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• W2891769144 abstract "Nowadays, good supply chain management is most important to guarantee a competitive advantage and to accomplish the value promise offered to the company’s clients. To this end, it is important to reduce uncertainty associated with demand, and it is important that demand forecast is as accurate as possible. To achieve this, it is necessary to know the features of the demand to be forecast and, based on this, to build or choose the best and the most accurate model or technique, which is based normally on that with fewer errors. Many statistical techniques exist, but for some 20 years, many heuristic algorithms have been developed that allow to absorb the variance associated with demand, and to reduce forecasting errors with better results than those obtained by statistical methods. These methods are normally adaptive and allow to hybridize techniques to construct different models. This document reviews these adaptive techniques, such as neural networks (NN) and hybrid methods, and in particular models based on case-based reasoning (CBR)." @default.
• W2891769144 created "2018-09-27" @default.
• W2891769144 creator A5009412588 @default.
• W2891769144 creator A5009888309 @default.
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• W2891769144 date "2018-09-19" @default.
• W2891769144 modified "2023-09-24" @default.
• W2891769144 title "Adaptive and Hybrid Forecasting Models—A Review" @default.
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• W2891769144 doi "https://doi.org/10.1007/978-3-319-96005-0_38" @default.
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