Scenario Analysis of Demands in a Technology Market Using Leading Indicators

This paper proposes an approach to analyze demand scenarios in technology-driven markets where product demands are volatile, but follow a few identifiable life-cycle patterns. After analyzing a large amount of semiconductor data, we found that not only can products be clustered by life-cycle patterns, but in each cluster there exists leading indicator products that provide advanced indication of changes in demand trends. Motivated by this finding we propose a scenario analysis structure in the context of stochastic programming. Using the Bass growth model and a Baysian update structure, the proposed method streamlines scenario analysis by focusing on parametric changes of the demand growth model overtime. The Baysian structure allows expert judgement to be incorporated in scenario generation while the Bass growth model allows an efficient representation of time varying demands. Further, by adjusting a likelihood threshold, the method could generate scenario trees of different sizes and accuracy. The structure provides a practical scenario-analysis method for manufacturing demand in a technology market. We demonstrate the applicability of this method using real semiconductor data.