Abstract:
|
Biopharma supply chain management faces various challenges, including (1) highly interactive complex systems; (2) high uncertainty in supply, production, testing and demand; (3) rapid change in technology and frequent launches of new products. In this talk, we first present a flexible Bayesian nonparametric forecasting model which can capture the important properties in the real-world input data streams. Then, we propose a rigorous and efficient simulation-based prediction and optimization framework. It can quantify the prediction uncertainty of system future response and quickly guide coherent operational decisions for complex biopharma supply chains hedging against various sources of risk in advance. The empirical study demonstrates that our approach has promising performance.
|