Capacity Expansion and Replacement in Growing Markets with Uncertain Technological Breakthroughs

• Published in: Management science Vol. 44; no. 1; pp. 12 - 30
• Main Authors: Rajagopalan, Sampath, Singh, Medini R, Morton, Thomas E
• Format: Journal Article
• Language: English
• Published: Linthicum, MD INFORMS 01.01.1998; Institute for Operations Research and the Management Sciences
• Series: Management Science
• Subjects: Acquisition costs; Algorithms; Applied sciences; Capacity costs; Capacity Expansion; Carrying costs; Computer industry; Demand; Dynamic programming; Economies of scale; Emerging technology; Environmental technology; Equipment Replacement; Exact sciences and technology; Expansion; Firm modelling; Innovation; Management of Technology; Management science; Market; New technology; Operating costs; Operational research and scientific management; Operational research. Management science; Optimal solutions; Personal computers; Prices; Production capacity; Reliability theory. Replacement problems; Salvage value; Stochastic Dynamic Programming; Stochastic models; Strategic planning; Studies; Technological change; Technological innovation; Technology; Technology adoption; Technology Adoption models; Tomography; Cost minimization; Uncertainty; Sensitivity analysis; Capacity; Technological forecasting; Technology; Replacement; Management; Dynamic programming; Acquisition; Stochastic programming
• ISSN: 0025-1909; 1526-5501
• DOI: 10.1287/mnsc.44.1.12

The accelerated pace of technological change has led to rapid obsolescence of productive capacity in electronics and other industries. Managers must consider the impact of future technologies while making acquisition and replacement decisions in such environments. We consider a problem where a sequence of technological breakthroughs are anticipated but their magnitude and timing are uncertain. A firm, operating in such an environment, must decide how much capacity of the current technology to acquire to meet future demand growth. It must also determine whether to upgrade any of the older vintages. We formulate this problem and present some structural results. Using these results, we then develop a highly efficient regeneration point-based dynamic programming algorithm. The effectiveness of the proposed algorithm is illustrated through a computational study. The sensitivity of the first period decision to various parameters is also explored.