Sucky, Eric2022-04-152022-04-152012Aksoy, A. vd. (2012). "A decision support system for demand forecasting in the clothing industry". International Journal of Clothing Science and Technology, 24(4), 221-236.0955-62221758-5953https://doi.org/10.1108/09556221211232829https://www.emerald.com/insight/content/doi/10.1108/09556221211232829/full/htmlhttp://hdl.handle.net/11452/25793Purpose - 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.eninfo:eu-repo/semantics/closedAccessMaterials scienceDemand forecastingClothing manufacturerNeuro-fuzzy techniquesClothingArtificial neural-networksSupply chainIntegrationManagementImpactArticle0003089028000042-s2.0-84864453354221236244Materials science, textilesSales Forecasting; Fast Fashion; Retail