Data-Driven Algorithms for Optimal Decision Making in Logistics and Supply Chain Management

Dear Colleagues,

In today's dynamic and ever-evolving business landscape, logistics and supply chain management have become increasingly intricate, necessitating efficient decision-making processes to ensure seamless operations for goods, information, and resources. Leveraging the transformative potential of Industry 4.0 technologies, an unprecedented abundance of data becomes available, presenting unique opportunities to derive valuable insights and intelligent decision-making in logistics and supply chain management. Mastering the art of harnessing the available data to comprehend past events, monitor current happenings, predict future trends, and execute optimal decisions has emerged as a key competitive advantage for companies across industries. Data analytics, particularly predictive analytics and prescriptive analytics, play pivotal roles in addressing the dynamic and uncertain business environments prevalent in logistics and supply chain management. By integrating cutting-edge technologies and data-driven methodologies, this field now faces unprecedented opportunities and challenges.

The primary goal of this Special Issue is to curate groundbreaking research on data-driven algorithms and their practical applications in enabling efficient decision-making within logistics and supply chain management. We invite original contributions that delve into the theoretical, methodological, and practical aspects of data-driven algorithms, with a special focus on their profound influence on optimal decision-making processes across various domains within logistics and supply chain management. This Special Issue welcomes exemplary contributions from both academia and industry, centered on data-driven algorithms for optimal decision-making in logistics and supply chain management. We are encouraging the submission of original papers encompassing a wide array of topics, including, but not limited to:

• Autonomous warehouse systems: Exploring advanced automation techniques to optimize warehouse operations and enhance overall efficiency.
• Big data analytics: Unveiling the hidden treasures in vast data sets, empowering businesses to make data-backed optimal decisions.
• Consumer behavior modeling and prediction: Utilizing data-driven approaches to better understand and anticipate consumer behavior patterns.
• Data-driven sales & operations planning (S&OP): Enhancing the planning process through data-backed insights to optimize sales and operations coordination.
• Digital supply chain management: Examining the pivotal role of digital technologies in revolutionizing supply chain operations.
• End-to-end supply chain integration and optimization: Addressing challenges and opportunities in achieving comprehensive supply chain optimization.
• Intelligent demand forecasting: Implementing AI-driven forecasting techniques for more accurate demand predictions.
• Intelligent manufacturing systems: Integrating data-driven intelligence into manufacturing processes for enhanced efficiency and adaptability.
• Intelligent transportation systems: Leveraging data analytics to optimize transportation operations and logistics.
• Inventory track and trace: Implementing real-time tracking solutions to optimize inventory management.
• Learning algorithms: Incorporating learning algorithms to develop intelligent systems capable of adaptability and continuous improvement.
• Mathematical models: Utilizing mathematical models to provide a rigorous foundation for decision-making processes.
• Predictive maintenance systems: Utilizing predictive analytics to optimize maintenance operations and minimize downtime.
• Predictive inventory management and optimization: Optimizing inventory levels based on predictive analytics to reduce costs and improve responsiveness.
• Route optimization in transportation and distribution: Applying data-driven algorithms to optimize route planning and delivery logistics.
• Supplier selection intelligence: Employing data-driven methods for optimal supplier selection and management.
• Sustainable supply chain management: Investigating how data-driven optimization can support environmentally responsible supply chain practices.

Dr. Xue-Ming Yuan
Dr. Hao Yu
Guest Editors

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• big data analytics
• data-driven algorithms
• optimal decision making
• intelligent systems
• learning algorithms
• mathematical models