Artificial Intelligence for Supply Chain Sustainability: A Research Roadmap

Maria Patricia Peeris; George Baryannis

doi:10.1109/SKIMA66621.2025.11155798

Artificial Intelligence for Supply Chain Sustainability: A Research Roadmap

Maria Patricia Peeris, George Baryannis

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

1 Citation (Scopus)

Abstract

In recent years, there has been an increasingly strong interest in improving supply chain sustainability to deal with wide-ranging impacts, from climate change to global trade uncertainty. While Artificial Intelligence has been identified as a key Industry 5.0 technology, its significant potential in the context of sustainability has not yet been harnessed. In this paper, we first present the results of a systematic literature review to both clarify the potential of AI for supply chain sustainability and understand which particular AI technologies have been utilised and for which application. Through insights drawn from this literature survey, we propose a research roadmap centred around four leading-edge AI approaches: generative AI, knowledge graphs, neurosymbolic AI and causal machine learning, and provide indicative suggestions for leveraging these approaches to help supply chain stakeholders meets sustainability targets.

Original language	English
Title of host publication	2025 International Conference on Software, Knowledge, Information Management & Applications (SKIMA)
Editors	Keshav Dahal, Zeeshan Pervez, Marco Gilardi
Publisher	IEEE
Number of pages	6
ISBN (Electronic)	9781665457347
ISBN (Print)	9781665457354
DOIs	https://doi.org/10.1109/SKIMA66621.2025.11155798
Publication status	Published - 16 Sept 2025
Event	16th International Conference on Software, Knowledge, Information Management and Applications - University of the West of Scotland, Paisley, United Kingdom Duration: 9 Jun 2025 → 11 Jun 2025 Conference number: 16 https://skimanetwork.org/

Conference

Conference	16th International Conference on Software, Knowledge, Information Management and Applications
Abbreviated title	SKIMA 2025
Country/Territory	United Kingdom
City	Paisley
Period	9/06/25 → 11/06/25
Internet address	https://skimanetwork.org/

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 9 Industry, Innovation, and Infrastructure
SDG 13 Climate Action

Access to Document

10.1109/SKIMA66621.2025.11155798Licence: Unspecified

SKIMA2025_SCS_AI_CRAccepted author manuscript, 94.2 KBLicence: Unspecified

Cite this

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title = "Artificial Intelligence for Supply Chain Sustainability: A Research Roadmap",

abstract = "In recent years, there has been an increasingly strong interest in improving supply chain sustainability to deal with wide-ranging impacts, from climate change to global trade uncertainty. While Artificial Intelligence has been identified as a key Industry 5.0 technology, its significant potential in the context of sustainability has not yet been harnessed. In this paper, we first present the results of a systematic literature review to both clarify the potential of AI for supply chain sustainability and understand which particular AI technologies have been utilised and for which application. Through insights drawn from this literature survey, we propose a research roadmap centred around four leading-edge AI approaches: generative AI, knowledge graphs, neurosymbolic AI and causal machine learning, and provide indicative suggestions for leveraging these approaches to help supply chain stakeholders meets sustainability targets.",

keywords = "Artificial intelligence, Supply chains, Sustainable development, Decision making, Reviews",

author = "Peeris, \{Maria Patricia\} and George Baryannis",

note = "Publisher Copyright: {\textcopyright} 2025 IEEE.; 16th International Conference on Software, Knowledge, Information Management and Applications, SKIMA 2025 ; Conference date: 09-06-2025 Through 11-06-2025",

year = "2025",

month = sep,

day = "16",

doi = "10.1109/SKIMA66621.2025.11155798",

language = "English",

isbn = "9781665457354",

editor = "Keshav Dahal and Zeeshan Pervez and Marco Gilardi",

booktitle = "2025 International Conference on Software, Knowledge, Information Management \& Applications (SKIMA)",

publisher = "IEEE",

address = "United States",

url = "https://skimanetwork.org/",

}

Peeris, MP & Baryannis, G 2025, Artificial Intelligence for Supply Chain Sustainability: A Research Roadmap. in K Dahal, Z Pervez & M Gilardi (eds), 2025 International Conference on Software, Knowledge, Information Management & Applications (SKIMA)., 11155798, IEEE, 16th International Conference on Software, Knowledge, Information Management and Applications, Paisley, United Kingdom, 9/06/25. https://doi.org/10.1109/SKIMA66621.2025.11155798

Artificial Intelligence for Supply Chain Sustainability: A Research Roadmap. / Peeris, Maria Patricia; Baryannis, George.
2025 International Conference on Software, Knowledge, Information Management & Applications (SKIMA). ed. / Keshav Dahal; Zeeshan Pervez; Marco Gilardi. IEEE, 2025. 11155798.

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

TY - GEN

T1 - Artificial Intelligence for Supply Chain Sustainability

T2 - 16th International Conference on Software, Knowledge, Information Management and Applications

AU - Peeris, Maria Patricia

AU - Baryannis, George

N1 - Conference code: 16

PY - 2025/9/16

Y1 - 2025/9/16

N2 - In recent years, there has been an increasingly strong interest in improving supply chain sustainability to deal with wide-ranging impacts, from climate change to global trade uncertainty. While Artificial Intelligence has been identified as a key Industry 5.0 technology, its significant potential in the context of sustainability has not yet been harnessed. In this paper, we first present the results of a systematic literature review to both clarify the potential of AI for supply chain sustainability and understand which particular AI technologies have been utilised and for which application. Through insights drawn from this literature survey, we propose a research roadmap centred around four leading-edge AI approaches: generative AI, knowledge graphs, neurosymbolic AI and causal machine learning, and provide indicative suggestions for leveraging these approaches to help supply chain stakeholders meets sustainability targets.

AB - In recent years, there has been an increasingly strong interest in improving supply chain sustainability to deal with wide-ranging impacts, from climate change to global trade uncertainty. While Artificial Intelligence has been identified as a key Industry 5.0 technology, its significant potential in the context of sustainability has not yet been harnessed. In this paper, we first present the results of a systematic literature review to both clarify the potential of AI for supply chain sustainability and understand which particular AI technologies have been utilised and for which application. Through insights drawn from this literature survey, we propose a research roadmap centred around four leading-edge AI approaches: generative AI, knowledge graphs, neurosymbolic AI and causal machine learning, and provide indicative suggestions for leveraging these approaches to help supply chain stakeholders meets sustainability targets.

KW - Artificial intelligence

KW - Supply chains

KW - Sustainable development

KW - Decision making

KW - Reviews

UR - https://www.scopus.com/pages/publications/105017666305

U2 - 10.1109/SKIMA66621.2025.11155798

DO - 10.1109/SKIMA66621.2025.11155798

M3 - Conference contribution

SN - 9781665457354

BT - 2025 International Conference on Software, Knowledge, Information Management & Applications (SKIMA)

A2 - Dahal, Keshav

A2 - Pervez, Zeeshan

A2 - Gilardi, Marco

PB - IEEE

Y2 - 9 June 2025 through 11 June 2025

ER -

Artificial Intelligence for Supply Chain Sustainability: A Research Roadmap

Abstract

Conference

UN SDGs

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Advancing Sustainable Supply Chains Through Knowledge Graph Completion and Graph-Based Artificial Intelligence

Artificial Intelligence Opportunities for Resilient Supply Chains

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