Artificial intelligence as a strategic lever for enhancing performance in service sector supply chains: a systematic literature review

Purpose – this study was undertaken through a systematic literature review to identify the central role of Artificial Intelligence (AI) in improving supply chain performance across all service sectors. Research methodology – a dual methodological approach was used involving both quantitative and qualitative analysis. The former is largely based on a bibliometric analysis of 61 peer-re- viewed articles published between 2019 and 2024. In this respect, two software tools, RStudio and VOSviewer, were used to provide a comprehensive bibliometric landscape. The second concerns a thematic content analysis of 25 full-text studies, focusing on AI techniques, performance results and industry-specific implementation challenges. Findings – the overall result of this systematic review indicates that AI contributes to service supply chain performance by improving forecasting, resilience, operational efficiency and real-time decision-making. Research limitations – however, limitations such as data availability, system interoperability, ethical risks and organizational resistance remain important. Practical implications – the results of this study help practitioners to select AI solutions suitable for their sector and to anticipate obstacles to integration. Originality/Value – this study provides a better understanding of how AI is reshaping supply chains in service industries, while identifying key avenues for both future research and practical adoption.

Authors

Institutions

Publication Details

Journal
Business Management and Economics Engineering
Published
2026-03-04
DOI
https://doi.org/10.3846/bmee.2026.24087
Primary Topic
Supply Chain Resilience and Risk Management
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Artificial intelligence as a strategic lever for enhancing performance in service sector supply chains: a systematic literature review

Abdelali Lahrech, Slimane Sidouna, El Mehdi Badre, Hind Chahbar et al.
Business Management and Economics Engineering
Supply Chain Resilience and Risk Management
article

Artificial intelligence as a strategic lever for enhancing performance in service sector supply chains: a systematic literature review

Abdelali Lahrech, Slimane Sidouna, El Mehdi Badre, Hind Chahbar, Adnane Mrabti
article en

Abstract

Purpose – this study was undertaken through a systematic literature review to identify the central role of Artificial Intelligence (AI) in improving supply chain performance across all service sectors. Research methodology – a dual methodological approach was used involving both quantitative and qualitative analysis. The former is largely based on a bibliometric analysis of 61 peer-re- viewed articles published between 2019 and 2024. In this respect, two software tools, RStudio and VOSviewer, were used to provide a comprehensive bibliometric landscape. The second concerns a thematic content analysis of 25 full-text studies, focusing on AI techniques, performance results and industry-specific implementation challenges. Findings – the overall result of this systematic review indicates that AI contributes to service supply chain performance by improving forecasting, resilience, operational efficiency and real-time decision-making. Research limitations – however, limitations such as data availability, system interoperability, ethical risks and organizational resistance remain important. Practical implications – the results of this study help practitioners to select AI solutions suitable for their sector and to anticipate obstacles to integration. Originality/Value – this study provides a better understanding of how AI is reshaping supply chains in service industries, while identifying key avenues for both future research and practical adoption.

Business Management and Economics EngineeringVol. 24(1)
Instituto Superior da Maia (PT), Sidi Mohamed Ben Abdellah University (MA)
Openalex Percentile: Top 38%
Supply Chain Resilience and Risk Management
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.