A scenario-based stochastic programming approach for technology and capacity planning
In response to market pressures resulting in increased competition, product proliferation and greater customization, firms in many industries have adopted modern technologies to provide operational flexibility on several dimensions. In this paper, we consider the role of product mix flexibility, defined as the ability to produce a variety of products, in an environment characterized by multiple products, uncertainty in product life cycles and dynamic demands. Using a scenario-based approach for capturing the evolution of demand, we develop a stochastic programming model for determining technology choices and capacity plans. Since the resulting model is likely to be large and may not be easy to solve with standard software packages, we develop a solution procedure based on augmented Lagrangian method and restricted simplicial decomposition. The scope of our approach for deriving context specific managerial insights is illustrated by the results of limited computations. Finally, we demonstrate the versatility of our approach by deriving a special case of the general model to address some tactical issues related to new product introduction. Product mix flexibility, defined as the ability to produce a variety of products, has often been cited as one of the strengths of modern flexible technologies in enhancing the competitiveness in manufacturing. While the associated trade-offs between such technologies and traditional dedicated plants are conceptually well understood, evaluation methodologies and planning techniques are rather limited and there is a need for models to assist managers in making technology and capacity decisions. In this paper, we develop a mathematical programming model for analyzing tradeoffs between dedicated and flexible technologies. A solution algorithm is developed for solving moderate sized problems of the model. Useful managerial insights are obtained based on computational results. Our model is fairly general and enables us to investigate a special case for tactical decisions on new product introduction.
flexible technology stochastic dynamic demands capacity and technology planning scenario-based approach large-scale stochastic programming