A Learning Based Vertical Integration Decision Model

Menekşe Gizem Görgün; Seçkin Polat; Umut Asan

doi:10.1007/978-3-030-85577-2_15

A Learning Based Vertical Integration Decision Model

Menekşe Gizem Görgün^*, Seçkin Polat, Umut Asan

^*Corresponding author for this work

Department of Industrial Engineering

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

Abstract

Vertical integration decisions, also referred to as make or buy decisions, affect firm activity areas that determine boundaries that play a strategic role in the success of firms. The decisions may result in form of spot market usage, long-term contracts, joint ventures, and in-house productions. This study first reviews the decision modeling approaches suggested in the literature for vertical integration. The review shows that for decision making, most of the approaches are qualitative in nature and but there are also optimization models and expert based models. Thus, a new learning based probabilistic classification approach is proposed to model the vertical integration decision problem. This data-driven quantitative approach using Naïve Bayes is able to represent the uncertainty inherent in the decision problem. The applicability and effectiveness of the developed learning based vertical integration decision model is demonstrated by an example that covers real software make or buy cases in a retail company. 85% of the decisions were correctly classified by the developed data-driven model.

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	131-137
Number of pages	7
ISBN (Print)	9783030855765
DOIs	https://doi.org/10.1007/978-3-030-85577-2_15
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

Make or buy decision
Naïve Bayes
Probabilistic classification
Vertical integration

Access to Document

10.1007/978-3-030-85577-2_15

Cite this

Görgün, M. G., Polat, S., & Asan, U. (2022). A Learning Based Vertical Integration Decision Model. 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. 131-137). (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_15

Görgün, Menekşe Gizem ; Polat, Seçkin ; Asan, Umut. / A Learning Based Vertical Integration Decision Model. 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. 131-137 (Lecture Notes in Networks and Systems).

@inproceedings{70eb913f532b4d0c900880d360d362e0,

title = "A Learning Based Vertical Integration Decision Model",

abstract = "Vertical integration decisions, also referred to as make or buy decisions, affect firm activity areas that determine boundaries that play a strategic role in the success of firms. The decisions may result in form of spot market usage, long-term contracts, joint ventures, and in-house productions. This study first reviews the decision modeling approaches suggested in the literature for vertical integration. The review shows that for decision making, most of the approaches are qualitative in nature and but there are also optimization models and expert based models. Thus, a new learning based probabilistic classification approach is proposed to model the vertical integration decision problem. This data-driven quantitative approach using Na{\"i}ve Bayes is able to represent the uncertainty inherent in the decision problem. The applicability and effectiveness of the developed learning based vertical integration decision model is demonstrated by an example that covers real software make or buy cases in a retail company. 85% of the decisions were correctly classified by the developed data-driven model.",

keywords = "Make or buy decision, Na{\"i}ve Bayes, Probabilistic classification, Vertical integration",

author = "G{\"o}rg{\"u}n, {Menek{\c s}e Gizem} and Se{\c c}kin Polat and Umut Asan",

note = "Publisher Copyright: {\textcopyright} 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.; International Conference on Intelligent and Fuzzy Systems, INFUS 2021 ; Conference date: 24-08-2021 Through 26-08-2021",

year = "2022",

doi = "10.1007/978-3-030-85577-2_15",

language = "English",

isbn = "9783030855765",

series = "Lecture Notes in Networks and Systems",

publisher = "Springer Science and Business Media Deutschland GmbH",

pages = "131--137",

editor = "Cengiz Kahraman and Selcuk Cebi and {Cevik Onar}, Sezi and Basar Oztaysi and Tolga, {A. Cagri} and Sari, {Irem Ucal}",

booktitle = "Intelligent and Fuzzy Techniques for Emerging Conditions and Digital Transformation - Proceedings of the INFUS 2021 Conference",

address = "Germany",

}

Görgün, MG, Polat, S & Asan, U 2022, A Learning Based Vertical Integration Decision Model. 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. 131-137, International Conference on Intelligent and Fuzzy Systems, INFUS 2021, Istanbul, Turkey, 24/08/21. https://doi.org/10.1007/978-3-030-85577-2_15

A Learning Based Vertical Integration Decision Model. / Görgün, Menekşe Gizem; Polat, Seçkin ; Asan, Umut.
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. 131-137 (Lecture Notes in Networks and Systems; Vol. 308).

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

TY - GEN

T1 - A Learning Based Vertical Integration Decision Model

AU - Görgün, Menekşe Gizem

AU - Polat, Seçkin

AU - Asan, Umut

PY - 2022

Y1 - 2022

N2 - Vertical integration decisions, also referred to as make or buy decisions, affect firm activity areas that determine boundaries that play a strategic role in the success of firms. The decisions may result in form of spot market usage, long-term contracts, joint ventures, and in-house productions. This study first reviews the decision modeling approaches suggested in the literature for vertical integration. The review shows that for decision making, most of the approaches are qualitative in nature and but there are also optimization models and expert based models. Thus, a new learning based probabilistic classification approach is proposed to model the vertical integration decision problem. This data-driven quantitative approach using Naïve Bayes is able to represent the uncertainty inherent in the decision problem. The applicability and effectiveness of the developed learning based vertical integration decision model is demonstrated by an example that covers real software make or buy cases in a retail company. 85% of the decisions were correctly classified by the developed data-driven model.

AB - Vertical integration decisions, also referred to as make or buy decisions, affect firm activity areas that determine boundaries that play a strategic role in the success of firms. The decisions may result in form of spot market usage, long-term contracts, joint ventures, and in-house productions. This study first reviews the decision modeling approaches suggested in the literature for vertical integration. The review shows that for decision making, most of the approaches are qualitative in nature and but there are also optimization models and expert based models. Thus, a new learning based probabilistic classification approach is proposed to model the vertical integration decision problem. This data-driven quantitative approach using Naïve Bayes is able to represent the uncertainty inherent in the decision problem. The applicability and effectiveness of the developed learning based vertical integration decision model is demonstrated by an example that covers real software make or buy cases in a retail company. 85% of the decisions were correctly classified by the developed data-driven model.

KW - Make or buy decision

KW - Naïve Bayes

KW - Probabilistic classification

KW - Vertical integration

UR - http://www.scopus.com/inward/record.url?scp=85115212349&partnerID=8YFLogxK

U2 - 10.1007/978-3-030-85577-2_15

DO - 10.1007/978-3-030-85577-2_15

M3 - Conference contribution

AN - SCOPUS:85115212349

SN - 9783030855765

T3 - Lecture Notes in Networks and Systems

SP - 131

EP - 137

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

Y2 - 24 August 2021 through 26 August 2021

ER -

Görgün MG, Polat S , Asan U. A Learning Based Vertical Integration Decision Model. 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. 131-137. (Lecture Notes in Networks and Systems). doi: 10.1007/978-3-030-85577-2_15

A Learning Based Vertical Integration Decision Model

Abstract

Publication series

Conference

Bibliographical note

Keywords

Access to Document

Other files and links

Fingerprint

Cite this