The dark side of innovation: Exploring obsolescence and supply chain evolution for sustainment-dominated systems

Abstract The problem of technological obsolescence in vendor supplied parts in the new product development process has increased in importance in recent years. This is compounded by both the rapid pace of technological advance and increasingly disintegrated supply chains. This issue has become particularly problematic for products with both a high degree of technology and long life cycles. This exploratory study relates empirical obsolescence findings to theoretically predicted models of innovation diffusion for different product-market conditions. Implications are analyzed for supply chain evolution and obsolescence management.

[1]  Michael Pecht,et al.  Electronic part life cycle concepts and obsolescence forecasting , 2000 .

[2]  Michael Pecht,et al.  Electronic Components Obsolescence , 1997 .

[3]  V. Mahajan,et al.  Timing, Diffusion, and Substitution of Successive Generations of Technological Innovations: The IBM Mainframe Case , 1996 .

[4]  G.E. Moore,et al.  Cramming More Components Onto Integrated Circuits , 1998, Proceedings of the IEEE.

[5]  J. Hair Multivariate data analysis , 1972 .

[6]  C. Bulte New Product Diffusion Acceleration: Measurement and Analysis , 2000 .

[7]  Jennifer Blackhurst,et al.  The Severity of Supply Chain Disruptions: Design Characteristics and Mitigation Capabilities , 2007, Decis. Sci..

[8]  Fernando Porté-Agel,et al.  Moore's Law and Numerical Modeling , 2002 .

[9]  R. P. Lavoie,et al.  Avionics Acquisition Beyond 2000 , 1987, IEEE Aerospace and Electronic Systems Magazine.

[10]  Laszlo B. Kish,et al.  Moore's law and the energy requirement of computing versus performance , 2004 .

[11]  F. Bass,et al.  A diffusion theory model of adoption and substitution for successive generations of high-technology products , 1987 .

[12]  Dieter Ernst,et al.  From Partial to Systemic Globalization: International Production Networks in the Electronics Industry , 1997 .

[13]  D. Hosmer,et al.  Applied Logistic Regression , 1991 .

[14]  Pameet Singh,et al.  Obsolescence Driven Design Refresh Planning for Sustainment-Dominated Systems , 2006 .

[15]  F. Bass A new product growth model for consumer durables , 1976 .

[16]  Stephan M. Wagner,et al.  AN EMPIRICAL EXAMINATION OF SUPPLY CHAIN PERFORMANCE ALONG SEVERAL DIMENSIONS OF RISK , 2008 .

[17]  Donald R. Lehmann,et al.  A Meta-Analysis of Applications of Diffusion Models , 1990 .

[18]  Oktay Günlük,et al.  Robust capacity planning in semiconductor manufacturing , 2005 .

[19]  Jacob Cohen,et al.  Applied multiple regression/correlation analysis for the behavioral sciences , 1979 .

[20]  Dipak C. Jain,et al.  Why the Bass Model Fits without Decision Variables , 1994 .

[21]  F. Bass The Relationship between Diffusion Rates, Experience Curves, and Demand Elasticities for Consumer Durable Technological Innovations , 1980 .

[22]  Vijay Mahajan,et al.  New Product Diffusion Models in Marketing: A Review and Directions for Research: , 1990 .

[23]  John A. Norton,et al.  Optimal Entry Timing for a Product Line Extension , 1989 .

[24]  Craig S. Galbraith,et al.  Can experts really assess future technology success? A neural network and Bayesian analysis of early stage technology proposals , 2007 .

[25]  Hans Georg Gemünden,et al.  NPD Planning Activities and Innovation Performance: The Mediating Role of Process Management and the Moderating Effect of Product Innovativeness , 2007 .

[26]  In Ho Lee,et al.  A Theory of Economic Obsolescence , 2003 .

[27]  Adam Brandenburger,et al.  Value-based Business Strategy , 2005 .

[28]  Michael E Brooks An Investigation of Time Series Growth Curves as a Predictor of Diminishing Manufacturing Sources of Electronic Components. , 1981 .

[29]  Jayashankar M. Swaminathan,et al.  Modeling Supply Chain Dynamics: A Multiagent Approach , 1998 .

[30]  Vijay Mahajan,et al.  Integrating time and space in technological substitution models , 1979 .

[31]  J M Masters A Note on the Effect of Sudden Obsolescence on the Optimal Lot Size , 1991 .

[32]  Lori Rosenkopf,et al.  INSTITUTIONAL AND COMPETITIVE BANDWAGONS: USING MATHEMATICAL MODELING AS A TOOL TO EXPLORE INNOVATION DIFFUSION , 1993 .

[33]  J. Neter,et al.  Applied Linear Regression Models , 1983 .

[34]  N. Flaherty New parts for old , 2005 .