Publications and Research
Document Type
Article
Publication Date
Spring 7-1-2024
Abstract
Purpose – Recent disruptions have sparked concern about building a resilient and sustainable manufacturing supply chain.While artificial intelligence (AI) strengthens resilience, research is needed to understand how cloud adoption can foster integration, collaboration, adaptation and sustainable manufacturing. Therefore, this study aimed to unleash the power of cloud adoption and AI in optimizing resilience and sustainable performance through collaboration and adaptive capabilities at manufacturing firms. Design/methodology/approach – This research followed a deductive approach and employed a quantitative method with a survey technique to collect data from its target population. The study used stratified random sampling with a sample size of 1,279 participants working in diverse manufacturing industries across California, Texas and New York. Findings – This research investigated how companies can make their manufacturing supply chains more resilient and sustainable. The findings revealed that integrating the manufacturing supply chains can foster collaboration and enhance adaptability, leading to better performance (hypotheses H1-H7, except H5). Additionally, utilizing artificial intelligence helps improve adaptability, further strengthening resilience and sustainability (H8-H11). Interestingly, the study found that internal integration alone does not significantly impact collaboration (H5). This suggests that external factors are more critical in fostering collaboration within the manufacturing supply chain during disruptions. Originality/value – This study dives into the complex world of interconnected factors (formative constructs in higher order) influencing manufacturing supply chains. Using advanced modeling techniques, it highlights the powerful impact of cloud-based integration. Cloud-based integration and artificial intelligence unlock significant improvements for manufacturers and decision-makers by enabling information processes and dynamic capability theory.
Included in
Business Administration, Management, and Operations Commons, Management Information Systems Commons, Management Sciences and Quantitative Methods Commons, Operations and Supply Chain Management Commons
Comments
Originally published in Journal of Science and Technology Policy Management. DOI 10.1108/JMTM-02-2024-0080