SETソフトウェア Company Profile

The development of AI based on machine learning that utilizes large amounts of data and abundant computational resources has proven that Go AI can possess the skill to defeat the strongest active players, leading to heightened expectations for its application in solving various unsolved problems. Furthermore, recent dialogue-based AI models based on large-scale language models are significantly changing the speed and quality of intellectual production. On the other hand, in the business world, digital transformation (DX) of management and business processes is being promoted to meet the diverse needs of consumers. However, in overwhelmingly many companies, the current situation is that they are either only partially introducing digital tools into their business processes or are unable to capitalize on opportunities for business transformation due to the difficulty of making investment decisions for systems based on data utilization, the lack of expertise in data analysis techniques, and the absence of personnel skilled in organizational operations that lead to results. In this seminar, we will acquire practical knowledge through demonstrations on the following topics: - Problem-solving processes based on analytical models - Handling of small data, which poses challenges in the use of analytical models, along with various case studies - Excel-based analytical model development environments effective for sharing organizational know-how - Case studies of organizational analytical model applications directly linked to solving management issues.

The development of AI based on machine learning utilizing big data and abundant computing resources, particularly the recent interactive AI based on large-scale language models, is significantly changing the speed and quality of intellectual production. On the other hand, in the business world, there is a growing call for the digital transformation (DX) of business processes to meet the diverse needs of consumers in a global market. Companies are emerging that are developing AI systems based on unique analytical models aimed at utilizing data, which is an essential element, and are achieving results. However, in the overwhelming majority of companies, the difficulty of making investment decisions for system infrastructure based on data utilization, the lack of expertise in data analysis techniques, and the absence of personnel familiar with organizational operations to translate these into results are leading to significant opportunity losses. In this seminar, while introducing case studies from the manufacturing industry, we will deepen practical knowledge and understanding through demonstrations on the following topics: - In the problem-solving process based on analytical models, handling small data, which is a major issue, and various utilization examples. - The use of a programming-free analytical model development environment based on Excel, which is beneficial for organizational use of analytical models.

In the manufacturing industry, a large amount of data is handled, but to extract valuable information from the data and interpret it, various statistical analyses must be used. Statistical analysis, or multivariate analysis, can summarize data, confirm trends, analyze causes, and make future predictions, but it is necessary to choose the appropriate method according to the purpose. Regarding the recently trending topic of artificial intelligence, efficient learning requires careful consideration of how data is presented and the need to process data into a suitable form for learning. In this context, data analysis through the aforementioned statistical and multivariate analysis is also required. On the other hand, when trying to master statistics and multivariate analysis, traditionally, one has to learn content that is not actually used in practice through dry and uninteresting formulas. Additionally, statistical and multivariate analysis software is necessary for data analysis. However, statistical and multivariate analysis software is polarized as described below, leading to hesitation in easily introducing and utilizing it. This course will minimize academic content and focus on practical methods of various data analyses used in manufacturing practice.