American Journal of Business and Operations Research

Through the article, the financial situation of some large joint-stock companies in the world is analyzed in depth, and based on the collected data, some mechanisms and methods of improving the financial support of joint-stock companies in the Republic of Uzbekistan are presented, and some suggestions and explanations are given about ways to use them effectively. developed. In the conclusion and suggestions part of the article, we will see some explanations for the shortcomings encountered in the joint-stock companies of our country and their elimination.

Several research on the topic of supply chain resilience and sustainability have been done in recent years. However, they make clear that there are various points of view when it comes to the sustainability-resilience relationship. To adapt supply chains (SC) to the needs of contemporary manufacturing processes, new trends and approaches in environmental protection and social welfare have been put into place. Even though sustainability and resilience have each been extensively examined separately, there aren't many concepts that combine them to determine supply chain performance. Therefore, this study is displaying the aspects of supply chain resilience and how it may affect sustainability triple bottom line. Moreover, this study presents an extension of analytic hierarchy process (AHP), α-Discounting multi-criteria decision-making (α-D MCDM) to evaluate the resilience aspects in more consistent manner. This study proposes an idea of utilization of α-D MCDM in different manner to solve several supply chain evaluation issues.  

A wide variety of approaches, strategies, and tools for designing, implementing, managing, and analyzing functional business processes have emerged from studies in business process management (BPM). It is the goal of the emerging topic of research known as "process mining" (PM) to improve the analysis of business process models by gleaning actionable insights from massive quantities of event logs. The purpose of this study is to research business process management and process mining by surveying the state-of-the-art methods and tools in each area and highlighting the most recent developments. This study concludes with a discussion of BPM and PM, in which PM acts as a bridge between BPM and data science to enhance business processes (BPs).

 Supply chain networks are complex systems that involve multiple entities and activities, making them vulnerable to various risks that can negatively impact their performance. Game theory models have been used in various fields to analyze strategic interactions among agents and to make decisions in uncertain environments. This study investigates the application of game theory models for risk management in supply chain networks. Then, we present a framework for applying game theory models for risk management in supply chain networks. Our framework consists of three stages: risk identification, risk analysis, and risk mitigation. We validate the application of the proposed framework using a case study of a supply chain network for a fictional company. The results of the case study demonstrate that game theory models can provide valuable insights into the behavior of supply chain entities in different risk scenarios. The models can also help in identifying optimal strategies for mitigating risks and improving the performance of the supply chain network. The finding  imply that the proposed framework can be used as a guide for practitioners to apply game theory models in their supply chain risk management practices.

The integration of the Internet of Things (IoT) and Big Data Analytics (BDA) has brought about a revolution in Green Supply Chain Management (GSCM). In particular, it has enabled the optimization of many aspects of the supply chain (SC), including transportation, inventory management, and customer service. The application of BDA in IoT-enabled GSCM is receiving a lot of attention because it has the capacity to assist businesses become more cost-effective and environmentally sustainable to make more informed decisions. By identifying the inefficiencies in the supply chain and take corrective action. With the advent of the IoT, businesses are now able to get a great deal of information from sensors that are installed in different parts of their SC, including transportation vehicles, warehouses, and factories. This data can be leveraged for a variety of purposes, including optimizing the SC for sustainability and reducing its environmental impact. There are also challenges associated with BDA in IoT-enabled GSCM. The volume of data that needs to be processed presents the biggest obstacles. This requires specialized tools and expertise in data management and analytics. Despite these difficulties, technology has the power to completely alter how firms conduct their operations. This paper presents an overview about BDA in IoT-enabled GSCM. The review highlights the benefits and challenges in adopting BDA in IoT-enabled GSCM, the key technologies involved, and the various applications of BDA in IoT-GSCM. Finally, provides insights into the future directions of research in this area.

Customer Relationship Management (CRM) is a crucial aspect of modern business that enables companies to maintain healthy relationships with their customers. In today's digital age, customers interact with companies through multiple channels, including social media, email, and phone. Therefore, analyzing customer feedback and sentiment has become increasingly important in understanding their needs and improving the overall customer experience. To this end, this work proposes a new system that applies deep learning for sentiment analysis in a way that improves the performance of CRM by analyzing customer feedback from various sources, companies can gain valuable insights into customer needs and preferences and identify areas for improvement in their products and services. Then, we present a case study of a company that implemented the proposed system in its CRM strategy. The results showed that our system could improve customer satisfaction and retention rates and enable the company to identify and address customer concerns more efficiently.Our approach can be applied as a powerful tool to enable companies to gain valuable insights into customer needs and preferences, identify areas for improvement in their products and services, and develop targeted marketing campaigns and personalized communication strategies.

This paper proposes an integrated machine learning (ML) framework for dynamic resource allocation in a multi-project environment. The framework utilizes machine learning algorithms to predict future resource demands and identify potential resource shortages. The proposed framework considers various factors such as project priorities, resource availability, and project deadlines to optimize resource allocation decisions. The framework is designed to continuously learn from past resource allocation decisions and improve future resource allocation strategies. The effectiveness of the proposed framework is evaluated through a case study in a real-world multi-project environment. The results show that the framework can significantly improve resource utilization and project completion times while reducing resource waste and cost. Overall, the proposed framework provides a practical solution for dynamic resource allocation in complex multi-project environments.