A Novel Approach for Multi Product Demand Forecast Using Data Mining Techniques (Empirical Study: Carpet Industry) | ||
| Advances in Industrial Engineering | ||
| دوره 53، شماره 4، زمستان 2019، صفحه 169-184 اصل مقاله (1000.12 K) | ||
| نوع مقاله: Research Paper | ||
| شناسه دیجیتال (DOI): 10.22059/jieng.2021.316849.1746 | ||
| نویسندگان | ||
| Sayedmohammadreza Vaghefinezhad1؛ Jafar Razmib* 2؛ Fariborz Jolai2 | ||
| 1Industrial Engineering, Kish International Campus, University of Tehran | ||
| 2School of Industrial Engineering, College of Engineering, University of Tehran, Iran | ||
| چکیده | ||
| Accurate demand forecasting plays an important role in meeting customers’ expectations and satisfaction that strengthen the enterprise's competitive position. In this research, time series and artificial neural networks methods compete to provide more precise demand estimation while having a large variety of products. After obtaining the initial results, suggestions have been implemented to improve forecasting accuracy. As a direct result of that, the average of mean absolute percentage error (MAPE) of all products' demand forecast reduces significantly. To improve the quality of historical records, association rules and substitution ratio have been applied . This method plays a significant role to detect the existing pattern in historical data and MAPE reduction. The satisfactory and applicable results provide the company with more accurate forecast. Moreover, the issue of precepting confusing historical data which caused unforecastable trends has been solved. The R language and “neuralnet”, “nnfor”, “forecast”, and “arules” packages have been applied in programming. | ||
| کلیدواژهها | ||
| Artificial Neural Network؛ Association Rules؛ Demand Forecasting؛ Data Mining؛ Time Series | ||
| مراجع | ||
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آمار تعداد مشاهده مقاله: 811 تعداد دریافت فایل اصل مقاله: 938 |
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