Artificial Intelligence (AI) and Machine Learning Solutions

basic information

There are various forms of operation, ranging from on-premises to cloud-based operations. - Methods: Statistics, machine learning, deep learning - Operations: On-premises, cloud - Learning methods: Utilizing pre-trained models, starting from scratch - Data: Public data, internal data Therefore, it is necessary to choose the one that aligns with the customer's objectives and goals. For example, if you aim to analyze the factors that influence the sales of a particular series of products and use that information for future product development, it may not be necessary to use deep learning in the cloud; on-premises multiple regression using statistical methods and BI tools might be sufficient to achieve your goals. The most efficient methods and forms will vary depending on the objectives, so it is important to select the one that suits the customer.

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Applications/Examples of results

Here, we introduce an example of a solution to a somewhat unusual problem. The diagram above shows the optimization of the picking route for parts in a factory using machine learning. When a skilled worker performs the picking task, they naturally pick along the shortest route. However, when an inexperienced worker does it, they cannot follow the shortest route, leading to decreased efficiency. This system allows even inexperienced individuals to follow a sequence comparable to that of skilled workers, thus preventing variations in efficiency among workers. This is a classic traveling salesman problem, but by utilizing machine learning, we can find the optimal solution in a realistic timeframe.

Detailed information

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  Relationship with IoT and Big Data Sensor data from IoT and data from corporate activities are stored as big data and used for corporate purposes such as analyzing data to investigate the causes of issues, formulating countermeasures, and making predictions to aid in product development. The data is utilized as training data for AI and machine learning. As shown in the diagram below, the results are only effectively used when they feedback, like the red arrows, to solve the challenges and achieve the objectives that the company has.

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  Data Utilization For data that is not publicly available, such as corporate data, we must start from the collection of the data itself. Below is a diagram summarizing the acquisition and utilization of this data. By storing data in a database instead of individual Excel files, it becomes possible to share the data, enabling its utilization through statistics, AI, and machine learning. Our company actively supports these four stages.

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  Problem-solving process Our company defines the problem-solving process in three stages as follows. There is no cost for the hearing, but costs will be incurred for the subsequent stages.

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  Frequently Asked Questions Many people may have questions about how artificial intelligence, machine learning, and deep learning differ, as well as their English representations. When illustrating their relationships, it looks like this.