Demand forecasting in retail operations for fashionable products: methods, practices, and real case study
Author
Abstract
Suggested Citation
DOI: 10.1007/s10479-019-03148-8
Download full text from publisher
As the access to this document is restricted, you may want to search for a different version of it.
References listed on IDEAS
- Shuyun Ren & Hau-Ling Chan & Pratibha Ram, 2017. "A Comparative Study on Fashion Demand Forecasting Models with Multiple Sources of Uncertainty," Annals of Operations Research, Springer, vol. 257(1), pages 335-355, October.
- Xiao Liu & Param Vir Singh & Kannan Srinivasan, 2016. "A Structured Analysis of Unstructured Big Data by Leveraging Cloud Computing," Marketing Science, INFORMS, vol. 35(3), pages 363-388, May.
- Willem van Jaarsveld & Alan Scheller-Wolf, 2015. "Optimization of Industrial-Scale Assemble-to-Order Systems," INFORMS Journal on Computing, INFORMS, vol. 27(3), pages 544-560, August.
- Syntetos, Aris A. & Boylan, John E., 2005. "The accuracy of intermittent demand estimates," International Journal of Forecasting, Elsevier, vol. 21(2), pages 303-314.
- Au, Kin-Fan & Choi, Tsan-Ming & Yu, Yong, 2008. "Fashion retail forecasting by evolutionary neural networks," International Journal of Production Economics, Elsevier, vol. 114(2), pages 615-630, August.
- Dimitris Bertsimas & Nathan Kallus & Amjad Hussain, 2016. "Inventory Management in the Era of Big Data," Production and Operations Management, Production and Operations Management Society, vol. 25(12), pages 2006-2009, December.
- Teunter, Ruud H. & Syntetos, Aris A. & Zied Babai, M., 2011. "Intermittent demand: Linking forecasting to inventory obsolescence," European Journal of Operational Research, Elsevier, vol. 214(3), pages 606-615, November.
- Wong, W.K. & Guo, Z.X., 2010. "A hybrid intelligent model for medium-term sales forecasting in fashion retail supply chains using extreme learning machine and harmony search algorithm," International Journal of Production Economics, Elsevier, vol. 128(2), pages 614-624, December.
- T-M Choi & D Li & H Yan, 2003. "Optimal two-stage ordering policy with Bayesian information updating," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 54(8), pages 846-859, August.
- Mostard, Julien & Teunter, Ruud & de Koster, René, 2011. "Forecasting demand for single-period products: A case study in the apparel industry," European Journal of Operational Research, Elsevier, vol. 211(1), pages 139-147, May.
- Pai, Ping-Feng & Lin, Chih-Sheng, 2005. "A hybrid ARIMA and support vector machines model in stock price forecasting," Omega, Elsevier, vol. 33(6), pages 497-505, December.
- Li, Chongshou & Lim, Andrew, 2018. "A greedy aggregation–decomposition method for intermittent demand forecasting in fashion retailing," European Journal of Operational Research, Elsevier, vol. 269(3), pages 860-869.
- Phillip M. Yelland & Xiaojing Dong, 2014. "Forecasting Demand for Fashion Goods:A Hierarchical Bayesian Approach," Springer Books, in: Tsan-Ming Choi & Chi-Leung Hui & Yong Yu (ed.), Intelligent Fashion Forecasting Systems: Models and Applications, edition 127, chapter 0, pages 71-94, Springer.
- Schaer, Oliver & Kourentzes, Nikolaos & Fildes, Robert, 2019. "Demand forecasting with user-generated online information," International Journal of Forecasting, Elsevier, vol. 35(1), pages 197-212.
- Thomassey, Sebastien & Happiette, Michel & Castelain, Jean Marie, 2005. "A short and mean-term automatic forecasting system--application to textile logistics," European Journal of Operational Research, Elsevier, vol. 161(1), pages 275-284, February.
- Alain Yee Loong Chong & Eugene Ch’ng & Martin J. Liu & Boying Li, 2017. "Predicting consumer product demands via Big Data: the roles of online promotional marketing and online reviews," International Journal of Production Research, Taylor & Francis Journals, vol. 55(17), pages 5142-5156, September.
- Olson, Dennis & Mossman, Charles, 2003. "Neural network forecasts of Canadian stock returns using accounting ratios," International Journal of Forecasting, Elsevier, vol. 19(3), pages 453-465.
Citations
Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
Cited by:
- Fildes, Robert & Kolassa, Stephan & Ma, Shaohui, 2022. "Post-script—Retail forecasting: Research and practice," International Journal of Forecasting, Elsevier, vol. 38(4), pages 1319-1324.
- Rémy Garnier, 2022. "Concurrent neural network: a model of competition between times series," Annals of Operations Research, Springer, vol. 313(2), pages 945-964, June.
- Vineet Paliwal & Shalini Chandra & Suneel Sharma, 2020. "Blockchain Technology for Sustainable Supply Chain Management: A Systematic Literature Review and a Classification Framework," Sustainability, MDPI, vol. 12(18), pages 1-39, September.
- Atanu Chaudhuri & Manjot Singh Bhatia & Yasanur Kayikci & Kiran J. Fernandes & Samuel Fosso-Wamba, 2023. "Improving social sustainability and reducing supply chain risks through blockchain implementation: role of outcome and behavioural mechanisms," Annals of Operations Research, Springer, vol. 327(1), pages 401-433, August.
- Xinxue (Shawn) Qu & Aslan Lotfi & Dipak C. Jain & Zhengrui Jiang, 2022. "Predicting upgrade timing for successive product generations: An exponential‐decay proportional hazard model," Production and Operations Management, Production and Operations Management Society, vol. 31(5), pages 2067-2083, May.
- Swaminathan, Kritika & Venkitasubramony, Rakesh, 2024. "Demand forecasting for fashion products: A systematic review," International Journal of Forecasting, Elsevier, vol. 40(1), pages 247-267.
- Duong An & Duy Tran Le Anh & Huong Le Thi Cam & Rajkishore Nayak & Majo George & Loan Bui Thi Cam & Nhu-Y Ngoc Hoang & Duy Tan Nguyen & Huy Truong Quang, 2024. "Navigating global supply networks: a strategic framework for resilience in the apparel industry," Operations Management Research, Springer, vol. 17(2), pages 523-543, June.
Most related items
These are the items that most often cite the same works as this one and are cited by the same works as this one.- Fildes, Robert & Ma, Shaohui & Kolassa, Stephan, 2019. "Retail forecasting: research and practice," MPRA Paper 89356, University Library of Munich, Germany.
- Fildes, Robert & Ma, Shaohui & Kolassa, Stephan, 2022. "Retail forecasting: Research and practice," International Journal of Forecasting, Elsevier, vol. 38(4), pages 1283-1318.
- Swaminathan, Kritika & Venkitasubramony, Rakesh, 2024. "Demand forecasting for fashion products: A systematic review," International Journal of Forecasting, Elsevier, vol. 40(1), pages 247-267.
- Majd Kharfan & Vicky Wing Kei Chan & Tugba Firdolas Efendigil, 2021. "A data-driven forecasting approach for newly launched seasonal products by leveraging machine-learning approaches," Annals of Operations Research, Springer, vol. 303(1), pages 159-174, August.
- Hu, Qiwei & Boylan, John E. & Chen, Huijing & Labib, Ashraf, 2018. "OR in spare parts management: A review," European Journal of Operational Research, Elsevier, vol. 266(2), pages 395-414.
- Kourentzes, Nikolaos & Athanasopoulos, George, 2021.
"Elucidate structure in intermittent demand series,"
European Journal of Operational Research, Elsevier, vol. 288(1), pages 141-152.
- Nikolaos Kourentzes & George Athanasopoulos, 2019. "Elucidate Structure in Intermittent Demand Series," Monash Econometrics and Business Statistics Working Papers 27/19, Monash University, Department of Econometrics and Business Statistics.
- Zeynep Hilal Kilimci & A. Okay Akyuz & Mitat Uysal & Selim Akyokus & M. Ozan Uysal & Berna Atak Bulbul & Mehmet Ali Ekmis, 2019. "An Improved Demand Forecasting Model Using Deep Learning Approach and Proposed Decision Integration Strategy for Supply Chain," Complexity, Hindawi, vol. 2019, pages 1-15, March.
- Pinçe, Çerağ & Turrini, Laura & Meissner, Joern, 2021. "Intermittent demand forecasting for spare parts: A Critical review," Omega, Elsevier, vol. 105(C).
- Schlaich, Tim & Hoberg, Kai, 2024. "When is the next order? Nowcasting channel inventories with Point-of-Sales data to predict the timing of retail orders," European Journal of Operational Research, Elsevier, vol. 315(1), pages 35-49.
- Spiliotis, Evangelos & Makridakis, Spyros & Kaltsounis, Anastasios & Assimakopoulos, Vassilios, 2021. "Product sales probabilistic forecasting: An empirical evaluation using the M5 competition data," International Journal of Production Economics, Elsevier, vol. 240(C).
- Wang, Shengjie & Kang, Yanfei & Petropoulos, Fotios, 2024. "Combining probabilistic forecasts of intermittent demand," European Journal of Operational Research, Elsevier, vol. 315(3), pages 1038-1048.
- Lalou Panagiota & Ponis Stavros T. & Efthymiou Orestis K., 2020. "Demand Forecasting of Retail Sales Using Data Analytics and Statistical Programming," Management & Marketing, Sciendo, vol. 15(2), pages 186-202, June.
- Ye, Yuan & Lu, Yonggang & Robinson, Powell & Narayanan, Arunachalam, 2022. "An empirical Bayes approach to incorporating demand intermittency and irregularity into inventory control," European Journal of Operational Research, Elsevier, vol. 303(1), pages 255-272.
- Tian, Xin & Wang, Haoqing & E, Erjiang, 2021. "Forecasting intermittent demand for inventory management by retailers: A new approach," Journal of Retailing and Consumer Services, Elsevier, vol. 62(C).
- Zhu, Sha & Jaarsveld, Willem van & Dekker, Rommert, 2020.
"Spare parts inventory control based on maintenance planning,"
Reliability Engineering and System Safety, Elsevier, vol. 193(C).
- Zhu, S. & van Jaarsveld, W.L. & Dekker, R., 2019. "Spare Parts Inventory Control based on Maintenance Planning," Econometric Institute Research Papers EI2019-02, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
- Roßmann, Bernhard & Canzaniello, Angelo & von der Gracht, Heiko & Hartmann, Evi, 2018. "The future and social impact of Big Data Analytics in Supply Chain Management: Results from a Delphi study," Technological Forecasting and Social Change, Elsevier, vol. 130(C), pages 135-149.
- Lolli, F. & Gamberini, R. & Regattieri, A. & Balugani, E. & Gatos, T. & Gucci, S., 2017. "Single-hidden layer neural networks for forecasting intermittent demand," International Journal of Production Economics, Elsevier, vol. 183(PA), pages 116-128.
- Zhu, Sha & Dekker, Rommert & van Jaarsveld, Willem & Renjie, Rex Wang & Koning, Alex J., 2017. "An improved method for forecasting spare parts demand using extreme value theory," European Journal of Operational Research, Elsevier, vol. 261(1), pages 169-181.
- Xuan Bi & Gediminas Adomavicius & William Li & Annie Qu, 2022. "Improving Sales Forecasting Accuracy: A Tensor Factorization Approach with Demand Awareness," INFORMS Journal on Computing, INFORMS, vol. 34(3), pages 1644-1660, May.
- Svetunkov, Ivan & Boylan, John E., 2023. "iETS: State space model for intermittent demand forecasting," International Journal of Production Economics, Elsevier, vol. 265(C).
More about this item
Keywords
Demand forecasting; Fashion retail; Case study; Big data;All these keywords.
Statistics
Access and download statisticsCorrections
All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:annopr:v:291:y:2020:i:1:d:10.1007_s10479-019-03148-8. See general information about how to correct material in RePEc.
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .
Please note that corrections may take a couple of weeks to filter through the various RePEc services.