@inbook{f289f40a695f4796bd7823dd55221f50,
title = "Fuzzy methods for demand forecasting in supply chain management",
abstract = "Forecasting the future demand is crucial for supply chain planning. In this chapter, the fuzzy methods that can be used to forecast future by historical demand information are explained. The examined methods include fuzzy time series, fuzzy regression, adaptive network-based fuzzy inference system and fuzzy rule based systems. The literature review is given and the methods are introduced for the mentioned methods. Also two numerical applications using fuzzy time series are presented. In one of the examples, future enrollments of a university is forecasted using Hwang, Chen and Lee's study and in the other example a company's oil consumption is predicted using Singh's algorithm. Finally, the forecasting accuracy of the methods is determined by using Mean Absolute Error (MAE).",
keywords = "Adaptive network-based fuzzy inference system, Fuzzy forecasting, Fuzzy regression, Fuzzy rule based systems, Fuzzy time series",
author = "Ba{\c s}ar {\"O}ztay{\c s}i and Eda Bolturk",
year = "2014",
doi = "10.1007/978-3-642-53939-8_11",
language = "English",
isbn = "9783642539381",
series = "Studies in Fuzziness and Soft Computing",
publisher = "Springer Verlag",
pages = "243--268",
booktitle = "Supply Chain Management Under Fuzziness",
address = "Germany",
}