Generative Artificial Intelligence in Adaptive Social Manufacturing: A Pathway to Achieving Industry 5.0 Sustainability Goals

As manufacturing transitions from Industry 4.0 to Industry 5.0, a critical challenge emerges in integrating Generative Artificial Intelligence (GAI) into adaptive social manufacturing to achieve sustainability goals. This transition reflects a paradigmatic shift from a technology-centric model focused on automation and efficiency toward a more holistic framework that embeds human-centricity and environmental responsibility into industrial systems. Whereas Industry 4.0 emphasizes digital innovation and productivity, Industry 5.0 seeks to align technological advancement with broader ecological and societal objectives. Despite advancements in automation and digitalization, existing frameworks lack a structured approach to leveraging GAI for environmental, social, and economic sustainability. This study explores the transformative role of GAI in adaptive social manufacturing, addressing the gap in the existing frameworks. Employing a multi-method research design, including content analysis, expert-driven validation, and system dynamics modeling, the study identifies nine key sustainability dimensions of Industry 5.0 and maps them to 17 GAI functions. The findings reveal that GAI significantly enhances adaptive social manufacturing by optimizing resource efficiency, promoting inclusivity, and supporting ethical governance. System dynamics analysis highlights the complex interdependencies between GAI-driven functions and sustainability outcomes, underscoring the need to balance technological innovation with human values. The research provides a novel framework for industries seeking to implement GAI in sustainable production systems, bridging theoretical insights with practical applications. Additionally, it offers actionable strategies to address challenges such as workforce adaptation, ethical AI governance, and adoption barriers, ultimately facilitating the transition toward Industry 5.0’s sustainability goals.

Authors

Institutions

Publication Details

Journal
Processes
Published
2025-04-12
DOI
https://doi.org/10.3390/pr13041174
Citations
10
Primary Topic
Digital Transformation in Industry
Type
article
Field-Weighted Citation Impact
12.42
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Generative Artificial Intelligence in Adaptive Social Manufacturing: A Pathway to Achieving Industry 5.0 Sustainability Goals

Parisa Jourabchi Amirkhizi, Ahad Shahhoseini, Sajjad Pakzad, Siamak Pedrammehr
10 citations
Processes
Digital Transformation in Industry
12.42
article

Generative Artificial Intelligence in Adaptive Social Manufacturing: A Pathway to Achieving Industry 5.0 Sustainability Goals

Parisa Jourabchi Amirkhizi, Ahad Shahhoseini, Sajjad Pakzad, Siamak Pedrammehr
article en
10 citations

Abstract

As manufacturing transitions from Industry 4.0 to Industry 5.0, a critical challenge emerges in integrating Generative Artificial Intelligence (GAI) into adaptive social manufacturing to achieve sustainability goals. This transition reflects a paradigmatic shift from a technology-centric model focused on automation and efficiency toward a more holistic framework that embeds human-centricity and environmental responsibility into industrial systems. Whereas Industry 4.0 emphasizes digital innovation and productivity, Industry 5.0 seeks to align technological advancement with broader ecological and societal objectives. Despite advancements in automation and digitalization, existing frameworks lack a structured approach to leveraging GAI for environmental, social, and economic sustainability. This study explores the transformative role of GAI in adaptive social manufacturing, addressing the gap in the existing frameworks. Employing a multi-method research design, including content analysis, expert-driven validation, and system dynamics modeling, the study identifies nine key sustainability dimensions of Industry 5.0 and maps them to 17 GAI functions. The findings reveal that GAI significantly enhances adaptive social manufacturing by optimizing resource efficiency, promoting inclusivity, and supporting ethical governance. System dynamics analysis highlights the complex interdependencies between GAI-driven functions and sustainability outcomes, underscoring the need to balance technological innovation with human values. The research provides a novel framework for industries seeking to implement GAI in sustainable production systems, bridging theoretical insights with practical applications. Additionally, it offers actionable strategies to address challenges such as workforce adaptation, ethical AI governance, and adoption barriers, ultimately facilitating the transition toward Industry 5.0’s sustainability goals.

ProcessesVol. 13(4)
Tabriz Islamic Arts University (IR)
Openalex Percentile: Top 1%
Digital Transformation in Industry
12.42
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.