Data-Driven Resilient Supply Management Supported by Demand Forecasting

被引：1
|
作者：
Grzegorowski, Marek [1 ]
Janusz, Andrzej [1 ,2 ]
Litwin, Jaroslaw [2 ]
Marcinowski, Lukasz [3 ]
机构：
[1] Univ Warsaw, Inst Informat, Banacha 2, PL-02097 Warsaw, Poland
[2] QED Software Sp Zoo, Warsaw, Poland
[3] FitFood Sp Zoo, Krakow, Poland
来源：
RECENT CHALLENGES IN INTELLIGENT INFORMATION AND DATABASE SYSTEMS, ACIIDS 2022 | 2022年 / 1716卷
关键词：
Time series; ML; Data-driven supply management; FMCG;
D O I：
10.1007/978-981-19-8234-7_10
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
The article discusses several challenges related to resilient supply management and demand forecasting. Both of those topics are of great importance for food retailers and producers who aim at reducing the risk of lost sales opportunities and food waste. In the investigated case study of FitBoxY.com, due to the overestimated demand and too large deliveries, historically, even 30% of the products were overdue. The developed ML framework integrated with the supply management system enabled optimization of business costs and reduced food waste from overestimated demand. The experimental evaluation showed that, with the developed solution, it is possible to improve demand forecasting by nearly 50% compared to estimates proposed by human operators.
引用
收藏
页码：122 / 134
页数：13
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