Demand Forecasting of a Company that Produces by Mass Customization with Machine Learning

Engin Yağcıoğlu, Ahmet Tezcan Tekin*, Ferhan Çebi

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Özet

Machine Learning (ML) algorithms are designed to extract information from existing data. The application of ML in production; can provide the acquisition of new information from existing data sets that can form a basis for the development of approaches about how the system should be in the future. This further information can support company managers in their decision-making processes or can be used directly to improve the system. Given the challenge of a rapidly changing and dynamic production environment, ML; As part of artificial intelligence, it can learn about changes and adapt to them. Mass customization; recently, has started to influence the textile sector as in many sectors. As A result of changing consumer habits and developing technology; companies have begun to focus on this area to meet the increasing number of mass customized demands.This study aims to make demand estimation by using ML algorithms of a textile workshop that performs mass customization. The results show that ML algorithms have the result of successful demand forecast in organizations implementing mass customization when there is enough data.

Orijinal dilİngilizce
Ana bilgisayar yayını başlığıIntelligent and Fuzzy Techniques for Emerging Conditions and Digital Transformation - Proceedings of the INFUS 2021 Conference
EditörlerCengiz Kahraman, Selcuk Cebi, Sezi Cevik Onar, Basar Oztaysi, A. Cagri Tolga, Irem Ucal Sari
YayınlayanSpringer Science and Business Media Deutschland GmbH
Sayfalar197-204
Sayfa sayısı8
ISBN (Basılı)9783030855765
DOI'lar
Yayın durumuYayınlandı - 2022
EtkinlikInternational Conference on Intelligent and Fuzzy Systems, INFUS 2021 - Istanbul, Turkey
Süre: 24 Ağu 202126 Ağu 2021

Yayın serisi

AdıLecture Notes in Networks and Systems
Hacim308
ISSN (Basılı)2367-3370
ISSN (Elektronik)2367-3389

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???event.eventtypes.event.conference???International Conference on Intelligent and Fuzzy Systems, INFUS 2021
Ülke/BölgeTurkey
ŞehirIstanbul
Periyot24/08/2126/08/21

Bibliyografik not

Publisher Copyright:
© 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

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