Healthcare Supply Chain Management (HSCM) advances the overarching societal objective of creating a sustainable system; however, artificial intelligence (AI) and big data (BD) have become essential facilitators of governance performance. Therefore, this chapter offers a comprehensive overview for AI and BD integration in HSCM with governance implications using PRISMA and SWOT frameworks to synthesize foundational studies published between 2015 and 2025. The results indicate that AI and BD support accurate predictions, optimize processes, and make overall systems resilient. However, ongoing challenges—such as data interoperability, algorithmic transparency, cybersecurity risks, and ethical governance—still hinder effective implementation. Finally, the study embraces theoretical principles and validation with governance-focused assessments, suggesting a sustainable use of AI and BD in HSCM co-benefits.

The predominant of multilateral international business between Bangladesh and China has expanded significantly in current times. The business friendship between two partners in improving across will deliver much assistance to the economy of both nations. However, some of the researchers have properly explored the familiarity between these countries. The initial objective is to emphasize and highlights the international export trade between these two partners, particularly, the opportunities in and importance of principal aptitude of trade. To detail the business model, we here apply the trade intensity index. The findings have been used to find strong areas for future contributions between these two partners to strengthen the economy and to seek diplomacy involvement for ongoing trade and monetary prosperity.

This chapter examines how big data analytics is reshaping global supply chains by enabling a shift toward sustainability, resilience, and responsible decision-making. It explores the integrative roles of Internet of Things (IoT) generated data, artificial intelligence (AI), and explainable AI (XAI) in enhancing visibility, predictive capability, and managerial accountability across complex supply networks. The discussion highlights how data-driven insights support energy efficiency, emissions reduction, risk anticipation, and adaptive operational responses, while emphasizing the importance of governance frameworks in addressing cybersecurity, data privacy, and ethical concerns. By synthesizing technological, managerial, and policy perspectives, the chapter demonstrates that sustainable and resilient supply chain transformation depends not only on advanced analytics but also on transparency, trust, and robust data stewardship in an increasingly digitalized global environment.

Sustainable supply chains often face challenges of operational uncertainty and institutional constraints as data-driven decision-making is complex process. Classical AI/ML focuses on efficiency and technical performance often overlooking employee well-being and transparent decision-making. The existing studies link explainable AI with transparency and interpretability, while scalability and optimizing efficiency are valuable implications of quantum computing. This demands to develop a conceptual framework for quantum XAI (QXAI) as a unique socio-technical resource exploring both technical and cognitive aspects. Therefore, this chapter situates QXAI as a dual-nature job resource within Job Demands Resources (JD-R) theory, through an integrative and semi-systematic review approach that can enhance quality decision making, employee well-being and sustainable performance in context of supply chain industry. The chapter further demonstrates that QXAI can concurrently improve operative competence, reinforce environmental authority, and endorse social sustainability in emerging economies.