Application of Artificial Intelligence to Improve Production Process Efficiency in Manufacturing Industry

The rapid advancement of Artificial Intelligence (AI) has profoundly impacted the manufacturing industry, offering transformative potential to enhance production process efficiency. This paper presents a systematic literature review of AI applications in the manufacturing sector, focusing on key AI technologies such as machine learning, robotics, predictive analytics, and natural language processing. The review highlights how these technologies have improved quality control, resource management, and overall operational performance. However, the adoption of AI also presents challenges, including significant investment costs, the need for a skilled workforce, and concerns over data security and privacy. Despite these challenges, the integration of AI in manufacturing presents numerous opportunities for future research and innovation, particularly in the areas of sustainable manufacturing and the convergence of AI with other emerging technologies. This study concludes that while AI offers substantial benefits for production efficiency, its successful implementation requires careful strategic planning and investment in both technology and human resources.

Authors

Institutions

Publication Details

Journal
West Science Information System and Technology
Published
2024-08-30
DOI
https://doi.org/10.58812/wsist.v2i02.1221
Citations
4
Primary Topic
Digital Transformation in Industry
Type
article
Field-Weighted Citation Impact
1.40
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Application of Artificial Intelligence to Improve Production Process Efficiency in Manufacturing Industry

Suharni Suharni, Deddy Rakhmad Hidayat, Lucky Mahesa Yahya, Arnes Yuli Vandika
4 citations
West Science Information System and Technology
Digital Transformation in Industry
1.40
article

Application of Artificial Intelligence to Improve Production Process Efficiency in Manufacturing Industry

Suharni Suharni, Deddy Rakhmad Hidayat, Lucky Mahesa Yahya, Arnes Yuli Vandika
article en
4 citations

Abstract

The rapid advancement of Artificial Intelligence (AI) has profoundly impacted the manufacturing industry, offering transformative potential to enhance production process efficiency. This paper presents a systematic literature review of AI applications in the manufacturing sector, focusing on key AI technologies such as machine learning, robotics, predictive analytics, and natural language processing. The review highlights how these technologies have improved quality control, resource management, and overall operational performance. However, the adoption of AI also presents challenges, including significant investment costs, the need for a skilled workforce, and concerns over data security and privacy. Despite these challenges, the integration of AI in manufacturing presents numerous opportunities for future research and innovation, particularly in the areas of sustainable manufacturing and the convergence of AI with other emerging technologies. This study concludes that while AI offers substantial benefits for production efficiency, its successful implementation requires careful strategic planning and investment in both technology and human resources.

West Science Information System and TechnologyVol. 2(02)
Universitas Bandar Lampung (ID), Universitas Islam Makassar (ID), Andalas University (ID)
Openalex Percentile: Top 17%
Digital Transformation in Industry
1.40
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.

Application of Artificial Intelligence to Improve Production Process Efficiency in Manufacturing Industry — Suharni Suharni, Deddy Rakhmad Hidayat, et al. · West Science Information System and Technology (2024) | TGRS Research Map | TGRS