SIM-OPT: A Computational Architecture to Address Valuable Business Aspects of Research Development Pipeline Management

摘要

The R&D Pipeline management problem has far-reaching economic implications for new-product-development driven industries, such as pharmaceutical, biotechnology, and agrochemical industries. Effective decision-making is required with respect to portfolio selection and project task scheduling in the face of significant uncertainty and an ever-constrained resource pool. Recently, Subramanian et al. (2000) described the here-and-now stochastic optimization problem inherent to the management of the R&D Pipeline by viewing it as the control problem of a performance-oriented, resource-constrained, stochastic, discrete-event, dynamic system. They presented a computing architecture, Sim-Opt, which combines mathematical programming and discrete event system simulation to assess the uncertainty present in the pipeline. They introduced the concept of timelines that studies multiple unique realizations of the controlled evolution of the discrete-event pipeline system. This work demonstrates how information may be integrated across the Sim-Opt timelines to obtain insights into the dynamics of the pipeline under uncertainty. It also illustrates how such insights may be applied to plan more effectively from an operational perspective. Lastly, it discusses briefly about other business questions that can be investigated using the Sim-Opt architecture.