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A decision support system for demand forecasting in the clothing industry

Asli Aksoy (Industrial Engineering Department, Uludag University, Bursa, Turkey)
Nursel Ozturk (Industrial Engineering Department, Uludag University, Bursa, Turkey)
Eric Sucky (Production and Logistics Department, Otto‐Friedrich University, Bamberg, Germany)

International Journal of Clothing Science and Technology

ISSN: 0955-6222

Article publication date: 27 July 2012

2850

Abstract

Purpose

Demand forecasting in the clothing industry is very complex due to the existence of a wide range of product references and the lack of historical sales data. To the authors' knowledge, there is an inadequate number of literature studies to forecast the demand with the adaptive network based fuzzy inference system for the clothing industry. The purpose of this paper is to construct a decision support system for demand forecasting in the clothing industry.

Design/methodology/approach

The adaptive‐network‐based fuzzy inference system (ANFIS) is used for forecasting demand in the clothing industry.

Findings

The results of the proposed study showed that an ANFIS‐based demand forecasting system can help clothing manufacturers to forecast demand more accurately, effectively and simply.

Originality/value

In this study, the demand is forecast in terms of clothing manufacturers by using ANFIS. ANFIS is a new technique for demand forecasting, it combines the learning capability of the neural networks and the generalization capability of the fuzzy logic. The input and output criteria are determined based on clothing manufacturers' requirements and via literature research, and the forecasting horizon is about one month. The study includes the real life application of the proposed system and the proposed system is tested by using real demand values for clothing manufacturers.

Keywords

Citation

Aksoy, A., Ozturk, N. and Sucky, E. (2012), "A decision support system for demand forecasting in the clothing industry", International Journal of Clothing Science and Technology, Vol. 24 No. 4, pp. 221-236. https://doi.org/10.1108/09556221211232829

Publisher

:

Emerald Group Publishing Limited

Copyright © 2012, Emerald Group Publishing Limited

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