Data Mining: To determine the optimal order point in inventory control system

Mehraneh Hamedani Vash¹ Reza Radfar² Mohammad Ali Afshar Kazemi³

Abstract :
The recognition of the amount of consumption, as well as a prediction of the essential Commodities of the railway industry, which leads to the relocation of an extensive volume of individuals and goods, is of critical importance. This depends on numerous factors, as it has the capacity of being all-encompassing for future periods. The lead time, in order to receive goods, on one hand, is extremely variable; whereas, on the other hand, by alleviating decision-making indexes, for specific reasons, such as, economic and political conditions, requires amendments, in relative to determining the optimal order point method and where a need is sensed. Hence, in this paper, by utilizing data mining, according to a composed E.O.Q Model, on the fundamentals of a costing method, based on the ABC concept in the inventory control system has been presented. The results of the research illustrate that, the model which has come to hand, is effective in incrementing productivity and reducing costs with the help of the clustering of goods, to determine the amount of consumption and predicting the demand for priority purchases of railway depots.
Keywords : EconomicOrderQuantity (E.O.Q), Data Mining, K-means Clustering, ABC

زبان مقاله : انگلیسی
محور مقاله : علوم انسانی
محل انتشار : کنگره بین المللی علوم و مهندسی ژاپن - TOKYO
آدرس وب سایت کنفرانس : tuicet.com
ارسال کننده : مهرانه همدانی وش
کد IOI مقاله : XBHB-DHGAA
لینک مستقیم مقاله : https://isnac.ir/XBHB-DHGAA