Machine Learning and Statistics: A Study for assessing innovative Demand Forecasting Models

被引：19

作者：

Moroff, Nikolas Ulrich ^{[1
]}

Kurt, Ersin ^{[1
]}

Kamphues, Josef ^{[1
]}

机构：

[1] Fraunhofer Inst Mat Flow & Logist, Joseph von Fraunhofer Str 2-4, D-44227 Dortmund, Germany

来源：

PROCEEDINGS OF THE 2ND INTERNATIONAL CONFERENCE ON INDUSTRY 4.0 AND SMART MANUFACTURING (ISM 2020) | 2021年 / 180卷

关键词：

Demand Forecast; Machine Learning; Statistical Methods; Deep Learning;

D O I：

10.1016/j.procs.2021.01.127

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Besides increasing dynamics in market demands, companies strive to avoid short-term changes in their supply chain planning. Therefore, an essential lever to improve supply chain performance is the optimization of the demand forecast. In this regard, artificial intelligence is a widely adopted technique in Industry 4.0 that is associated with high expectations. Against this background, six different forecasting models from statistics and machine learning were evaluated in respect to forecast quality and effort for implementation. The results underline the potential of innovative forecasting models as well as the necessity for an intensive and application-specific evaluation of the advantages and disadvantages of the available approaches. (C) 2021 The Authors. Published by Elsevier B.V.

引用

页码：40 / 49

页数：10

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