A simulation-optimization approach to integrate process design and planning decisions under technical and market uncertainties: A case from the chemical-pharmaceutical industry

Technical and market uncertainties during product launch planning can have a significant impact in the companies decision-making process, particularly in today’s business context of the pharmaceutical industry. This paper addresses this planning problem, considering both types of uncertainty and integrating process design and capacity decisions under limited resources. A Mixed Integer Linear Programing (MILP) model, with a two-step Monte Carlo Simulation (MCS) framework, is developed. The MCS component captures the simultaneous impact of uncertainty in product demand, in the outcomes of clinical trials, and in processing times, through respectively normal distributions, Bernoulli distributions, and uniform distributions. Results show that although the processing time variability is not relevant in the NPV, it inevitably affects the decision-making process by changing process design, scale up, and capacity extensions decisions. The proposed approach provides a robust and valuable information to support the medium and the long-term decisions associated with the product launch planning problem.