A continuous training approach for risk informed supplier selection and order allocation DOI Creative Commons
Matteo Gabellini,

Stephen Mak,

Stefan Schoepf

и другие.

Production & Manufacturing Research, Год журнала: 2024, Номер 13(1)

Опубликована: Дек. 27, 2024

Язык: Английский

The Interplay between Digital Technologies, Supply Chain Resilience, Robustness and Sustainable Environmental Performance: Does Supply Chain Complexity Matter? DOI Open Access
Abdelmoneim Bahyeldin Mohamed Metwally, Hesham Ali Ahmed Ali, Saleh Aly Saleh Aly

и другие.

Sustainability, Год журнала: 2024, Номер 16(14), С. 6175 - 6175

Опубликована: Июль 19, 2024

This study aims to investigate the mediating role of supply chain resilience and robustness on relationship between use digital technologies sustainable environmental performance. Additionally, it investigates moderating complexity impact robustness. Data were gathered from 292 managers at registered manufacturing companies in Egypt analyzed using Smart-PLS 4 software. The findings reveal that partially mediate link Moreover, was found positively moderate effect both model explained 53.2% variance robustness, 56.6% resilience, 72.3% These results provide critical insights for corporate policymaking, helping drive continuous improvements management, performance, development.

Язык: Английский

Процитировано

5

Artificial intelligence and machine learning for resilient and sustainable logistics and supply chain management DOI

Nitin Rane,

Saurabh Choudhary, Jayesh Rane

и другие.

SSRN Electronic Journal, Год журнала: 2024, Номер unknown

Опубликована: Янв. 1, 2024

Integrating artificial intelligence (AI) and machine learning (ML) into logistics supply chain management is crucial for enhancing resilience efficiency in today's unpredictable global market. This paper explores the latest advancements applications of AI ML technologies that are transforming operations. AI-driven predictive analytics real-time data processing have enabled companies to anticipate disruptions, optimize routes, improve demand forecasting accuracy. Machine algorithms essential identifying patterns anomalies within large datasets, supporting proactive decision-making risk management. Current trends indicate a growing use autonomous delivery systems, which aim reduce human error times. Additionally, AI-enhanced blockchain technology becoming more popular its ability increase transparency traceability across chain, ensuring ethical sourcing reducing fraud. inventory has significantly minimized overstocking stockouts by providing accurate levels automating replenishment processes. Furthermore, AI-powered systems increasingly adopted streamline supplier selection performance evaluation, creating resilient networks. research highlights pivotal role agile, responsive, capable withstanding quickly recovering from disruptions. The findings emphasize necessity ongoing innovation adoption these maintain competitive edge sustainability amid challenges.

Язык: Английский

Процитировано

3

Does AI elevate corporate ESG performance? A supply chain perspective DOI
Boqiang Lin, Yitong Zhu

Business Strategy and the Environment, Год журнала: 2024, Номер unknown

Опубликована: Окт. 16, 2024

Abstract As a pivotal catalyst for sustainable development, the evolution and integration of AI are propelling both companies society toward more efficient trajectory. Utilizing multi‐period difference‐in‐difference (DID) model, study assesses impact 2019 China Artificial Intelligence Pilot (AIP) policy on corporate environmental, social, governance (ESG). The study's findings following: (1) Optimizing through artificial intelligence (AI), AIP has significantly bolstered ESG performance in pilot areas. (2) Mechanistic analysis demonstrates that elevates by bolstering efficiency supply chains. (3) Heterogeneity testing shows exerts pronounced effect non‐state‐owned companies, with high energy consumption, those new sector. This manuscript furnishes empirical insights evaluating implications development initiatives.

Язык: Английский

Процитировано

3

AI-Enabled Supply Chain Management: A Bibliometric Analysis Using VOSviewer and RStudio Bibliometrix Software Tools DOI Open Access
Mihaela Gabriela Belu,

Ana Maria Marinoiu

Sustainability, Год журнала: 2025, Номер 17(5), С. 2092 - 2092

Опубликована: Фев. 28, 2025

Artificial intelligence (AI) is fundamentally transforming the management of supply chain activities, offering companies opportunity to configure resilient, transparent, and sustainable chains. Given its importance, this paper presents aspects implementation artificial in by performing a bibliometric analysis 400 scientific papers published between 2010 2024 indexed Scopus database. The was based on Bibliometrix 4.4.2 VOSviewer 1.6.19 software identify most important authors journals interest for researched topic. Keyword co-occurrence co-citation analyses were used map intellectual networks highlight themes interest. research results confirm increase field applying AI management, highlighting advantages implementing technology management. At same time, recommendations conclusions will be useful both academic researchers business professionals potential areas collaboration with aim developing strategies that contribute competitiveness are part network.

Язык: Английский

Процитировано

0

Artificial intelligence as a tool for item reduction in an organizational resilience questionnaire DOI
Ivan Mihajlović, Nikola Petrović, Vesna Spasojević-Brkić

и другие.

International Journal of Occupational Safety and Ergonomics, Год журнала: 2025, Номер unknown, С. 1 - 14

Опубликована: Март 3, 2025

Objectives. Considering that there is no standardized questionnaire for safety climate and resilience assessment, authors usually review a large number of questionnaires from the available literature, which results in high questions distributed to respondents. As length increases, resistance respondents increases. Artificial intelligence (AI) tools until now have not been used item reduction, besides need selecting retaining only most relevant informative with adequate accuracy. Methods. AI such as multiple linear regression analysis (MLRA) multilayer perceptron artificial neural network (MLP ANN) are development model able cluster respondents' ratings predict values organizational based on specific questions. Results. could be valuable tool since prediction accuracy MLRA 70.4–71.5% MLP ANN it 76.4%. Conclusions. This research proves machine learning algorithms can build predictive models determine survey index calculation using factors.

Язык: Английский

Процитировано

0

How Does Artificial Intelligence Shape Supply Chain Resilience? The Moderating Role of the CEOs’ Sports Experience DOI Creative Commons
Yuxuan Xu,

Yu Hua,

Ran Qiu

и другие.

Systems, Год журнала: 2025, Номер 13(3), С. 190 - 190

Опубликована: Март 9, 2025

In the volatility, uncertainty, complexity, and ambiguity (VUCA) environment, application of artificial intelligence (AI) technologies is a key engine for shaping supply chain resilience (SCR). This study employs entropy method to develop an evaluation index system SCR, incorporating two dimensions: resistance recovery capacity. Using sample Chinese-listed enterprises from 2009 2022, this reveals that AI significantly enhances CEOs’ sports experience can positively moderate association between SCR. Mechanism examination shows promotes SCR through operational efficiency optimization, information, knowledge spillover in chain. Heterogeneity analysis positive impact more significant firms with high-skilled labor force, high heterogeneity executive team’s human capital, high-tech industries, regions strong digital infrastructure. Moreover, has diffusion effect on upstream downstream chain, improving adoption levels. Our research not only augments existing literature economic ramifications strategic value derived extramural but also offers both theoretical frameworks empirical insights recruitment fortifying

Язык: Английский

Процитировано

0

Artificial intelligence in healthcare logistics – moderating role of industry pressure and organisational readiness DOI
Aman Sharma, Bhuvanesh Kumar Sharma, Vimal Bhatt

и другие.

Journal of Decision System, Год журнала: 2025, Номер 34(1)

Опубликована: Янв. 2, 2025

Язык: Английский

Процитировано

0

Modelling supply chain risk events by considering their contributing events: a systematic literature review DOI Creative Commons
Maryam Shahsavari, Omar Khadeer Hussain, Pankaj Sharma

и другие.

Enterprise Information Systems, Год журнала: 2025, Номер unknown

Опубликована: Март 24, 2025

Proactive Supply Chain Risk Management (SCRM) helps organisations anticipate and mitigate risks, ensuring business continuity resilience in a violet market. Existing research proposes various techniques to quantify risk occurrence, but none account for the causal relationships between contributing events events. This paper addresses this gap through systematic literature review of SCRM outlines future directions enhance proactive by incorporating dependencies quantification.

Язык: Английский

Процитировано

0

The Transformative Role of ML Algorithms in Supply Chain Management: A Systematic Literature Review DOI

Khaoula Elkabtane,

Touria Benazzouz,

Samya Dahbi

и другие.

Lecture notes in networks and systems, Год журнала: 2025, Номер unknown, С. 211 - 220

Опубликована: Янв. 1, 2025

Язык: Английский

Процитировано

0

Supply chain resilience capabilities: an outlook of the Brazilian agri-food sector post Covid-19 pandemic DOI

Jefferson Capelli,

Paulo Renato de Sousa, Marcelo Werneck Barbosa

и другие.

International Journal of Disaster Risk Reduction, Год журнала: 2025, Номер unknown, С. 105486 - 105486

Опубликована: Апрель 1, 2025

Язык: Английский

Процитировано

0