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Forecasting demand for single-period products: A case study in the apparel industry

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  • Mostard, Julien
  • Teunter, Ruud
  • de Koster, René

Abstract

The problem considered is that of forecasting demand for single-period products before the period starts. We study this problem for the case of a mail order apparel company that needs to order its products pre-season. The lack of historical demand data implies that other sources of data are needed. Advance order data can be obtained by allowing a selected group of customers to pre-order at a discount from a preview catalogue. Judgments can be obtained from purchase managers or other company experts. In this paper, we compare several existing and new forecasting methods for both sources of data. The methods are generic and can be used in any single-period problem in the apparel or fashion industries. Among the pre-order based methods, a novel 'top-flop' approach provides promising results. For a small group of products from the case company, expert judgment methods perform better than the methods based on advance demand information. The comparative results are obviously restricted to the specific case study, and additional testing is required to determine whether they are valid in general.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:ejores:v:211:y:2011:i:1:p:139-147
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    1. Madhukar Nagare & Pankaj Dutta & Naoufel Cheikhrouhou, 2016. "Optimal ordering policy for newsvendor models with bidirectional changes in demand using expert judgment," OPSEARCH, Springer;Operational Research Society of India, vol. 53(3), pages 620-647, September.
    2. Rina Tanaka & Aya Ishigaki & Tomomichi Suzuki & Masato Hamada & Wataru Kawai, 2019. "Data Analysis of Shipment for Textiles and Apparel from Logistics Warehouse to Store Considering Disposal Risk," Sustainability, MDPI, vol. 11(1), pages 1-14, January.
    3. 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.
    4. Halkos, George & Kevork, Ilias, 2012. "Unbiased estimation of maximum expected profits in the Newsvendor Model: a case study analysis," MPRA Paper 40724, University Library of Munich, Germany.
    5. Fildes, Robert & Ma, Shaohui & Kolassa, Stephan, 2022. "Retail forecasting: Research and practice," International Journal of Forecasting, Elsevier, vol. 38(4), pages 1283-1318.
    6. Sascha Kurz & Jörg Rambau & Jörg Schlüchtermann & Rainer Wolf, 2015. "The Top-Dog Index: a new measurement for the demand consistency of the size distribution in pre-pack orders for a fashion discounter with many small branches," Annals of Operations Research, Springer, vol. 229(1), pages 541-563, June.
    7. Shuyun Ren & Hau-Ling Chan & Tana Siqin, 2020. "Demand forecasting in retail operations for fashionable products: methods, practices, and real case study," Annals of Operations Research, Springer, vol. 291(1), pages 761-777, August.
    8. Fildes, Robert & Ma, Shaohui & Kolassa, Stephan, 2019. "Retail forecasting: research and practice," MPRA Paper 89356, University Library of Munich, Germany.
    9. Hong, Jungsik & Koo, Hoonyoung & Kim, Taegu, 2016. "Easy, reliable method for mid-term demand forecasting based on the Bass model: A hybrid approach of NLS and OLS," European Journal of Operational Research, Elsevier, vol. 248(2), pages 681-690.
    10. Nguyen, Duy Tan & Adulyasak, Yossiri & Landry, Sylvain, 2021. "Research manuscript: The Bullwhip Effect in rule-based supply chain planning systems–A case-based simulation at a hard goods retailer," Omega, Elsevier, vol. 98(C).
    11. 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.
    12. Feuerriegel, Stefan & Gordon, Julius, 2019. "News-based forecasts of macroeconomic indicators: A semantic path model for interpretable predictions," European Journal of Operational Research, Elsevier, vol. 272(1), pages 162-175.
    13. Koppius, O.R. & Ozdemir, O. & van der Laan, E.A., 2011. "Beyond Waste Reduction: Creating Value with Information Systems in Closed-Loop Supply Chains," ERIM Report Series Research in Management ERS-2011-024-LIS, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.
    14. Mei, Wanxia & Du, Li & Niu, Baozhuang & Wang, Jincheng & Feng, Jiejian, 2016. "The effects of an undisclosed regular price and a positive leadtime in a presale mechanism," European Journal of Operational Research, Elsevier, vol. 250(3), pages 1013-1025.
    15. Bai, Qingguo & Xu, Jianteng & Gong, Yeming & Chauhan, Satyaveer S., 2022. "Robust decisions for regulated sustainable manufacturing with partial demand information: Mandatory emission capacity versus emission tax," European Journal of Operational Research, Elsevier, vol. 298(3), pages 874-893.

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