Invited Speaker

Assoc. Prof. Noriyuki Suyama, Toyo University, Japan
 

Dr. Noriyuki Suyama is an Associate Professor in Toyo University, Japan. His publications and research interest focus primarily on Global Marketing and Customer Relationship Management with quantitative methods. He was also engaged in commercial real estate management and food business as a general manager and a CEO, respectively in overseas markets. His overseas assignment totals more than 10 years, mainly in Southeast Asia. Currently, he is also an adjunct faculty of Marketing Research and International Marketing at Metropolitan University of Tokyo and Sophia University, respectively. Dr. Suyama belongs to Japanese Society of Marketing and Distribution, Japan Marketing Academy, Japan Society for Southeast Asia Studies, Fashion Business Association and Japan Halal Association. He is a member of Gerson Lehrman Group (GLG) Council, who consults with clients.

Relationship between Weather Conditions and Fashion Business in Japan and Implications for the Future - Part Ⅱ -

This is a progress report on a three-year project supported by the Grants-in-Aid for Scientific Research, administered by the Ministry of Education, Culture, Sports, Science and Technology of Japan. Specifically, it is an empirical study that will present a statistical model using AI to provide new knowledge that can be applied to the business world in the final year of the project in 2025. Currently, clothing sales in Japan are declining. Although this social phenomenon has a large impact on the gross domestic product, which is dominated by consumption, there is currently very little academic research on it. Accordingly, this paper focuses on the impact of weather conditions on consumer purchasing behavior regarding clothing. Using information such as the impact of the consumer's perceived temperature on the sales of each clothing item, a statistical model using artificial intelligence technology will be created. The ultimate goal is to create a statistical model that evaluates the degree of impact using cutting-edge artificial intelligence technology, elucidate the process that consumers go through to make a purchase throughout the supply chain, and propose a business model that will contribute to increasing clothing sales in the real business world. The data handled in this study are customer profiles and purchase history data held by department stores, as well as seasonal factors, store factors, basic weather conditions such as temperature, humidity, and wind power, pollen dispersion, and specific weather conditions such as long rains and El Niño. In addition, we will explore the possibility of using ID-attached POS data, qualitative variables other than weather data, time series data, panel data, etc. through trial and error.