Forging a New Discipline: Data-driven Supply Chain Management

Data analytics has become ever more important in business and industry, driven partly by availability of data and by progress in computing. Yet the science of data analytics for supply chain management is still at a nascent stage. We are still far from directly transforming data into executable supply chain decisions.

The size and the variety of data pose a challenge. It is critical to identify the key features in the data that allow for effective decision-making in highly dynamic business environments. Everyone agrees on the need for data exchange to enable coordination mechanisms and enhance overall capability of supply chain networks, but there have been endless debates on the nature of appropriate data to be exploited. The velocity of data coming from internal operations, IoT, and public domains requires a careful choice on what data should be collected and stored. Progress can only be accomplished when machine learning/artificial intelligence expertise are integrated with domain knowledge of supply chains.

The first workshop will explore the avenues by which new methodologies and theories should be developed to push the frontier of data analytics specifically for supply chain management. We plan to explore and expand the existing data analytics approaches for supply chain management and develop data-driven structure models for addressing supply chain management issues with machine learning/artificial intelligence.