In the design of production processes in the manufacturing industry, it is necessary to transfer the defect investigation knowledge, which is the empirical knowledge of experienced workers, to beginners. Leonard and Swap pointed out the importance of acquiring empirical knowledge through experimental learning. However, they have not clarified the explicit empirical knowledge representation method and the experimental learning method. If the knowledge cannot be represented, empirical knowledge remains tacit, and the knowledge transfer from experts to beginners is not only individual and inefficient, but also difficult to reuse.In this paper, we propose a production knowledge chart (PKC) that expresses the production process to acquire the empirical knowledge necessary for investigating defects in the production process. In addition, we clarify the effectiveness of the proposed method by expressing a general production process with PKC. In addition, we will discuss future research.

The "Study Group for Quality and Value Creation in 2030," launched in April 2022, is a group of manufacturing companies in the Chubu region, mainly in the automotive field, working on the transformation from manufacturing to "koto-zukuri" Solution and the creation of new quality and value rooted in solving social issues.From this perspective, the group aims to create specific indicators forQC x DX x SDGs in the supply chain of the entire automotive industry, and to create an SDQ cube that will optimize the entire industry by continuing to create optimal quality and value without waste and duplication of resources at each level of the hierarchy.As a first step, this paper proposes a template designed to visualize the actual status and ideal state of each tier from the three perspectives of SDGs, DX, and QC.By presenting this template to society at large and encouraging similar efforts in each field, we intend to coordinate and optimize resources and initiatives throughout society.

The design of composite services targeting things, events, and IT, such as the Product and Service System (PSS), is required. So far, service design models and methods using ArchiMate have been proposed. However, the method for analyzing service quality was not clear. In this paper, we propose a pattern for analyzing service quality using ArchiMate and demonstrate its effectiveness by applying it to a specific service quality analysis.

Propose notation rules for game mechanics using ArchiMate.The objective is to improve the readability and ease of understanding of the diagrams created.

The authors are analyzing student behavior in programming exercises using Jupyter Notebook. We report the result of examining the method of analyzing the execution log of the exercise process by machine learning.