A Simulation-Based Prediction and Optimization Framework for Bio-pharmaceutical Supply Chain Dynamic Risk Management

• Activity Number: 156 - Statistical Aspects in Stochastic and Deterministic Simulation
• Type: Topic Contributed
• Date/Time: Monday, July 30, 2018 : 10:30 AM to 12:20 PM
• Sponsor: Section on Physical and Engineering Sciences
• Author(s): Wei Xie* and Pu Zhang and Ilya O. Ryzhov
• Companies: Rensselaer Polytechnic Institute and Rensselaer Polytechnic Institute and University of Maryland
• Keywords: Bayesian nonparametric probabilistic forecasting; biopharma supply chain; risk management; stochastic simulation; optimization

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.