A Data-Driven Analysis for Engineering Conferences: The Institute of Industrial and Systems Engineering (IISE) Annual Conference Proceedings (2002-2025)

TL;DR

This study employs computational methods, including NLP and network analysis, to map the thematic evolution, influential works, and community structure of the Industrial and Systems Engineering field over two decades, providing a comprehensive historical overview.

Contribution

It introduces a large-scale, data-driven approach using LLMs and network science to analyze the evolution of ISE research from 2002 to 2025, which is novel in scope and methodology.

Findings

Identified key research themes and their evolution over time

Mapped influential papers and authors in ISE

Revealed collaboration and citation networks within the community

Abstract

Charting the intellectual evolution of a scientific discipline is crucial for identifying its core contributions, challenges, and future directions. The IISE Annual Conference proceedings offer a rich longitudinal archive of the Industrial and Systems Engineering (ISE) community's development, but the sheer volume of scholarship produced over two decades makes a holistic analysis difficult. Traditional reviews often fail to capture the full scale of thematic shifts and complex collaboration networks that define the community's growth. This paper presents a computational analysis of IISE proceedings from 2002 to 2025, drawing on an initial dataset of 9,350 titles from ProQuest for thematic analysis and 8,958 titles from Google Scholar for citation analysis, to deliver a cartography of the ISE field's intellectual history. Leveraging Large Language Models (LLMs) for domain-aware classification, Natural Language Processing, and Network Science, our study systematically maps thematic evolution to identify dominant, emerging, and receding research topics. We analyze citation data and co-authorship networks to uncover influential papers and authors, providing critical insights into knowledge diffusion and community structure. Through this comprehensive analysis, we establish a baseline for understanding the trajectory of ISE research and offer valuable insights for researchers, practitioners, and educators. The findings illuminate the field's intellectual assets and provide a data-informed map to guide the future of ISE. To foster reproducibility and further research, the curated dataset used in this study and the results will be made publicly available.