Solving the Capacity Optimization Problem under Demand Uncertainty

This paper provides theoretical and practical insight into the solution of the investment project optimization problem under uncertainty. A case study recommends the use of statistical simulation, which is shown to be a powerful, practical, flexible and comprehensive tool for managerial decision-making purposes, even as it takes into account exogeneous uncertainty, as well as endogeneous processes structurally vested in the project. Results show that excess capacity may have a value exceeding its cost, which can be assessed either through comparison of available variants, or by carrying out a full optimization. From the theoretical point of view, the relationship of the problem to real-option analysis is investigated in more detail. Even though it obviously does contain real options, in principle, as various project alternatives differ in terms of their flexibility to alter operating scale, the proposed solution clearly surmounts some of the limitations of prevalent realoption models.