Conference Agenda

We model a supply chain in which the buyer audits the supplier’s compliance with the social and environmental code of conduct. We investigate the effect of the buyer’s upfront commitment to price and quantity on supplier’s compliance to the sustainability code of conduct.

This study centers on refining the allocation of waterborne containerized imports from Asia to U.S. ports, with a specific focus on comparing the East Coast and West Coast options. The objective is to enhance shipping routes, reduce lead time, minimize transportation costs, and curtail unnecessary fuel consumption and emissions. The project's significance lies in its potential contribution to sustainability and environmental conservation. Utilizing simulation models developed in any Logistix, the ongoing analysis aims to identify the most efficient allocation strategy, providing valuable insights for optimizing the flow of containerized imports and promoting eco-friendly practices within the global supply chain.

Liver transplantation is the gold standard treatment for irreversible liver failure, providing a lifeline to thousands of patients annually in the United States. Despite its success, the scarcity of donor organs remains a critical challenge, leading to significant mortality rates among patients awaiting transplantation. In this study, we explored machine learning models to predict the 90-day survival of liver transplant recipients. Among the models evaluated, logistic regression demonstrated the highest performance, achieving an AUC score of 0.70. Our findings highlight the potential of machine learning in improving patient outcomes and optimizing organ allocation strategies.

Companies often avoid collaborating with global partners involved in illegal activities like tax evasion, which can significantly affect their financial performance. This study proposes a methodology to detect companies and their supply chain partners suspected of tax fraud. By integrating social network analysis and machine learning algorithms, our approach identifies tax evasion networks. Initially, we model the social network of taxpayers (manufacturers and suppliers), then pinpoint individual taxpayers suspected of tax fraud. Subsequently, we analyze the taxpayers' network to detect communities engaged in tax evasion. The proposed tool aids managers and tax auditors in assigning resources efficiently to investigate high-risk companies and their supply chain partners, thereby streamlining audits and reducing operational costs.