Operations research widely applies existing scientific and technological knowledge and mathematical methods to solve specific problems in supply chain management and provides a basis for decision-makers to choose the best decision. The basic methods of operations research include mathematical methods, statistical methods, computer science methods, etc. In particular, optimization methods are very essential methods. In recent years, operations research has been continuously innovated and developed. New models, new theories, and new methods have emerged in the research. They have provided solutions for various complex supply chain management problems. For instance, they have solved complex supply chain management problems in terms of linear and nonlinear, continuous, and discrete, certainty and uncertainty systems.

Supply chain management research has become widely popular. However, there are still some interesting and challenging problems in technology and methods that are worth further exploring. Data-driven operations research is one of the most popular trends in current academic research. Due to the lack of a rigorous theoretical system, there is no unified definition. Data-driven is to use data as a means of production of extracted features through scientific methods and apply them to problems to be solved. Data-driven methods have certain applicability and advantages in the research of supply chain management. Therefore, there is a need for more scholars to conduct research and drive innovation in supply chain management by using operations research from a data-driven perspective.

The aim of this Special Issue is to bring together original research and review articles discussing the latest developments in data-driven operations research in supply chain management. We welcome submissions that present new ideas and discuss the future of operations research in supply chain management. Research including novel mathematical theories, methods, and applications addressing challenges in data-driven operations research within supply chain management is encouraged.

Potential topics include but are not limited to the following:

• Operations research and regression in supply chain management
• Operations research and clustering in supply chain management
• Operations research and classification in supply chain management
• Operations research and outlier detection in supply chain management
• Data-driven supply chain management
• Emergency management in supply chain management
• Data envelopment analysis in supply chain management
• Group decision-making analysis in supply chain management
• Emergency facilities location and material scheduling in supply chain management
• Multi-criteria decision analysis and application in supply chain management
• Robust optimization in supply chain management
• Stochastic optimization in supply chain management
• Fuzzy programming in supply chain management