Mission and scientific program

//Mission and scientific program
Mission and scientific program2018-11-12T11:34:23+00:00


Data analytics and real-time decision making play a prominent role in revolutionizing the supply chain industry today. The research program in supply chain analytics aims to transform the planning and execution processes through scientific developments that make use of operations research and machine learning techniques, and to bridge the gap between academics and practitioners.

The main goal is to enable, (1) seamless integration of planning and real-time execution to deal with uncertainty, (2) supply chain resilience to cope with unexpected disruptions, and (3) supply chain intel-ligence to constantly learn and adapt to the rapid changes in the environments around them.

Scientific program

The applications of interest in the research program cover the main areas of supply chain and retail analytics:

  • Predictive analytics for demand and inventory management in retails
    The focuses of this research area are to enable time-varying probabilistic forecast for inventory controls, to predict and analyze product interaction due to similarity and complementarity, and to develop nonparametric approach to multi-echelon inventory planning with dynamic demand in omni-channel retail and supply chain networks.
  • Prescriptive analytics for integrated supply chain planning under uncertainty
    This research area addresses dynamic and stochastic supply chain planning under uncertainty to breakdown the silos in the supply chain decision making process, which enables the manufacturer’s capabilities to anticipate and mitigate disruptions in MRP/DRP, allows the fulfillment planner to develop a robust and holistic plan, to review it and react promptly to respond to the changes in the operational environments.
  • Fundamental research to bridge the gap between operations research (OR) and machine learning (ML) in the decision making processes to tackle real-world problems.
    In particular, we aim to develop the capability to enable the synergies between the techniques in OR and ML, and to leverage practical knowledge in supply chain management in order to create practical data-driven initiatives for private and public sectors.