Impact of Artificial Intelligence Adoption on Manufacturing Efficiency in the United States
DOI:
https://doi.org/10.47672/ejt.1855Keywords:
Artificial Intelligence, Adoption, Manufacturing EfficiencyAbstract
Purpose: The aim of the study was to assess the impact of artificial intelligence adoption on manufacturing efficiency in the United States.
Methodology: This study adopted a desk methodology. A desk study research design is commonly known as secondary data collection. This is basically collecting data from existing resources preferably because of its low cost advantage as compared to a field research. Our current study looked into already published studies and reports as the data was easily accessed through online journals and libraries.
Findings: The adoption of artificial intelligence (AI) in manufacturing has led to significant improvements in efficiency across various facets of the industry. AI technologies, including machine learning algorithms and predictive analytics, have enabled manufacturers to optimize production processes, reduce downtime, and enhance quality control. By analyzing vast amounts of data in real-time, AI systems can identify patterns and anomalies, allowing for proactive maintenance and minimizing the risk of equipment failures. Additionally, AI-driven automation has streamlined tasks such as inventory management and supply chain logistics, leading to cost savings and faster time-to-market. Furthermore, AI-powered robotics and cobots have revolutionized assembly lines, increasing productivity and flexibility while ensuring worker safety.
Implications to Theory, Practice and Policy: Resource-based theory, technology-organization-environment framework and institutional theory be use to anchor future studies on assessing the impact of artificial intelligence adoption on manufacturing efficiency in the United States. Facilitate knowledge exchange platforms and networks where manufacturing firms can share best practices, challenges, and lessons learned from AI adoption initiatives. Collaborate with industry stakeholders to develop regulatory frameworks and standards that promote responsible AI adoption in manufacturing, addressing concerns related to data privacy, cybersecurity, and ethical use of AI technologies.
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