Machine Learning Applications in Supply Chains: Long Short-Term Memory for Demand Forecasting

被引:7
作者
Bousqaoui, Halima [1 ]
Achchab, Said [1 ]
Tikito, Kawtar [2 ]
机构
[1] Mohammed V Univ, Natl Higher Sch Comp Sci & Syst Anal ENSIAS, Rabat, Morocco
[2] Mines Natl Higher Sch ENSMR, Rabat, Morocco
来源
CLOUD COMPUTING AND BIG DATA: TECHNOLOGIES, APPLICATIONS AND SECURITY | 2019年 / 49卷
关键词
ARTIFICIAL NEURAL-NETWORK; VECTOR MACHINE; FUZZY; MODEL; CLASSIFICATION; SELECTION; SYSTEM; TIME; METHODOLOGY; INTEGRATION;
D O I
10.1007/978-3-319-97719-5_19
中图分类号
TP301 [理论、方法];
学科分类号
081202 ;
摘要
Due to the rapid technological advances, machine Learning or the ability of a machine to learn automatically has found applications in various fields. It has proven to be a valuable tool for aiding decision makers and improving the productivity of enterprise processes, due to its ability to learn and find interesting patterns in the data. Thereby, it is possible to improve supply chains processes by using Machine Learning which generates in general better forecasts than the traditional approaches. As such, this chapter examines multiple Machine Learning algorithms, explores their applications in the various supply chain processes, and presents a long short-term memory model for predicting the daily demand in a Moroccan supermarket.
引用
收藏
页码:301 / 317
页数:17
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