“ Market and Process Uncertainty in Operations ”

By adopting a real options framework, we develop and analyze a production control model that jointly incorporates process and market risks. In our model, process risk is typified by random yield variability while market risk is defined through demand uncertainty. The stochastic processes used to depict uncertainty in these state variables reflect a wide variety of distributional forms and are not confined to the traditional processes typically used in the real options literature. In our approach, the production inputs represent renewable, partially renewable or non-renewable resources. Furthermore, the production outputs are treated as non-traded assets, so that the model has a much broader range of applicability beyond that of standard commodities for which futures contracts trade. Given this setting, techniques of contingent claims analysis and stochastic control theory are employed to obtain value maximizing production policies in a constrained capacity environment. In light of the stochastic nature of the state variables, the rate of production is modeled as an adapted positive realvalued process and analogously evaluated as a sequence of complex real options. As the optimal adjustments to the rate of production also depend on the outputs’ yield, we establish and explore “flexibility triggers” justifying variations to the rate of production over time. This is achieved by providing closed form analytic results in the presence of generalized diffusion processes including mean reverting processes for the state variables to follow. We also use a numerical example to highlight the model’s sensitivity and contingent features.