The Dynamic Effects of Planning Horizons on the Selection of Optimal Product Strategies

The selection of appropriate product strategies by firms must account for the expected long-run returns from the market a firm contemplates entering, relative to the expected long-run returns from the market it is currently serving. These expected returns must also reflect the capabilities a firm has to implement this strategy and the resulting competitive situations it will face. The mathematical techniques of Markov processes and dynamic programming are used in this paper to develop an analytic framework which will select optimal product strategies for a firm that are consistent with its capabilities, that will meet its competitive situations, and that will maximize expected returns in the long run. This model is applied to a selected set of Michigan machine tool firms. The results indicate the general importance of total firm capabilities and the greater importance of management capability over the supporting capabilities in the selection of optimal product strategies.

• 저널Management Science · 10(3) · 524–544
• 토픽Reinforcement Learning in Robotics · Artificial Intelligence
• DOI10.1287/mnsc.10.3.524
• 저자Marvin G. DeVries