Acceso usuarios
Fernando Rojas Zuñiga
, Peter Wanke, Yong Tan
Objectives: The study aims to perform a bibliometric analysis of research on stochastic demand inventory models within pharmaceutical supply chains, focusing on identifying key trends, influential contributions, and collaboration networks that have shaped the field between 2018 and 2024.
Theoretical Framework: The analysis is grounded in the theory of inventory management under uncertainty, particularly in the context of the pharmaceutical industry, where demand variability presents significant challenges for supply chain optimization.
Method: A systematic review of 39 articles published between 2018 and 2024 was conducted. Keywords related to stochastic demand and pharmaceutical supply chain inventory management were used to select the studies. Bibliometric tools were employed to evaluate publication trends, citation counts, author collaboration networks, and emerging research topics.
Results and Discussion: The analysis revealed a concentration of research efforts on optimizing inventory levels to manage uncertain demand. Key authors in the field have contributed significantly, as reflected in high citation counts, establishing them as influential figures. The study also notes an increasing trend of interdisciplinary and international collaborations, along with the adoption of advanced stochastic modeling techniques.
Research Implications: The findings suggest that further research should continue exploring interdisciplinary approaches and advanced modeling techniques, as these areas show potential for practical advancements in managing pharmaceutical supply chains under uncertainty.
Originality/Value: This study offers a focused bibliometric analysis of the recent developments in stochastic demand modeling for pharmaceutical supply chains. Its value lies in highlighting current research trends, identifying influential contributors, and offering insights into future directions, making it a useful reference for researchers and practitioners in the field.
