One of the greatest challenges facing industry in the new century will be the process of selecting which new products to develop. Capital resources and manpower are limited. Stakeholders are demanding ever-increasing rates of return. These problems are especially difficult in highly regulated industries such as the chemicals and life sciences businesses, where development times are long and costly. In formative industries such as biotechnology, regulatory requirements continue to tighten, as public perception is often more influential than science in the approval process. Engineers are comfortable building process models. However, they infrequently think about the development of new products or the selection of new products as processes. This study is an attempt to get the engineers involved in the new product decision making process. Using the pharmaceutical industry as an example, probabilistic network models are used to capture all the activities and resources required in the 'process' of developing a new drug. The data representing each new product candidate are then combined into a simulation model of the new product development pipeline. This simulation model can be used by management to obtain insights into a new product portfolio, which will provide high rates of return at an acceptable level of exposure to risk for the corporation.