Decision-making framework for sustainability-related supply chain risk management DOI
Ming-Fu Hsu

Computers & Industrial Engineering, Journal Year: 2024, Volume and Issue: unknown, P. 110825 - 110825

Published: Dec. 1, 2024

Language: Английский

AI meets Spend Classification: a new frontier in Information Processing DOI Creative Commons
Michela Guida, Federico Caniato, Antonella Moretto

et al.

Journal of Purchasing and Supply Management, Journal Year: 2025, Volume and Issue: unknown, P. 100993 - 100993

Published: Feb. 1, 2025

Language: Английский

Citations

2
Democracy in the Age of AI DOI
Jipson Joseph, Ananya Pandey

IGI Global eBooks, Journal Year: 2025, Volume and Issue: unknown, P. 19 - 38

Published: Feb. 21, 2025

Democracy is an age-old idea. Although it originated in Ancient Greek Polis Athens, took almost 20 centuries to become a globally preferred political system of governance. In the aftermath Second World War, many countries became independent and democratic affirming role strength people governance country. revolves around values like human rights, free choice, freedom, equality, decision-making. However, with latest technological advancements area artificial intelligence (AI), there are serious concerns regard smooth functioning democracy as biased involvements officially reported. The virtual world AI systems used for targeting efficiency democracy. chapter, this perspective, critically examines enlists certain ethical legal principles AI's effective incorporation into dynamics politics

Language: Английский

Citations

1
Large scale foundation models for intelligent manufacturing applications: a survey DOI
Haotian Zhang,

Stuart Dereck Semujju,

Zhicheng Wang

et al.

Journal of Intelligent Manufacturing, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 4, 2025

Language: Английский

Citations

0
The impact of artificial intelligence usage on supply chain resilience in manufacturing firms: a moderated mediation model DOI

Xiaoxing Yue,

Mary Kang, Yanming Zhang

et al.

Journal of Manufacturing Technology Management, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 7, 2025

Purpose Manufacturing firms must strengthen their supply chain resilience to survive in turbulent business environments. This study explores how artificial intelligence (AI) can be leveraged enhance resilience. Design/methodology/approach Drawing on organizational information processing theory, the research investigates impact of AI usage proactive and reactive by fostering referent power context demand dynamism. The analyzes survey data from 285 Chinese manufacturing using structural equation modeling regression analysis. Findings results indicate that both Referent only mediates relationship between Furthermore, this mediating effect is stronger under high-level Originality/value highlights value strengthening uncovers its underlying mechanisms. Theoretical practical implications are discussed.

Language: Английский

Citations

0
Influence of artificial intelligence development on supply chain diversification DOI
Bin Wu, Hang Chen, Yujie Shi

et al.

Finance research letters, Journal Year: 2025, Volume and Issue: unknown, P. 107210 - 107210

Published: March 1, 2025

Language: Английский

Citations

0
Automating quality control through an expert system DOI Creative Commons
Giorgio Scarton, Marco Formentini, Pietro Romano

et al.

Electronic Markets, Journal Year: 2025, Volume and Issue: 35(1)

Published: Feb. 14, 2025

Language: Английский

Citations

0
Unraveling the path to smart manufacturing advancement: insights from necessary condition and fuzzy-set qualitative comparative analyses DOI
Francesco Arcidiacono, Matteo Podrecca, Florian Schupp

et al.

Industrial Management & Data Systems, Journal Year: 2025, Volume and Issue: unknown

Published: March 29, 2025

Purpose Smart manufacturing (SM), a key dimension of the Industry 4.0 paradigm, envisions transformation traditional production systems into autonomous, interconnected and data-driven networks. By leveraging advanced technologies, SM promises to enhance firms’ competitiveness operational efficiency. Despite its strategic relevance, many companies still struggle achieve high levels advancement. While operations management literature has identified several technical social factors crucial for advancement, there is little empirical evidence regarding relevance each factor in path. Building on socio-technical theory, this study aims understand specific sequence which firms need deploy advance whether combinations enable higher Design/methodology/approach This combines necessary condition analysis (NCA) fuzzy-set qualitative comparative (fsQCA) using survey data from 234 automotive component industry. Findings The results NCA suggest that must be deployed SM. Initial stages adoption require limited set baseline factors, while necessitate broader array factors. Additionally, findings show advancing does not maximize magnitude every but rather ensure minimum presence critical indicating existence saturation effects. outcomes fsQCA further reveal certain strictly their own, play role within configurations, illustrating how interplay sequencing tailored drives successful Research limitations/implications Our advances theory by reframing alignment as dynamic evolving process adapts varying priorities at different uncovering stage, we provide deeper understanding can navigate complexities progress. Moreover, enrich research demonstrating enablers interact emphasizing importance tailoring strategies context-specific requirements, moving beyond static frameworks highlight stage-specific pathways drive sustainable Practical implications offer practical guidance executives, detailing allocate resources strategically adapt configurations align with unique requirements transformation, thereby facilitating more streamlined effective progression toward capabilities. Originality/value first quantitative both reach maturity

Language: Английский

Citations

0
Artificial Intelligence and MCDA in Circular Economy: Governance Strategies and Optimization for Reverse Supply Chains of Solid Waste DOI Creative Commons
Joel Joaquim de Santana Filho, Arminda Paço, Pedro Dinis Gaspar

et al.

Applied Sciences, Journal Year: 2025, Volume and Issue: 15(9), P. 4758 - 4758

Published: April 25, 2025

The integration of multi-criteria decision analysis (MCDA) and Artificial Intelligence (AI) is revolutionizing the governance reverse supply chains for solid waste (RSCSW) within a circular economy framework. However, existing literature lacks systematic assessment effectiveness these methods compared to traditional management practices. This study conducts review (SLR), following PRISMA guidelines P.I.C.O. framework, investigate how MCDA AI can optimize governance, operational efficiency, sustainability RSCSW. After collecting 1139 articles, 22 were selected used analysis. results indicate that hybrid MCDA-AI models, employing techniques, such as TOPSIS, AHP, neural networks, genetic algorithms, enhance decision-making automation, reduce costs, improve traceability. Nevertheless, regulatory barriers technological challenges still hinder large-scale adoption. proposes an innovative framework address gaps drive evidence-based public policies. findings provide policymakers managers, contributing Sustainable Development Goals (SDGs) agenda advancements in governance.

Language: Английский

Citations

0
Technological trends in mountain logistics: A patent analysis DOI Creative Commons
Mehari Beyene Teshome, Matteo Podrecca, Guido Orzes

et al.

Research in Transportation Business & Management, Journal Year: 2024, Volume and Issue: 57, P. 101202 - 101202

Published: Sept. 11, 2024

Language: Английский

Citations

1
Decision-making framework for sustainability-related supply chain risk management DOI
Ming-Fu Hsu

Computers & Industrial Engineering, Journal Year: 2024, Volume and Issue: unknown, P. 110825 - 110825

Published: Dec. 1, 2024

Language: Английский

Citations

1