Conducted by PIT
, Started on 2025 -
Completed on 2025
Completed
Published
Total Page Views : 10
Total Likes : 4
Like
Artificial Intelligence (AI) has become a transformative force in operational domains such as manufacturing,
logistics, scheduling, and cloud-based systems. The rapid growth of research output, particularly during
2025–2026, necessitates a systematic bibliometric assessment to identify emerging themes, intellectual
structures, and influential contributors in AI-driven operations research. This study employed a
quantitative bibliometric design using the Biblioshiny interface of the Bibliometrix R package. Data were
retrieved from Scopus and limited to publications from 2025–2026 that explicitly addressed AI applications
in operational contexts. Performance indicators (publication productivity and citation impact) and science
mapping techniques—including co-authorship analysis, keyword co-occurrence, thematic clustering, and
network centrality measures—were applied. Results show a strong concentration of publications in 2025,
reflecting a rapidly accelerating research front. China emerged as the leading contributor, with South China
University of Technology among the most productive institutions. Thematic analysis identified three
dominant clusters—reinforcement learning, scheduling algorithms, and smart manufacturing—along with
an emerging fabrication-focused cluster. Strong interconnections among themes indicate increasing
convergence between AI methodologies, operational optimization, and Industry 4.0 applications. Due to
the recency of publications, citation counts were limited; thus, network-based metrics such as PageRank
proved more effective in capturing early scholarly influence. Overall, AI-driven operations research is
expanding rapidly, with a clear shift from theoretical development toward applied innovation, underscoring
the need for ongoing monitoring, interdisciplinary collaboration, and strategic policy and industry
engagement.