automatically generate sample images of defective products for learning purposes, eliminating the need to collect large amounts of data required for learning. Depending on your needs, you can choose between “MMEye”, which creates the image decision model in the cloud, and “MMEye Box”, which creates it on-premises.
Important points to consider when introducing AI in the manufacturing sector
Although AI offers several advantages, many companies worry that they will not be able to benefit from it to the fullest extent even if they introduce it. Here are some points that manufacturing companies should keep in mind when introducing AI.
Otsuka Shokai also provides consulting services to prevent failure when introducing AI, so please feel free to contact us if you are interested.
Introduce your company after organizing your problems and goals.
While AI is a great technology that can help solve problems in the manufacturing industry, it is by no means a panacea. Therefore, if you implement Artificial intelligence without a clear mission or purpose such as “I want to introduce it,” there is a high probability that you will not be able to fully utilize it and will fail.
Consider what problems your company has in your business, whether they can be solved with AI, and what benefits you can achieve, and then introduce it.
Prioritize and introduce without overextending the scope.
Implementing Artificial intelligence requires significant effort and cost. If you expand the scope of the application too much from the beginning, it may not work properly, so we recommend that you prioritize before implementing. If you focus on introducing AI to tasks that are particularly challenging first, and then expand to other tasks after testing the effects of introducing AI, you are less likely to fail.
Validating the impact of introducing AI through PoC
When introducing AI, make sure to first conduct a “PoC (Proof of Concept)” to verify whether it is possible to create an AI model that meets expectations. Even if there is a task that you want to automate or simplify using AI, there may be cases where this is not possible due to the lack of available data or insufficient accuracy.
In the PoC, you will collect the necessary data, create a prototype AI model, actually make predictions and decisions using AI, and evaluate the results. If you proceed with full-scale implementation after validating the effectiveness of the Artificial intelligence application through a PoC, you are less likely to fail when implementing AI.
Emphasis on data quality and quantity
The quality and quantity of training data are critical to improving the accuracy of predictions and decisions made by AI. Please note that if there is not enough data or there are gaps in the data, the predictions and decisions made by Artificial intelligence are likely to be unsuccessful. If you do not currently have enough data to achieve your goals, use technologies such as IoT to collect additional data.
Continuous improvement
AI can further improve its accuracy by learning and repeating the analysis. Even after the operation has started, continue to verify and improve its effectiveness by learning again from new data obtained during the operation. In addition, AI needs to be aware of continuous updating, as the task content may change after the operation or new factors may arise that affect the predictions and decisions of AI.
Summary
This time, we introduce the use of Artificial intelligence in the manufacturing sector with examples. Although the adoption rate of AI in Japan is still low, it is believed that the use of Artificial intelligence will become necessary in the future. Compared with the past, AI solutions for the manufacturing sector have become more complete, and AI will now be introduced not only by large companies but also by small and medium-sized enterprises. Why not consider introducing and using AI to solve the problems your company is facing?