A new metric is presented for evaluating supply chain design and planning projects in which there are significant elements of uncertainty and thus risk. The risk premium construct provides the basis for a rational balance between expected value of investment performance and variance. An effective polytope integration method for evaluation of expected values and variances of revenue is adopted which can account for the effects of demand uncertainties on revenue while recognizing the uncertainty in inventory over time. The combination of these elements with conventional deterministic mathematical programming models offers the promise of providing an effective approach to accommodating uncertainties and a rational basis for balancing risk. A small scale example is used to contrast the proposed approach with conventional stochastic programming-based methods. Another example shows the nature of the return and risk for a multiperiod production plan with stochastic effects on inventory. The computational complexities which are introduced by the risk premium construct are reviewed, and some directions for future research discussed.