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 strength to defeat the strongest active players, leading to a significant increase in expectations for its application in solving various unsolved problems. Furthermore, recent interactive AI based on large-scale language models is poised to greatly change the speed and quality of intellectual production. On the other hand, in the business field, 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 difficulties in investment decisions for system infrastructure based on data utilization, a 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 utilization 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 utilization 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, and companies are emerging that are developing AI systems based on unique analytical models aimed at utilizing data, leading to tangible results. However, in the overwhelming majority of companies, the difficulty of making investment decisions for system foundations based on data utilization, the lack of expertise in data analysis technologies, and the absence of personnel skilled in organizational operations to connect these efforts to 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: - How to handle small data, which poses a significant problem in the problem-solving process based on analytical models, 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 need to be used. Statistical analysis, or multivariate analysis, can summarize data, confirm trends, analyze causes, and make future predictions, but it is necessary to select appropriate methods 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 analyses is also necessary. 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 required for data analysis. However, statistical and multivariate analysis software is polarized as described below, leading to hesitation in easy implementation and utilization. In this course, we will minimize academic content and focus on practical methods of various data analyses used in manufacturing practice.