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Retail forecasting: Research and practice

Citations

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Cited by:

  1. Carl-Christian Groh, 2024. "Big Data and Inequality," CRC TR 224 Discussion Paper Series crctr224_2024_555, University of Bonn and University of Mannheim, Germany.
  2. Spiliotis, Evangelos & Petropoulos, Fotios, 2024. "On the update frequency of univariate forecasting models," European Journal of Operational Research, Elsevier, vol. 314(1), pages 111-121.
  3. Elisabeth Obermair & Andreas Holzapfel & Heinrich Kuhn, 2023. "Operational planning for public holidays in grocery retailing - managing the grocery retail rush," Operations Management Research, Springer, vol. 16(2), pages 931-948, June.
  4. Dazhou Lei & Yongzhi Qi & Sheng Liu & Dongyang Geng & Jianshen Zhang & Hao Hu & Zuo-Jun Max Shen, 2025. "Pooling and Boosting for Demand Prediction in Retail: A Transfer Learning Approach," Manufacturing & Service Operations Management, INFORMS, vol. 27(6), pages 1779-1794, November.
  5. Alexandra Birkmaier & Adhurim Imeri & Gerald Reiner, 2024. "Improving supply chain planning for perishable food: data-driven implications for waste prevention," Journal of Business Economics, Springer, vol. 94(6), pages 1-36, August.
  6. Fahimnia, Ben & Tan, Tarkan & Tahirov, Nail, 2025. "Service-level anchoring in demand forecasting: The moderating impact of retail promotions and product perishability," International Journal of Forecasting, Elsevier, vol. 41(2), pages 554-570.
  7. Theodorou, Evangelos & Spiliotis, Evangelos & Assimakopoulos, Vassilios, 2025. "Forecast accuracy and inventory performance: Insights on their relationship from the M5 competition data," European Journal of Operational Research, Elsevier, vol. 322(2), pages 414-426.
  8. Long, Xueying & Bui, Quang & Oktavian, Grady & Schmidt, Daniel F. & Bergmeir, Christoph & Godahewa, Rakshitha & Lee, Seong Per & Zhao, Kaifeng & Condylis, Paul, 2025. "Scalable probabilistic forecasting in retail with gradient boosted trees: A practitioner’s approach," International Journal of Production Economics, Elsevier, vol. 279(C).
  9. Fernando, Angeline Gautami & Aw, Eugene Cheng-Xi, 2023. "What do consumers want? A methodological framework to identify determinant product attributes from consumers’ online questions," Journal of Retailing and Consumer Services, Elsevier, vol. 73(C).
  10. M. Harshvardhan & Cara Curtland & Jerry Hwang & Chuck VanDam & Adam Ghozeil & Pedro A. Neto & Frederic Marie & Chuanren Liu, 2025. "Print Demand Forecasting with Machine Learning at HP Inc," Interfaces, INFORMS, vol. 55(6), pages 469-483, November.
  11. Hunneman, Auke & Bijmolt, Tammo H.A. & Elhorst, J. Paul, 2023. "Evaluating store location and department composition based on spatial heterogeneity in sales potential," Journal of Retailing and Consumer Services, Elsevier, vol. 73(C).
  12. Khosrowabadi, Naghmeh & Hoberg, Kai & Imdahl, Christina, 2022. "Evaluating human behaviour in response to AI recommendations for judgemental forecasting," European Journal of Operational Research, Elsevier, vol. 303(3), pages 1151-1167.
  13. Harrison Katz, 2026. "Directional-Shift Dirichlet ARMA Models for Compositional Time Series with Structural Break Intervention," Papers 2601.16821, arXiv.org, revised Jun 2026.
  14. Ge, Xianlong & Yin, Qiushuang & Moktadir, Md. Abdul & Ren, Jingzheng, 2025. "Dynamic routing optimization of electric vehicles for retailers based on consumer behavior prediction," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 204(C).
  15. Saccomanno, Francesco Paolo & Trivella, Alessio & Guerriero, Francesca, 2026. "Integrated sales planning for in-store retail: A multi-stage stochastic optimization approach," European Journal of Operational Research, Elsevier, vol. 329(2), pages 669-686.
  16. Feddersen, Leif & Cleophas, Catherine, 2026. "Hierarchical neural additive models for interpretable demand forecasts," International Journal of Forecasting, Elsevier, vol. 42(1), pages 216-234.
  17. 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.
  18. Paul MUKUCHA & Divaries Cosmas JARAVAZA & Fanny SARUCHERA, 2025. "Strategic Postponement In Fast Food Operations: Enhancing Order Fulfilment In A Frontier Emerging Market," Business Excellence and Management, Faculty of Management, Academy of Economic Studies, Bucharest, Romania, vol. 15(2), pages 5-18, June.
  19. Thais de Castro Moraes & Xue‐Ming Yuan & Ek Peng Chew, 2024. "Hybrid convolutional long short‐term memory models for sales forecasting in retail," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(5), pages 1278-1293, August.
  20. Marco Zanotti, 2025. "On the stability of global forecasting models," Working Papers 553, University of Milano-Bicocca, Department of Economics.
  21. Juan Pablo Fernández-Gutiérrez & Juan G. Villegas & José-Fernando Camacho-Vallejo, 2026. "A simple and effective exact method for the medianoid problem with multipurpose trips," Computational Optimization and Applications, Springer, vol. 93(2), pages 851-897, March.
  22. Ma, Shaohui & Fildes, Robert, 2022. "The performance of the global bottom-up approach in the M5 accuracy competition: A robustness check," International Journal of Forecasting, Elsevier, vol. 38(4), pages 1492-1499.
  23. Makridakis, Spyros & Spiliotis, Evangelos & Assimakopoulos, Vassilios & Chen, Zhi & Gaba, Anil & Tsetlin, Ilia & Winkler, Robert L., 2022. "The M5 uncertainty competition: Results, findings and conclusions," International Journal of Forecasting, Elsevier, vol. 38(4), pages 1365-1385.
  24. David Winkelmann & Theresa Elbracht & Jonas Brenker & Arnold Gerzen, 2026. "Discounted Sales of Expiring Perishables: Challenges for Demand Forecasting in Grocery Retail Practice," Papers 2602.04464, arXiv.org.
  25. 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.
  26. Sagaert, Yves R. & Kourentzes, Nikolaos, 2025. "Inventory management with leading indicator augmented hierarchical forecasts," Omega, Elsevier, vol. 136(C).
  27. Kolassa, Stephan, 2022. "Commentary on the M5 forecasting competition," International Journal of Forecasting, Elsevier, vol. 38(4), pages 1562-1568.
  28. Abolghasemi, Mahdi & Ganbold, Odkhishig & Rotaru, Kristian, 2025. "Humans vs. large language models: Judgmental forecasting in an era of advanced AI," International Journal of Forecasting, Elsevier, vol. 41(2), pages 631-648.
  29. Marco Zanotti, 2025. "The cost of ensembling: is it always worth combining?," Working Papers 554, University of Milano-Bicocca, Department of Economics.
  30. Theodorou, Evangelos & Wang, Shengjie & Kang, Yanfei & Spiliotis, Evangelos & Makridakis, Spyros & Assimakopoulos, Vassilios, 2022. "Exploring the representativeness of the M5 competition data," International Journal of Forecasting, Elsevier, vol. 38(4), pages 1500-1506.
  31. Zhang, Bohan & Kang, Yanfei & Panagiotelis, Anastasios & Li, Feng, 2023. "Optimal reconciliation with immutable forecasts," European Journal of Operational Research, Elsevier, vol. 308(2), pages 650-660.
  32. Ye, Lili & Xie, Naiming & Boylan, John E. & Shang, Zhongju, 2024. "Forecasting seasonal demand for retail: A Fourier time-varying grey model," International Journal of Forecasting, Elsevier, vol. 40(4), pages 1467-1485.
  33. Oleksandr Shchur & Abdul Fatir Ansari & Caner Turkmen & Lorenzo Stella & Nick Erickson & Pablo Guerron-Quintana & Michael Bohlke-Schneider & Yuyang Wang, 2025. "fev-bench: A Realistic Benchmark for Time Series Forecasting," Boston College Working Papers in Economics 1101, Boston College Department of Economics.
  34. Marco Zanotti, 2025. "Do global forecasting models require frequent retraining?," Working Papers 551, University of Milano-Bicocca, Department of Economics.
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