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Mardan, Ehsan, Qorbani, Ali. (1404). Development of an Inventory Management Strategy Model (Two-Products) Based on Demand Predicting in Digital Supply Chain Networks by Combining Data Analysis Methods. , (), -. doi: 10.22059/aie.2026.406483.1962
Ehsan Mardan; Ali Qorbani. "Development of an Inventory Management Strategy Model (Two-Products) Based on Demand Predicting in Digital Supply Chain Networks by Combining Data Analysis Methods". , , , 1404, -. doi: 10.22059/aie.2026.406483.1962
Mardan, Ehsan, Qorbani, Ali. (1404). 'Development of an Inventory Management Strategy Model (Two-Products) Based on Demand Predicting in Digital Supply Chain Networks by Combining Data Analysis Methods', , (), pp. -. doi: 10.22059/aie.2026.406483.1962
Mardan, Ehsan, Qorbani, Ali. Development of an Inventory Management Strategy Model (Two-Products) Based on Demand Predicting in Digital Supply Chain Networks by Combining Data Analysis Methods. , 1404; (): -. doi: 10.22059/aie.2026.406483.1962

Development of an Inventory Management Strategy Model (Two-Products) Based on Demand Predicting in Digital Supply Chain Networks by Combining Data Analysis Methods

Advances in Industrial Engineering
مقالات آماده انتشار، پذیرفته شده، انتشار آنلاین از تاریخ 28 بهمن 1404
شناسه دیجیتال (DOI): 10.22059/aie.2026.406483.1962
نویسندگان
Ehsan Mardan* 1؛ Ali Qorbani2
1department of industrial engineering, semnan university, semnan, iran
2Department of Industrial Engineering, Semnan University, Semnan, Iran
چکیده
Recording and classifying commerce data electronically within the new age of information and development has many benefits for sellers and consumers in the online supply chain. We can predict customer purchase behavior patterns by analyzing this classified data. In recent years, digital stores have received additional attention due to their advantages. On the other hand, the performance of these stores is directly associated with the performance of their suppliers. Hence, supply chain management is essential during this sales system. In this investigation, the classification methods (WFRM), demand prediction analysis (Binary Logistic Regression), data classification (Discriminant Analysis), time series analysis (Trend Analysis), and mathematical modeling have been used to select suppliers to order led to the development of a management strategy to prevent shortages and reduce the average inventory and costs of sending and producing for suppliers. It ultimately covers a 29% error in sales data for assigning suppliers to orders.
کلیدواژه‌ها
Customer clustering؛ Data mining؛ logistic regression؛ Online supply chain؛ Demand forecast
آمار
تعداد مشاهده مقاله: 59





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