We have introduced a data mining system utilizing natural language processing technology from Business Engineering Co., Ltd. (located in Chiyoda-ku, Tokyo, represented by President Masakazu Haneda) to enhance safety and quality in our logistics operations. By leveraging this data mining system, we aim to improve the quality of our logistics services by applying insights gained to safety and quality management and on-site education.
Since 2018, we have been using the iPad software “mcframe RAKU-PAD” provided by Business Engineering Co., Ltd. to digitize “near misses,” accident reports,” “risk assessments,” and “patrol activities” and share them among all our logistics bases nationwide. Through accumulating data gathered from across the country, we discovered that similar “near misses” and “accidents” were occurring at various locations and that there were differences in the accuracy of accident countermeasures proposed by site managers, which also affected the accident rate.
With the aim of preventing accidents and anomalies in on-site work, we will utilize Business Engineering Co., Ltd.’s data mining system to derive effective countermeasures from the vast amount of data accumulated thus far, swiftly and accurately develop safety and quality measures, and apply them to on-site education.
We will continue to strive for enhanced safety and quality in our logistics operations, aiming to become a trusted company by providing high-quality logistics services to our customers.
Achievements Realized by the Data Mining System:
- Detect and confirm occurrences of similar “near misses” and “accident reports,” propose preventive measures.
- Detect effective countermeasure examples for “near misses” and “accident reports” and deploy them to other logistics centers.
- Suggest the level of danger based on keywords frequently found in each report and countermeasure.
- Understand the occurrences and proportions of “near misses” and accidents along the timeline from accident prevention to occurrence and countermeasures. Also, understand them by years of experience of workers.
Source: Suzuyo
https://www.suzuyo.co.jp/news/notice/2024031101.html
