Research on AI- and machine learning-based support for store information surveys
First written in FY2017. 4 of 6 entries say "Advanced", first in FY2018. Latest entry: FY2021.
1 other company's latest entry also says "Advanced".
- FY2021AdvancedAnnual report
「During the fiscal year, in addition to a proof of concept for “demand forecasting” using AI, we began work on “SCM optimization” using a quantum computer.」
The focus of the company’s AI research progressed to a proof of concept for demand forecasting, and it also began work on SCM optimization using a quantum computer. - FY2020AdvancedAnnual report
「During the fiscal year, we conducted research on “demand forecasting using AI” and tested demand forecasting using transaction data, weather data and other information, with the aim of developing a new information-sharing model for the industry.」
The focus of the company’s AI and machine-learning research has progressed from store information and survey analysis to testing demand forecasting and an information-sharing model using transaction data and weather data. - FY2020-Q1RestatedAnnual report
「We continued research into and development of AI and big data applications.」
The company continues to conduct research and development on the use of AI and big data in distribution operations. - FY2019AdvancedAnnual report
「We are testing the potential for contributing to labor savings and the creation of new value in the distribution industry. During the current fiscal year, we conducted research under the themes of “investigating the store attributes of a specific chain using image recognition based on web scraping technology and a machine-learning API” and “a proof of concept for text mining to use survey response data.”」
The company has made its applications of AI and machine learning more specific, expanding from store-attribute research to image recognition and text mining of survey responses. - FY2018AdvancedAnnual report
「As AI and machine learning continue to be applied in business, we are testing their potential to contribute to efficiency improvements across the distribution industry. During the current fiscal year, we conducted research under the themes of “store inquiries using text mining and chatbots” and “analyzing free-text answers in surveys using machine learning.”」
The company has made its applied AI and machine-learning research more specific, focusing on the use of text mining and chatbots for store inquiries and the analysis of free-text survey responses. - FY2017First statedAnnual report
「As AI and machine learning continue to be applied in business, we are testing their potential to contribute to efficiency improvements across the distribution industry. During the current fiscal year, we conducted research to support survey work, including information gathering and organization, under the theme of having a robot investigate store attributes registered in a business-partner database.」
The company has for the first time specifically described research testing the application of AI and machine learning to the distribution industry, with robots supporting investigations of store attributes in a business-partner database.