日本語 
EVERSTEEL Co., Ltd., provider of the iron scrap AI inspection system “Tetsunavi Inspection AI,” is pleased to announce the launch of a new iron scrap delivery data analysis function, “Tetsunavi Analytics.”
At the same time, starting with this service, we will develop multiple products as part of the steel scrap digital transformation platform “TetsuNavi,” which will support the digital transformation of the entire steelmaking process, from procurement to steelmaking.
Tetsunavi’s inspection AI, which has been introduced in over 10 factories, has further evolved to support delivery management and purchasing by analyzing and visualizing delivery data accumulated starting from inspections, as well as to promote the efficiency of inspection work.
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Background to the release of “Tenavi Analytics”
The iron scrap AI inspection system “Tetsunavi Inspection AI” that we have provided so far has been introduced at over 10 locations in Japan, mainly by major electric furnace manufacturers. The amount of scrap analyzed by this product exceeds 4.8 million tons per year, which covers 20% of the scrap consumed in Japan. As a measure to further utilize delivery data, we have released Tetsunavi Analytics.
In recent years, as scrap supply and demand has tightened, more efficient scrap procurement management, blending, and steelmaking have become necessary, resulting in the following issues becoming apparent at steelmaking sites:
· Although the workplace is aware of the scrap quality of each supplier, it is not quantified or shared in a way that can be utilized by the purchasing department, and this information is not utilized in purchasing strategies to decide which suppliers should purchase more scrap from.
・Delivery volume and return data are printed out manually and on paper every month, making reviewing and cross-checking time-consuming.
· The inspection department and purchasing department perform Excel analyses separately, and the information is not centralized.
Tetsunavi Analytics serves as a common in-house platform that connects data from the pre- and post-processes starting from inspection and even provides improvement suggestions, helping to resolve these issues all at once.
SOURCE: PRTimes
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