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Forecasting Preliminary Order Cost to Increase Order Management Performance: A Case Study in the Apparel Industry

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Listed:
  • Tüzin Akçinar Günsari

    (TYH Textile, Turkey)

  • Aysegül Kaya

    (TYH Textile, Turkey)

  • Yeliz Ekinci

    (İstanbul Bilgi University, Turkey)

Abstract

In this study, the cost estimation to be used in the optimization of proposed order price offer is made by artificial neural network (ANN) method. A case study is performed by the real data of a company, and the forecast results of the traditional arithmetic model used by the company and the proposed ANN based method are compared and it is seen that the proposed method results outperform the other. The biggest contribution of this study to companies is to increase the company’s order management performance by helping the company to make more accurate pricing due to more accurate cost estimation. Moreover, to the best of our knowledge, this is the first study on forecasting preliminary order cost in the apparel industry and fills an important gap in the literature.

Suggested Citation

  • Tüzin Akçinar Günsari & Aysegül Kaya & Yeliz Ekinci, 2022. "Forecasting Preliminary Order Cost to Increase Order Management Performance: A Case Study in the Apparel Industry," International Journal of Business Analytics (IJBAN), IGI Global, vol. 9(5), pages 1-15, January.
    • Handle: RePEc:igg:jban00:v:9:y:2022:i:5:p:1-15
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    References listed on IDEAS

    as
    1. Adhikari, Arnab & Bisi, Arnab & Avittathur, Balram, 2020. "Coordination mechanism, risk sharing, and risk aversion in a five-level textile supply chain under demand and supply uncertainty," European Journal of Operational Research, Elsevier, vol. 282(1), pages 93-107.
    2. Tor Guimaraes & Ketan Paranjape, 2021. "Assessing the Overall Impact of Data Analytics on Company Decision Making and Innovation," International Journal of Business Analytics (IJBAN), IGI Global, vol. 8(4), pages 34-51, October.
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