Skip to main content
SpringerLink
Log in

Forecasting a Fashion Collection with the Optimization of Costs of Overestimation and Underestimation of Demand

  • Conference paper
  • First Online:
Book cover 16th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2021) (SOCO 2021)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1401))

  • 999 Accesses

Abstract

The article presents a real prognostic algorithm used in the fashion industry. The method merges an econometric approach with solutions used in operations research. The Weighted Least Squares (WLS) method with constraints was used, combined with an algorithm for optimizing losses resulting from overestimating or underestimating demand.

The theoretical basis of the algorithm was presented, and its arrangement in the form of a step-by-step procedure was established. An example of the procedure was then calculated, and the numerical complexity class was estimated.

The order prediction algorithm represents a classical approach that can be used in any prognostic problem in which the features of the product and the sales history are known. The approach is especially dedicated when the estimated demand concerns new products with features that have already appeared in previous sales.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 219.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 279.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    This is a part of the Project implemented as part of the Program Operacyjny Inteligentny Rozwój 2014–2020 of Narodowe Centrum Badań i Rozwoju. This is a part of the Project title: “Demand forecasting system controlled by dynamic fash-ion trends for clothing, textile and, lingerie enterprises”. Number of application: POIR.01.01.01-00-0886/17-00.

References

  1. Bukietyńska A., Czekała M., Gurak M., Jagodziński J. Kłosowski J.: Estimation of the probability of inversion in assessing the credibility of experts. A case study. Math. Econ. 20(13), 5–14 (2017)

    Google Scholar 

  2. Chen, R.R., Cheng, T.C.E., Choi, T.M., Wang, Y.: Novel advances in applications of the newsvendor model. Decis. Sci. 47, 8 (2016). https://doi.org/10.1111/deci.12215

    Article  Google Scholar 

  3. Czekała, M., Bukietyńska, A., Gurak, M., Jagodziński, J., Kłosowski, J.: Condition analysis and forecasting in the fashion industry. Int. J. Econ. Financ. Manag. Sci. 7(2), 74–81 (2019)

    Google Scholar 

  4. Czekała, M., Bukietyńska, A., Gurak, M., Jagodziński, J., Kłosowski, J.: Extrapolation of maxima with application in chi square test. In: Wilimowska, Z., Borzemski, L., Świątek, J. (eds.) ISAT 2019. AISC, vol. 1052, pp. 36–48. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-30443-0_4

    Chapter  Google Scholar 

  5. Gustafsson A., Herrmann A., Huber F. (ed.), Conjoint Measurement. Methods and Application, 4th edn. Springer, Heidelberg (2007). https://doi.org/10.1007/978-3-540-71404-0

  6. Green P.E., Krieger A.M., Wind Y.: Thirty years of conjoint analysis: reflections and prospects. Interfaces 31(3) supplement, S56–S73 (2001)

    Google Scholar 

  7. Green P.E., Srinivasan V.: Conjoint analysis in consumer research: issues and outlook. J. Consum. Res. 5, 103–123 (1978)

    Google Scholar 

  8. Hillier, F.S., Lieberman, G.: Introduction to Stochastic Models in Operational Research. McGraw-Hill Book, New York (1990)

    Google Scholar 

  9. Şen, A., Zhang, A.X.: The newsboy problem with multiple demand classes. IIE Trans. 31, 431–444 (1999)

    Google Scholar 

  10. Theil, H.: Principles of Econometrics. Wiley, New York (1971)

    MATH  Google Scholar 

  11. Tveit, A.: On the complexity of matrix inversion. Math. Note p. 1 (2003). https://www.researchgate.net/publication/240954906_On_the_Complexity_of_Matrix_Inversion

  12. Williams V.V.: Multiplying matrices faster than coppersmith-winograd. In: Proceedings of the Forty-Fourth Annual ACM Symposium on Theory of Computing (STOC 2012). Association for Computing Machinery, New York, NY, USA, pp. 887–898 (2012)

    Google Scholar 

  13. Yan, X., Wang, Y.: A newsvendor model with capital constraint and demand forecast update. Int. J. Prod. Res. 52, 17 (2014)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Wroclaw School of Banking, Finance and Management Department, Wroclaw, Poland

    Mariusz Czekała

  2. Department of Control Systems and Mechatronics, Wroclaw University of Science and Technology, Wroclaw, Poland

    Jacek Jagodziński

  3. MG4-LTD Co., Wroclaw, Poland

    Jakub Przybylski, Dawid Zaraza & Karol Ziółkowski

Authors
  1. Mariusz Czekała
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jacek Jagodziński
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jakub Przybylski
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Dawid Zaraza
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Karol Ziółkowski
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jacek Jagodziński .

Editor information

Editors and Affiliations

  1. Faculty of Engineering, University of Deusto, Bilbao, Spain

    Hugo Sanjurjo González

  2. Faculty of Engineering, University of Deusto, Bilbao, Spain

    Iker Pastor López

  3. Faculty of Engineering, University of Deusto, Bilbao, Spain

    Pablo García Bringas

  4. Department of Industrial Engineering, University of A Coruña, Ferrol, Spain

    Héctor Quintián

  5. BISITE Research Group, University of Salamanca, Salamanca, Spain

    Emilio Corchado

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Czekała, M., Jagodziński, J., Przybylski, J., Zaraza, D., Ziółkowski, K. (2022). Forecasting a Fashion Collection with the Optimization of Costs of Overestimation and Underestimation of Demand. In: Sanjurjo González, H., Pastor López, I., García Bringas, P., Quintián, H., Corchado, E. (eds) 16th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2021). SOCO 2021. Advances in Intelligent Systems and Computing, vol 1401. Springer, Cham. https://doi.org/10.1007/978-3-030-87869-6_61

Download citation

Publish with us

Policies and ethics

Search

Navigation