|تعداد مشاهده مقاله||107,995,100|
|تعداد دریافت فایل اصل مقاله||84,430,272|
A Novel Approach for Multi Product Demand Forecast Using Data Mining Techniques (Empirical Study: Carpet Industry)
|Advances in Industrial Engineering|
|دوره 53، شماره 4، دی 2019، صفحه 169-184 اصل مقاله (1000.12 K)|
|نوع مقاله: Research Paper|
|شناسه دیجیتال (DOI): 10.22059/jieng.2021.316849.1746|
|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|
|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.|
|Artificial Neural Network؛ Association Rules؛ Demand Forecasting؛ Data Mining؛ Time Series|
 Hyndman, R. J., and Athanasopoulos, G. “Forecasting : Principles and Practice”, (2019).
 Gonçalves, J. N. C.; Cortez, P.; Carvalho, M. S.; Frazão, N. M. “A multivariate approach for multi-step demand forecasting in assembly industries: Empirical evidence from an automotive supply chain”. Decision Support Systems, 113452, (2020).
 del Campo-Ávila, J.; Takilalte, A.; Bifet, A.; Mora-López, L. “Binding data mining and expert knowledge for one-day-ahead prediction of hourly global solar radiation”, Expert Systems with Applications, 114147, (2020)
 Van Nguyen, T.; Zhou, L.; Chong, A. Y. L.; Li, B.; Pu, X. “Predicting customer demand for remanufactured products: A data-mining approach”. European Journal of Operational Research, 281(3), pp.543–558, (2020).
 Chopra, S., and Meindl, P. “Supply chain management: Strategy, planning, and operation (6th ed.)”, Upper Saddle River, New Jersey: Pearson Education, Inc. (2016)
 Wheelwright, S.C. and Makridakis, S.G. “Forecasting methods for management(5th ed.)”. Wiley, (1989)
 Mentzer, JohnT. “Forecasting with adaptive extended exponential smoothing”, Journal of the Academy of Marketing Science, 16(3-4), pp.62-70, (1988).
 Pantazopoulos, Sotiris N., and Pappis, Costas P. “A new adaptive method for extrapolative forecasting algorithms”, European Journal of Operational Research, 94(1), pp.106-111, (1996).
 Roberts, S. D., and R. Reed. "The Development of a Self-Adaptive Forecasting Technique", AIIE Transactions I (No. 4), 314-322, (1969).
 Hyndman, R. J.; Koehler, A. B.; Snyder, R. D.; Grose, S. “A state space framework for automatic forecasting using exponential smoothing methods”. International Journal of Forecasting, 18, pp.439–454, (2002).
 Taylor, J. W. “Exponential smoothing with a damped multiplicative trend. International Journal of Forecasting”, 19, pp.273– 289, (2003).
 Muth, J. F. “Optimal properties of exponentially weighted forecasts”, Journal of the American Statistical Association, 55, pp.299– 306, (1960).
 Holt, C. C. “Forecasting seasonals and trends by exponentially weighted averages”, O.N.R. Memorandum 52/1957, Carnegie Institute of Technology. Reprinted with discussion in 2004. International Journal of Forecasting, 20, pp.5 – 13. (1957).
 Winters, P. R. “Forecasting sales by exponentially weighted moving averages”, Management Science 6, pp.324–342, (1960).
 Anne B.; Koehler Ralph D.; Snyder J.;Keith Ord, “Forecasting models and prediction intervals for the multiplicative Holt–Winters method”, International Journal of Forecasting, 17, pp. 269-286, (2001)
 Hamzaçebi, Coşkun. “Improving artificial neural networks’ performance in seasonal time series forecasting”, Information Sciences, 178(23), pp.4550-4559, (2008).
 G.E.P. Box, G.M. Jenkins, “Time Series Analysis Forecasting and Control”, Holden-Day, San Francisco, (1976).
 Zhang, G.; Peter, ; Qi, Min. “Neural network forecasting for seasonal and trend time series”, European Journal of Operational Research, 160(2), pp.501-514, (2005).
 Williams, T. M. “Adaptive Holt-Winters forecasting”, Journal of the Operational Research Society, 553-560, (1987).
 Zhang, G.; Patuwo, B. E.; Hu, M. Y. “Forecasting with artificial neural networks: The state of the art”, International journal of forecasting, 14(1), pp.35-62,(1998).
 Hornik, K.; Stinchcombe, M.; White, H. “Multilayer feedforward networks are universal approximators”. Neural networks, 2(5), pp.359-366,(1989).
 Funahashi, K.I. “On the approximate realization of continuous mappings by neural networks. Neural networks”, 2(3), pp.183-192, (1989).
 Da Costa Lewis, N. “Neural Networks for Time Series Forecasting with R”. (2017).
 Yu, Y.; Choi, T.-M.; Hui, C.-L. “An intelligent fast sales forecasing model for fashion products”, Experts systems with application, 38, pp. 7373-7379, (2011).
 Kumar, P.; Herbert, M.; Rao, S. “Demand forecasting Using Artificial Neural Network Based on Different Learning Methods: Comparative Analysis”, International journal for research in applied science and engineering technology, 2(4), pp. 364-374, (2014).
 Zhang, X. “Time series analysis and prediction by neural networks”, Optimization Methods and Software, 4(2), pp.151-170, (1998).
 Aggarwal, Charu C. “Data Mining: The Textbook”. Springer, (2015).
 Telikani A.; Gandomi A.; Shahbahrami A. “A survey of evolutionary computation for association rule mining”, Information Sciences, 524, pp.318-352, (2020).
 Agrawal, R., and Srikant, R. “Fast algorithms for mining association rules. 20th int. conf. very large data bases, VLDB , 1215, pp. 487-499, (1994).
 Han, J., Pei, J., & Yin, Y. “Mining frequent patterns without candidate generation”, ACM, 29(2), pp. 1-12, (2000).
 Zaki, M. J.; Parthasarathy, S.; Ogihara, M.; Li, W. “New Algorithms for Fast Discovery of Association Rules”. In KDD , 97, pp. 283-286, (1997).
 Savasere, A.; Omiecinski, E.; Navathe, S. “Mining for strong negative associations in a large database of customer transactions”, 14th International Conference on IEEE, pp. 494-502, (1998).
 Shocker, A. D.; Bayus, B. L.; Kim, N. “Product complements and substitutes in the real world: The relevance of other products”, Journal of Marketing, 68(1), pp.28-40, (2004).
 Scholz-Reiter, B.; Heger, J.; Meinecke, C.; Bergmann, J. “Integration of demand forecasts in ABC-XYZ analysis: Practical investigation at an industrial company”, International Journal of Productivity and Performance Management, 61(4),pp.445–451,(2012).
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