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- Title
BÜTÜNLEŞİK ZAMAN SERİSİ ANALİZİ İLE TALEP TAHMİNİ: İLAÇ TEDARİK ZİNCİRİNDE BİR UYGULAMA.
- Authors
SARI, Tuğba; GÜL, Bünyamin Salih
- Abstract
Purpose: In order for the supply chain activities of a product to be carried out efficiently, the future demand for that product must be accurately forecasted. In this study, it is aimed to make a forecast for the future demand of a product by analyzing the sales data of the past periods in the pharmaceutical industry. Methodology: Within the scope of the study, 36-month sales data of a pharmaceutical product produced in Türkiye were analyzed by ARIMA, Holt-Winters exponential smoothing and integrated artificial neural networks (ANN) models. Findings: The results reveal that all these methods yield quite low forecasting errors. In between these three methods, the one that gives the most accurate forecasting result is the integrated ANN model. Originality: This study differs from existing studies in terms of both the method and the data set used. It is anticipated that the study will contribute to the demand forecasting studies in the pharmaceutical industry which is limited in the literature and will provide support in supply chain management decisions due to its practical applicability.
- Publication
Verimlilik Dergisi, 2022, Issue 4, p597
- ISSN
1013-1388
- Publication type
Article
- DOI
10.51551/verimlilik.1091150