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

Engin Yağcıoğlu; Ahmet Tezcan Tekin; Ferhan Çebi

doi:10.1007/978-3-030-85577-2_23

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

Engin Yağcıoğlu, Ahmet Tezcan Tekin^*, Ferhan Çebi

^*Corresponding author for this work

Department of Management Engineering

Istanbul Technical University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

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.

Original language	English
Title of host publication	Intelligent and Fuzzy Techniques for Emerging Conditions and Digital Transformation - Proceedings of the INFUS 2021 Conference
Editors	Cengiz Kahraman, Selcuk Cebi, Sezi Cevik Onar, Basar Oztaysi, A. Cagri Tolga, Irem Ucal Sari
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	197-204
Number of pages	8
ISBN (Print)	9783030855765
DOIs	https://doi.org/10.1007/978-3-030-85577-2_23
Publication status	Published - 2022
Event	International Conference on Intelligent and Fuzzy Systems, INFUS 2021 - Istanbul, Turkey Duration: 24 Aug 2021 → 26 Aug 2021

Publication series

Name	Lecture Notes in Networks and Systems
Volume	308
ISSN (Print)	2367-3370
ISSN (Electronic)	2367-3389

Conference

Conference	International Conference on Intelligent and Fuzzy Systems, INFUS 2021
Country/Territory	Turkey
City	Istanbul
Period	24/08/21 → 26/08/21

Bibliographical note

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

Keywords

Demand forecast
Machine learning
Mass customization

Access to Document

10.1007/978-3-030-85577-2_23

Cite this

Yağcıoğlu, E., Tekin, A. T., & Çebi, F. (2022). Demand Forecasting of a Company that Produces by Mass Customization with Machine Learning. In C. Kahraman, S. Cebi, S. Cevik Onar, B. Oztaysi, A. C. Tolga, & I. U. Sari (Eds.), Intelligent and Fuzzy Techniques for Emerging Conditions and Digital Transformation - Proceedings of the INFUS 2021 Conference (pp. 197-204). (Lecture Notes in Networks and Systems; Vol. 308). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-85577-2_23

Yağcıoğlu, Engin ; Tekin, Ahmet Tezcan ; Çebi, Ferhan. / Demand Forecasting of a Company that Produces by Mass Customization with Machine Learning. Intelligent and Fuzzy Techniques for Emerging Conditions and Digital Transformation - Proceedings of the INFUS 2021 Conference. editor / Cengiz Kahraman ; Selcuk Cebi ; Sezi Cevik Onar ; Basar Oztaysi ; A. Cagri Tolga ; Irem Ucal Sari. Springer Science and Business Media Deutschland GmbH, 2022. pp. 197-204 (Lecture Notes in Networks and Systems).

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abstract = "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.",

keywords = "Demand forecast, Machine learning, Mass customization",

author = "Engin Yağcıoğlu and Tekin, \{Ahmet Tezcan\} and Ferhan {\c C}ebi",

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Yağcıoğlu, E, Tekin, AT & Çebi, F 2022, Demand Forecasting of a Company that Produces by Mass Customization with Machine Learning. in C Kahraman, S Cebi, S Cevik Onar, B Oztaysi, AC Tolga & IU Sari (eds), Intelligent and Fuzzy Techniques for Emerging Conditions and Digital Transformation - Proceedings of the INFUS 2021 Conference. Lecture Notes in Networks and Systems, vol. 308, Springer Science and Business Media Deutschland GmbH, pp. 197-204, International Conference on Intelligent and Fuzzy Systems, INFUS 2021, Istanbul, Turkey, 24/08/21. https://doi.org/10.1007/978-3-030-85577-2_23

Demand Forecasting of a Company that Produces by Mass Customization with Machine Learning. / Yağcıoğlu, Engin; Tekin, Ahmet Tezcan; Çebi, Ferhan.
Intelligent and Fuzzy Techniques for Emerging Conditions and Digital Transformation - Proceedings of the INFUS 2021 Conference. ed. / Cengiz Kahraman; Selcuk Cebi; Sezi Cevik Onar; Basar Oztaysi; A. Cagri Tolga; Irem Ucal Sari. Springer Science and Business Media Deutschland GmbH, 2022. p. 197-204 (Lecture Notes in Networks and Systems; Vol. 308).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Demand Forecasting of a Company that Produces by Mass Customization with Machine Learning

AU - Yağcıoğlu, Engin

AU - Tekin, Ahmet Tezcan

AU - Çebi, Ferhan

PY - 2022

Y1 - 2022

N2 - 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.

AB - 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.

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SN - 9783030855765

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BT - Intelligent and Fuzzy Techniques for Emerging Conditions and Digital Transformation - Proceedings of the INFUS 2021 Conference

A2 - Kahraman, Cengiz

A2 - Cebi, Selcuk

A2 - Cevik Onar, Sezi

A2 - Oztaysi, Basar

A2 - Tolga, A. Cagri

A2 - Sari, Irem Ucal

PB - Springer Science and Business Media Deutschland GmbH

T2 - International Conference on Intelligent and Fuzzy Systems, INFUS 2021

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ER -

Yağcıoğlu E, Tekin AT, Çebi F. Demand Forecasting of a Company that Produces by Mass Customization with Machine Learning. In Kahraman C, Cebi S, Cevik Onar S, Oztaysi B, Tolga AC, Sari IU, editors, Intelligent and Fuzzy Techniques for Emerging Conditions and Digital Transformation - Proceedings of the INFUS 2021 Conference. Springer Science and Business Media Deutschland GmbH. 2022. p. 197-204. (Lecture Notes in Networks and Systems). doi: 10.1007/978-3-030-85577-2_23

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

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Keywords

Access to Document

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