IDEAS home Printed from https://ideas.repec.org/r/eee/intfor/v38y2022i4p1319-1324.html

Post-script—Retail forecasting: Research and practice

Citations

Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
as


Cited by:

  1. 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.
  2. Feddersen, Leif & Cleophas, Catherine, 2026. "Hierarchical neural additive models for interpretable demand forecasts," International Journal of Forecasting, Elsevier, vol. 42(1), pages 216-234.
  3. Marco Zanotti, 2025. "On the stability of global forecasting models," Working Papers 553, University of Milano-Bicocca, Department of Economics.
  4. Sagaert, Yves R. & Kourentzes, Nikolaos, 2025. "Inventory management with leading indicator augmented hierarchical forecasts," Omega, Elsevier, vol. 136(C).
  5. 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.
  6. Marco Zanotti, 2025. "Do global forecasting models require frequent retraining?," Working Papers 551, University of Milano-Bicocca, Department of Economics.
  7. 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.
  8. 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.
  9. 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.
  10. 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.
  11. 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.
  12. Marco Zanotti, 2025. "The cost of ensembling: is it always worth combining?," Working Papers 554, University of Milano-Bicocca, Department of Economics.
  13. 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).
  14. 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).
  15. Harrison Katz, 2026. "Directional-Shift Dirichlet ARMA Models for Compositional Time Series with Structural Break Intervention," Papers 2601.16821, arXiv.org, revised Apr 2026.
  16. 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).
  17. 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.
  18. 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.
  19. 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.
  20. 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.
  21. 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.
  22. 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).
  23. 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.
  24. 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.
  25. Kolassa, Stephan, 2022. "Commentary on the M5 forecasting competition," International Journal of Forecasting, Elsevier, vol. 38(4), pages 1562-1568.
  26. 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.
  27. 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.
IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.