Scenario analysis of demand in a technology market using leading indicators

This paper proposes an approach to analyzing demand scenarios in technology-driven markets where product demands are volatile, but follow a few identifiable lifecycle patterns. After examining a large amount of semiconductor data, we found that not only can products be clustered by lifecycle patterns, but in each cluster there exists a leading indicator product that provides advanced indication of changes in demand trends. Motivated by this finding, we propose a scenario analysis structure in the context of stochastic programming. Specifically, the demand model that results from this approach provides a mechanism for building a scenario tree for semiconductor demand. Using the Bass growth model and a Bayesian update structure, the approach streamlines scenario analysis by focusing on parametric changes of the demand growth model over time. The Bayesian structure allows expert judgment to be incorporated into scenario generation while the Bass growth model allows an efficient representation of time varying demands. Further, by adjusting a likelihood threshold, the method generates scenario trees of different sizes and accuracy. This 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.

[1]  Vijay Mahajan,et al.  Maximum Likelihood Estimation for an Innovation Diffusion Model of New Product Acceptance , 1982 .

[2]  Lee Shepstone.  Methods for Statistical Data Analysis of Multivariate Observations, Second Edition , 1998 .

[3]  Benjamin M. Adams,et al.  Combined Control Charts for Forecast-Based Monitoring Schemes , 1996 .

[4]  Ramanathan Gnanadesikan,et al.  Methods for statistical data analysis of multivariate observations , 1977, A Wiley publication in applied statistics.

[5]  H. M. Wagner,et al.  Principles of Operations Research: With Applications to Managerial Decisions. , 1971 .

[6]  Lloyd S. Nelson,et al.  Control Charts for Individual Measurements , 1982 .

[7]  Jitka Dupačová,et al.  Applications of stochastic programming under incomplete information , 1994 .

[8]  Graham K. Rand,et al.  Decision Systems for Inventory Management and Production Planning , 1979 .

[9]  L. Zurich,et al.  Operations Research in Production Planning, Scheduling, and Inventory Control , 1974 .

[10]  Dennis V. Lindley,et al.  An Introduction to Bayesian Inference and Decision , 1974 .

[11]  D. Berry.  Statistical Decision Theory, Foundations, Concepts, and Methods , 1982 .

[12]  Charlotte H. Mason,et al.  Technical Note---Nonlinear Least Squares Estimation of New Product Diffusion Models , 1986 .

[13]  Vijay Mahajan,et al.  A simple algebraic estimation procedure for innovation diffusion models of new product acceptance , 1986 .

[14]  Hirofumi Matsuo,et al.  Forecasting and Inventory Management of Short Life-Cycle Products , 1996, Oper. Res..

[15]  T. Fearn,et al.  Bayesian statistics : principles, models, and applications , 1990 .

[16]  Linus Schrage,et al.  OR Practice - A Scenario Approach to Capacity Planning , 1989, Oper. Res..

[17]  Ronald J. M. M. Does,et al.  Shewhart-Type Control Charts for Individual Observations , 1993 .

[18]  Gwilym M. Jenkins,et al.  Time series analysis, forecasting and control , 1972 .

[19]  Robert L. Winkler,et al.  An Introduction to Bayesian Inference and Decision , 1972 .

[20]  A. M. Ireland,et al.  Scenario formulation in an algebraic modelling language , 1995, Ann. Oper. Res..

[21]  S. D. Wu,et al.  Relating Demand Behavior and Production Policies in the Manufacturing Supply Chain , 1998 .

[22]  Michael A. West,et al.  Bayesian Forecasting and Dynamic Models (2nd edn) , 1997, J. Oper. Res. Soc..

[23]  F. Guess.  Bayesian Statistics: Principles, Models, and Applications , 1990 .

[24]  Robert J. Thomas,et al.  Estimating Market Growth for New Products: An Analogical Diffusion Model Approach , 1985 .

[25]  Laureano F. Escudero,et al.  Production planning via scenario modelling , 1993, Ann. Oper. Res..

[26]  Ambar G. Rao,et al.  Bayesian Estimation and Control of Detailing Effort in a Repeat Purchase Diffusion Environment , 1981 .

[27]  John Snyder,et al.  Forecasting and Market Analysis Techniques: A Practical Approach , 1994 .

[28]  H. M. Wagner,et al.  Principles of Operations Research: With Applications to Managerial Decisions. , 1971 .

[29]  R. Wets,et al.  Stochastic programming , 1989 .

[30]  Nigel Meade,et al.  Technological Forecasting-Model Selection, Model Stability, and Combining Models , 1998 .

[31]  V. Mahajan,et al.  Innovation Diffusion Models of New Product Acceptance. , 1987 .