Development of an AI-Enhanced Refill Station Model for Sustainable Packaging in the FMCG Sector

Abdullah Bilal Kibar; Aleyna Nur Erarslanoğlu; Ferhan Çebi

doi:10.1007/978-3-032-22784-3_5

Development of an AI-Enhanced Refill Station Model for Sustainable Packaging in the FMCG Sector

Abdullah Bilal Kibar
, Aleyna Nur Erarslanoğlu^*
, 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

This project addresses the environmental challenges posed by single-use plastic packaging in the Fast-Moving Consumer Goods (FMCG) sector, focusing on the Turkish laundry detergent market. It proposes the implementation of “Refill on the Go” stations, enabling consumers to refill reusable containers in partnership with supermarkets and retail chains. The project integrates AI and machine learning technologies to optimize station operations, predict demand, and personalize user experiences. Algorithms are used to monitor stock levels, forecast refilling needs, and analyze consumer behavior patterns based on transactional data, enabling dynamic pricing and targeted promotions. Through a comprehensive literature review, market analysis, fieldwork, and customer segmentation, the project demonstrates both environmental and economic viability. Consumer surveys indicate strong interest in sustainable, affordable, and convenient solutions. The refill model aligns with EU circular economy goals and UN Sustainable Development Goals by reducing plastic waste, promoting behavioral change, and supporting data-driven decision-making for long-term impact.

Original language	English
Title of host publication	AI-Driven Production with Green Sustainability - Selected Papers from ISPR2025
Editors	Numan M. Durakbasa, Hatice Camgöz Akdag, Kemal Güven Gülen
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	67-74
Number of pages	8
ISBN (Print)	9783032227836
DOIs	https://doi.org/10.1007/978-3-032-22784-3_5
Publication status	Published - 2026
Event	International Symposium for Production Research, ISPR 2025 - Istanbul, Turkey Duration: 9 Oct 2025 → 11 Oct 2025

Publication series

Name	Lecture Notes in Mechanical Engineering
ISSN (Print)	2195-4356
ISSN (Electronic)	2195-4364

Conference

Conference	International Symposium for Production Research, ISPR 2025
Country/Territory	Turkey
City	Istanbul
Period	9/10/25 → 11/10/25

Bibliographical note

Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.

Keywords

AI
Circularity
Consumption
FMCG
Forecasting
Machine Learning
Packaging
Plastic Packages
Plastic Waste
Refill
Reusables
Sustainability
Waste Reduction

Access to Document

10.1007/978-3-032-22784-3_5

Cite this

Kibar, A. B., Erarslanoğlu, A. N., & Çebi, F. (2026). Development of an AI-Enhanced Refill Station Model for Sustainable Packaging in the FMCG Sector. In N. M. Durakbasa, H. C. Akdag, & K. G. Gülen (Eds.), AI-Driven Production with Green Sustainability - Selected Papers from ISPR2025 (pp. 67-74). (Lecture Notes in Mechanical Engineering). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-032-22784-3_5

Kibar, Abdullah Bilal ; Erarslanoğlu, Aleyna Nur ; Çebi, Ferhan. / Development of an AI-Enhanced Refill Station Model for Sustainable Packaging in the FMCG Sector. AI-Driven Production with Green Sustainability - Selected Papers from ISPR2025. editor / Numan M. Durakbasa ; Hatice Camgöz Akdag ; Kemal Güven Gülen. Springer Science and Business Media Deutschland GmbH, 2026. pp. 67-74 (Lecture Notes in Mechanical Engineering).

@inproceedings{0d02b13ffdbf46708d512807f5ef5eb6,

title = "Development of an AI-Enhanced Refill Station Model for Sustainable Packaging in the FMCG Sector",

abstract = "This project addresses the environmental challenges posed by single-use plastic packaging in the Fast-Moving Consumer Goods (FMCG) sector, focusing on the Turkish laundry detergent market. It proposes the implementation of {\textquotedblleft}Refill on the Go{\textquotedblright} stations, enabling consumers to refill reusable containers in partnership with supermarkets and retail chains. The project integrates AI and machine learning technologies to optimize station operations, predict demand, and personalize user experiences. Algorithms are used to monitor stock levels, forecast refilling needs, and analyze consumer behavior patterns based on transactional data, enabling dynamic pricing and targeted promotions. Through a comprehensive literature review, market analysis, fieldwork, and customer segmentation, the project demonstrates both environmental and economic viability. Consumer surveys indicate strong interest in sustainable, affordable, and convenient solutions. The refill model aligns with EU circular economy goals and UN Sustainable Development Goals by reducing plastic waste, promoting behavioral change, and supporting data-driven decision-making for long-term impact.",

keywords = "AI, Circularity, Consumption, FMCG, Forecasting, Machine Learning, Packaging, Plastic Packages, Plastic Waste, Refill, Reusables, Sustainability, Waste Reduction",

author = "Kibar, \{Abdullah Bilal\} and Erarslanoğlu, \{Aleyna Nur\} and Ferhan {\c C}ebi",

note = "Publisher Copyright: {\textcopyright} The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.; International Symposium for Production Research, ISPR 2025 ; Conference date: 09-10-2025 Through 11-10-2025",

year = "2026",

doi = "10.1007/978-3-032-22784-3\_5",

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

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editor = "Durakbasa, \{Numan M.\} and Akdag, \{Hatice Camg{\"o}z\} and G{\"u}len, \{Kemal G{\"u}ven\}",

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Kibar, AB, Erarslanoğlu, AN & Çebi, F 2026, Development of an AI-Enhanced Refill Station Model for Sustainable Packaging in the FMCG Sector. in NM Durakbasa, HC Akdag & KG Gülen (eds), AI-Driven Production with Green Sustainability - Selected Papers from ISPR2025. Lecture Notes in Mechanical Engineering, Springer Science and Business Media Deutschland GmbH, pp. 67-74, International Symposium for Production Research, ISPR 2025, Istanbul, Turkey, 9/10/25. https://doi.org/10.1007/978-3-032-22784-3_5

Development of an AI-Enhanced Refill Station Model for Sustainable Packaging in the FMCG Sector. / Kibar, Abdullah Bilal; Erarslanoğlu, Aleyna Nur; Çebi, Ferhan.
AI-Driven Production with Green Sustainability - Selected Papers from ISPR2025. ed. / Numan M. Durakbasa; Hatice Camgöz Akdag; Kemal Güven Gülen. Springer Science and Business Media Deutschland GmbH, 2026. p. 67-74 (Lecture Notes in Mechanical Engineering).

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

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AU - Kibar, Abdullah Bilal

AU - Erarslanoğlu, Aleyna Nur

AU - Çebi, Ferhan

N1 - Publisher Copyright: © The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.

PY - 2026

Y1 - 2026

N2 - This project addresses the environmental challenges posed by single-use plastic packaging in the Fast-Moving Consumer Goods (FMCG) sector, focusing on the Turkish laundry detergent market. It proposes the implementation of “Refill on the Go” stations, enabling consumers to refill reusable containers in partnership with supermarkets and retail chains. The project integrates AI and machine learning technologies to optimize station operations, predict demand, and personalize user experiences. Algorithms are used to monitor stock levels, forecast refilling needs, and analyze consumer behavior patterns based on transactional data, enabling dynamic pricing and targeted promotions. Through a comprehensive literature review, market analysis, fieldwork, and customer segmentation, the project demonstrates both environmental and economic viability. Consumer surveys indicate strong interest in sustainable, affordable, and convenient solutions. The refill model aligns with EU circular economy goals and UN Sustainable Development Goals by reducing plastic waste, promoting behavioral change, and supporting data-driven decision-making for long-term impact.

AB - This project addresses the environmental challenges posed by single-use plastic packaging in the Fast-Moving Consumer Goods (FMCG) sector, focusing on the Turkish laundry detergent market. It proposes the implementation of “Refill on the Go” stations, enabling consumers to refill reusable containers in partnership with supermarkets and retail chains. The project integrates AI and machine learning technologies to optimize station operations, predict demand, and personalize user experiences. Algorithms are used to monitor stock levels, forecast refilling needs, and analyze consumer behavior patterns based on transactional data, enabling dynamic pricing and targeted promotions. Through a comprehensive literature review, market analysis, fieldwork, and customer segmentation, the project demonstrates both environmental and economic viability. Consumer surveys indicate strong interest in sustainable, affordable, and convenient solutions. The refill model aligns with EU circular economy goals and UN Sustainable Development Goals by reducing plastic waste, promoting behavioral change, and supporting data-driven decision-making for long-term impact.

KW - AI

KW - Circularity

KW - Consumption

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KW - Forecasting

KW - Machine Learning

KW - Packaging

KW - Plastic Packages

KW - Plastic Waste

KW - Refill

KW - Reusables

KW - Sustainability

KW - Waste Reduction

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M3 - Conference contribution

AN - SCOPUS:105046867598

SN - 9783032227836

T3 - Lecture Notes in Mechanical Engineering

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BT - AI-Driven Production with Green Sustainability - Selected Papers from ISPR2025

A2 - Durakbasa, Numan M.

A2 - Akdag, Hatice Camgöz

A2 - Gülen, Kemal Güven

PB - Springer Science and Business Media Deutschland GmbH

T2 - International Symposium for Production Research, ISPR 2025

Y2 - 9 October 2025 through 11 October 2025

ER -

Kibar AB, Erarslanoğlu AN, Çebi F. Development of an AI-Enhanced Refill Station Model for Sustainable Packaging in the FMCG Sector. In Durakbasa NM, Akdag HC, Gülen KG, editors, AI-Driven Production with Green Sustainability - Selected Papers from ISPR2025. Springer Science and Business Media Deutschland GmbH. 2026. p. 67-74. (Lecture Notes in Mechanical Engineering). doi: 10.1007/978-3-032-22784-3_5

Development of an AI-Enhanced Refill Station Model for Sustainable Packaging in the FMCG Sector

Abstract

Publication series

Conference

Bibliographical note

Keywords

Access to Document

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