KONFEKSİYON ENDÜSTRİSİNDE PERAKENDE TALEP TAHMİNLEMESİ

Tahmin gelecekteki çıktıları belirleme bilimidir ve iş hayatında bir işin, ürünün ya da sektörün geleceğe dönük hedeflerini belirlemede kullanılır. İş hayatında, yeni ürün veya ürün hatları geliştirmeden önce uygun bir tahmin yapmak pazarda başarısız bir ürün geliştirmeyi, zaman ve para harcamayı önlemede son derece önemlidir. Bu çalışmada Türkiye’de tanınmış bir perakende markasının geçmiş satış verileri baz alınarak üç farklı nicel tahminleme yöntemi ile, basit hareketli ortalama modeli, ağırlıklı hareketli ortalama modeli ve doğrusal eğilim modeli, kullanılarak satış tahminlemesi yapılmıştır

RETAIL DEMAND FORECASTING IN CLOTHING INDUSTRY

Forecasting is the science of predicting future outcomes and, it is used to determine the future targets of a business, product, or industry in business life. It is extremely important for a business to do proper forecasting before developing new products or product lines and prevents spending a lot of time and money for developing a product that fails in the marketplace. In this study, three different quantitative forecasting models, simple moving average model, weighted moving average model and linear trend model are applied by using the past sales data of a well-known retailing brand in Turkey for forecasting sales

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  • 1. Levy, M. and Weitz, B.A., (1992), Retailing management, Von Hoffman Press,Inc., USA, pp. 5-10.
  • 2. Wolfe, M., (2009), Fashion marketing & merchandising, The Goodheart-Willcox Company, Inc., Ilinois, p.238.
  • 3. Gilliland, M., (2003), Alternative metrics for forecasting performance, The Journal of Business Forecasting Methods & Systems, 22, pp. 17-20.
  • 4. Fildes, R. and Goodwin, P., (2008), Forecasting, Financial Management.
  • 5. Brannon, E.L., (2005), Fashion forecasting, Fairchild Publications, INC, pp. 167-184, New York.
  • 6. Makridakis, S., Wheelwright, S.C. and McGee, V.E., (1983), Forecasting, methods and applications, Wiley, New York.
  • 7. Chen, Y.L., Chen, J.M. and Tung, C.W., (2006), A data mining approach for retail knowledge discovery with consideration of the effect of shelf-space adjacency on sales,Decision Support Systems, 42, pp.1503–1520.
  • 8. Shaw, M.J., Subramaniam, C., Tan, G.W. and Welge, M.E., (2001), Knowledge management and data mining for marketing, Decision Support Systems, 31, pp.127–137.
  • 9. Wisner, J.D., Leong, G.K. and Tan, K., (2005), Principles of supply chain management, Thomson South-Western, Maryland, pp.125-148.
  • 10. Sebastien Thomassey, Antonio Fiordaliso, (2006), A hybrid sales forecasting system based on clustering and decision trees, Decision Support Systems, 42, 408– 421.