PL EN


Preferencje help
Widoczny [Schowaj] Abstrakt
Liczba wyników
Tytuł artykułu

Intelligent Design Suggestion and Sales Forecasting for New Products in the Apparel Industry

Treść / Zawartość
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
This study demonstrates how algorithms can assist humans in decision-making in the apparel industry. A two-stage method including suggestions and intelligent forecasting was proposed. In the first stage, a web crawler was used to browse a B2C apparel website to identify popular products. In the second stage, machine learning methods were used to predict the sales demand for new products. Additionally, we used Google Trends to collect external information indices to adjust the demand forecasting. Our numerical study shows that the intelligent forecasting approach can effectively reduce the Mean Square Error (MSE), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE) by at least 45.79, 26.35, and 26.34 %, respectively.
Rocznik
Strony
30--38
Opis fizyczny
Bibliogr. 25 poz., rys., tab.
Twórcy
  • Department of Industrial Management, National Taiwan University of Science and Technology, Taipei, Taiwan
  • Artificial Intelligence for Operations Management Research Center, National Taiwan University of Science and Technology, Taipei, Taiwan
autor
  • Department of Industrial Management, National Taiwan University of Science and Technology, Taipei, Taiwan
  • Artificial Intelligence for Operations Management Research Center, National Taiwan University of Science and Technology, Taipei, Taiwan
autor
  • Department of Industrial Management, National Taiwan University of Science and Technology, Taipei, Taiwan
  • Artificial Intelligence for Operations Management Research Center, National Taiwan University of Science and Technology, Taipei, Taiwan
autor
  • Department of Business Management, Tatung University, Taipei, Taiwan
Bibliografia
  • 1. Bhardwaj V, Fairhurst A. Fast fashion: response to changes in the fashion industry. The International Review of Retail, Distribution and Consumer Research 2010, 20(1): 165-173.
  • 2. Barnes L, Lea-Greenwood G. Fast fashion in the retail store environment. International Journal of Retail & Distribution Management 2010, 38(10): 760-772.
  • 3. Čiarnienė R, Vienažindienė M. Management of Contemporary Fashion Industry: Characteristics and Challenges. Procedia - Social and Behavioral Sciences 2014, 156: 63-68.
  • 4. Lin YT, Parlaktürk AK, Swaminathan JM. Vertical Integration under Competition: Forward, Backward, or No Integration? Production and Operations Management 2014, 23(1):19-35.
  • 5. Alexandersson E, R, Matlak R, Torell V. Cultural differences in fashion magazines, Targeting Vogue 2017.
  • 6. Zalay D. Environmental impacts of fashion industry increasingly alarming, Environmental Impacts of Fashion Industry Increasingly Alarming (hungarytoday.hu) 2021
  • 7. Laaziz El. AI based forecasting in fast fashion industry: a review. IOP Conference Series: Materials Science and Engineering 2020. 827. 012065. 10.1088/1757-899X/827/1/012065.
  • 8. Yelland PM, Dong X. Forecasting Demand for Fashion Goods: A Hierarchical Bayesian Approach. In T.-M. Choi, C.-L. Hui, & Y. Yu (Eds.), Intelligent Fashion Forecasting Systems: Models and Applications 2014, 71-94. Berlin, Heidelberg: Springer Berlin Heidelberg
  • 9. Ramos P, Oliveira JM. A Procedure for Identification of Appropriate State Space and ARIMA Models Based on Time-Series Cross-Validation. Algorithms 2016, 9(4): 76.
  • 10. Frank C, Garg A, Sztandera L, Raheja A. Forecasting women’s apparel sales using mathematical modeling. International Journal of Clothing Science and Technology 2003, 15(2): 107-125.
  • 11. Au, K. F., Choi T. M. & Yu Y. (2008). Fashion retail forecasting by evolutionary neural networks. International Journal of Production Economics, 114(2), 615-630.
  • 12. Sun ZL, Choi TM, Au KF, Yu Y. Sales forecasting using extreme learning machine with applications in fashion retailing. Decision Support Systems 2008, 46: 411-419.
  • 13. Yüzbaşıoğlu O, Küçükaydin, H. Forecasting with Ensemble Methods: An Application Using Fashion Retail Sales Data. Unpublished doctoral dissertation 2019. MEF University Institute of Science and Technology, Istanbul, Turkey.
  • 14. Abhishekh, Gautam SC, Singh SR. A new method of time series forecasting using intuitionistic fuzzy set based on average-length. Journal of Industrial and Production Engineering 2020, 37(4): 175-185.
  • 15. Huang H, Liu Q. Intelligent retail forecasting system for new clothing products considering stock-out. Fibres and Textiles in Eastern Europe 2017, 25(1):10-16.
  • 16. Loureiro ALD, Miguéis VL, da Silva LFM. Exploring the use of deep neural networks for sales forecasting in fashion retail. Decision Support Systems 2018, 114: 81-93.
  • 17. Singh P, Gupta Y, Jha N, Rajan A. Fashion Retail: Forecasting Demand for New Items. ArXiv 2019, abs/1907.01960.
  • 18. Comlan M, Koulo E. Sales Forecast and Design Generation for Textile Products Using Machine Learning. Proceedings of the 2022 Future of Information and Communication Conference 2022, 183-197.
  • 19. Liaw A, Wiener M. Classification and Regression by RandomForest. R News 2002, 2(3):18-22.
  • 20. Chen T, Guestrin C. XGBoost: A Scalable Tree Boosting System. Paper presented at the Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining 2016, San Francisco, California, USA.
  • 21. Cui R, Gallino S, Moreno A, Zhang DJ. The Operational Value of Social Media Information. Production and Operations Management 2018, 27(10): 1749-1769.
  • 22. Boone T, Ganeshan R, Hicks RL, Sanders NR. Can Google Trends Improve Your Sales Forecast? Production and Operations Management 2018, 27(10): 1770-1774.
  • 23. Yavari M, Ajalli P. Suppliers’ coalition strategy for green-Resilient supply chain network design. Journal of Industrial and Production Engineering 2021, 38(3): 197-212.
  • 24. Tsao YC, Zhang Q, Zhang X, Vu TL. Supply chain network design for perishable products under trade credit. Journal of Industrial and Production Engineering 2921, 38(6): 466-474.
  • 25. Sudusinghe JI, Seuring S. Supply chain collaboration and sustainability performance in circular economy: A systematic literature review. International Journal of Production Economics 2021, 108402.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-a78a0d79-96b6-4896-8662-0301633d4cf2
JavaScript jest wyłączony w Twojej przeglądarce internetowej. Włącz go, a następnie odśwież stronę, aby móc w pełni z niej korzystać.