Abstract: This study aimed to examine the effect of raw material inventory and production costs on company net profit. The dependent variable is the net profit of manufacturing companies, while the independent variables are raw material inventory and production costs consisting of raw material costs, direct labor costs, and factory overhead costs. The population of this study was manufacturing companies in the consumer industry sub-sector that were listed on the Indonesia Stock Exchange (IDX) during the period 2018–2020. Sampling was based on purposive sampling using the criteria of consumer industry companies listed on IDX during 2018–2020, which used the rupiah as the currency in their financial reports, and had complete financial report data. Multiple linear regression was employed as the data analysis technique. The results show that raw material inventory had no effect on company profits, raw material costs had a significant positive effect on company profits, direct labor costs had a significant positive effect on company profits, and factory overhead costs had no significant effect on company profits. The coefficient of determination (R2) shows that 14.4% of company profits in the consumer industry sub-sector for the period 2018–2020 can be explained by raw material inventories, raw material costs, direct labor costs, and factory overhead costs.

	© 2010 by the authors; licensee Growing Science, Canada. This is an open access article distributed under the terms and conditions of the license. Creative Commons Attribution (CC-BY)

Abstract: The rapid evolution of Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) has profoundly influenced various domains, including portfolio optimization. In today’s dynamic and interconnected global economy, understanding the development of scientific publications in this field is crucial for both academics and practitioners. This paper aims to conduct a comprehensive bibliometric study of the scientific literature on portfolio optimization, focusing on the impact of AI, ML, and DL advancements. By analyzing key trends, influential publications, and emerging research areas, this study provides valuable insights into the progression of portfolio optimization research in the context of these transformative technologies, helping to map future directions and identify knowledge gaps in the field. This paper endeavors to present an exhaustive synthesis of the most recent advancements and innovations within the domain of portfolio optimization, particularly as influenced by progressive developments in AI, ML and DL from 1996 to 2024. Employing a rigorous bibliometric analysis, this study scrutinizes the structural and global paradigms governing this field. The analytical framework integrates several dimensions, including: (1) comprehensive dataset interrogation, (2) critical evaluation of source repositories, (3) contributions of seminal authors, (4) geographical and institutional affiliations, (5) document- centric analysis, and (6) exploration of keyword dynamics. A corpus of 745 bibliographic entries, meticulously curated from the Web of Science database, forms the basis of this inquiry, which utilizes advanced Scientometric network methodologies to extrapolate substantive research insights. The discourse culminates in a robust critique of the inherent strengths and methodological limitations, while delineating strategic avenues for future research, with the objective of steering ongoing scholarly discourse in the realm of portfolio optimization. The empirical outcomes of this study enhance the understanding of prevailing intellectual trajectories, thus laying a fortified foundation for future investigative pursuits in this critically evolving discipline.

	© 2010 by the authors; licensee Growing Science, Canada. This is an open access article distributed under the terms and conditions of the license. Creative Commons Attribution (CC-BY)