The integration of Artificial Intelligence (AI) into supply chain management (SCM) is transforming traditional operations into intelligent, resilient, and sustainable ecosystems. This book explores the application of AI-driven models to enhance decision-making, optimize logistics, and minimize environmental impact across various stages of the supply chain. Emphasis is placed on how machine learning, deep learning, and predictive analytics enable proactive demand forecasting, waste reduction, and resource optimization. The research proposes a sustainability-centered framework that combines AI-based optimization with environmental, social, and governance (ESG) principles to achieve transparency, traceability, and ethical sourcing. The findings demonstrate that AI adoption not only strengthens supply chain agility and resilience but also contributes significantly to achieving Sustainable Development Goals (SDGs) by promoting circular economy practices and carbon footprint minimization.
Features:
• Includes a step-by-step procedure for developing the Blockchain Internet of Things for industries using D-APP and hyperledgers.
• Covers different blockchain and Internet of Things-based sustainable supply chain management merits, demerits, challenges, and risks.
• Delves into the integration of artificial intelligence into smart contracts to foster responsible and efficient operations across industries.
• Showcases the use of artificial intelligence algorithms within smart contract frameworks to optimize decision-making processes and ensure ethical considerations are prioritized in automated contract executions.
• Explores how artificial intelligence-driven solutions are being tailored to meet the unique demands of different sectors, including manufacturing, healthcare, retail, agriculture, and logistics.
It is primarily written for senior undergraduates, graduate students, and academic researchers in electrical engineering, electronics and communications engineering, computer science and engineering, supply chain management, manufacturing engineering, and industrial engineering.