Forecasting sales and product evolution: The case of the hybrid/electric car

Abstract We present a model that forecasts sales and product evolution, based on data on market and industry, which can be collected before the product is introduced. Product evolution can be incremental but can also take place by releasing new generations. In our model adoption of a new product is motivated by attribute improvements (enabled by technology evolution), and firms' attribute improvements strategies are motivated by market growth and directed by market preferences. The interdependency between attributes' improvements and cumulative adoption level makes the problem inherently dynamic. The dependency of attribute levels on adoption levels is assessed using industry and technology analysis. Market preferences and purchase intention response to attribute levels changes are assessed based on a conjoint study. The option of collecting and interpreting data about both demand and supply aspects, before the new product is introduced, enables us to estimate sales and technology progress endogenously rather than to require them as inputs. We demonstrate the method on the hybrid car market.

[1]  Cheryl T. Druehl,et al.  Changes in Product Attributes and Costs as Drivers of New Product Diffusion and Substitution , 2005 .

[2]  Inseong Song,et al.  A Micromodel of New Product Adoption with Heterogeneous and Forward-Looking Consumers: Application to the Digital Camera Category , 2003 .

[3]  Christoph H. Loch,et al.  A Punctuated-Equilibrium Model of Technology Diffusion , 1999 .

[4]  Bruce R. Robinson,et al.  Dynamic Price Models for New-Product Planning , 1975 .

[5]  Sridhar Balasubramanian,et al.  Customer relationship management in competitive environments: The positive implications of a short-term focus , 2007 .

[6]  Henning Madsen,et al.  Strategic considerations in technology management: some theoretical and methodological perspectives , 1992 .

[7]  Barton A. Weitz,et al.  Product Development - Managing a Dispersed Process by , 2011 .

[8]  Lena Neij,et al.  Cost dynamics of wind power , 1999 .

[9]  Tugrul U. Daim,et al.  Technology Assessment: Forecasting the Future Adoption of Emerging Technologies , 2011 .

[10]  Tugrul Daim,et al.  Difficulties in R&D target-setting addressed through technology forecasting using data envelopment analysis , 2010, PICMET 2010 TECHNOLOGY MANAGEMENT FOR GLOBAL ECONOMIC GROWTH.

[11]  C. Shapiro,et al.  Network Externalities, Competition, and Compatibility , 1985 .

[12]  Gino Verleye,et al.  Innovation diffusion: The need for more accurate consumer insight. Illustration of the PSAP scale as a segmentation instrument , 2004 .

[13]  Tugrul U. Daim,et al.  Value Driven Technology Road Mapping (VTRM) process integrating decision making and marketing tools: Case of Internet security technologies , 2009 .

[14]  Nathasit Gerdsri,et al.  Roadmapping future powertrain technologies: a case study of Ford Otosan , 2010 .

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

[16]  S. Kalish.  A New Product Adoption Model with Price, Advertising, and Uncertainty , 1985 .

[17]  D. Y. Sha,et al.  Linking innovative product development with customer knowledge: a data-mining approach , 2006 .

[18]  J. Goldenberg,et al.  The chilling effects of network externalities , 2010 .

[19]  Jacob Goldenberg,et al.  From Density to Destiny: Using Spatial Dimension of Sales Data for Early Prediction of New Product Success , 2004 .

[20]  Patrik Söderholm,et al.  Learning Curve Analysis for Energy Technologies: Theoretical and Econometric Issues , 2003 .

[21]  Frank M. Bass,et al.  DIRECTV: Forecasting Diffusion of a New Technology Prior to Product Launch , 2001 .

[22]  George L. Nemhauser,et al.  Handbooks in operations research and management science , 1989 .

[23]  Bruce G. S. Hardie,et al.  Marketing-Mix Variables and the Diffusion of Successive Generations of a Technological Innovation , 2001 .

[24]  Steven Klepper,et al.  The capabilities of new firms and the evolution of the US automobile industry , 2002 .

[25]  M. Tenenhaus,et al.  R&D productivity: an exploratory international study , 2007 .

[26]  Tugrul U. Daim,et al.  Forecasting emerging technologies: Use of bibliometrics and patent analysis , 2006 .

[27]  Barry L. Bayus,et al.  CREATING AND SURVIVING IN NEW INDUSTRIES , 2004 .

[28]  Gustav Feichtinger,et al.  Optimal pricing in a diffusion model with concave price-dependent market potential , 1982, Oper. Res. Lett..

[29]  Alladi Venkatesh,et al.  Beyond Adoption: Development and Application of a Use-Diffusion Model , 2004 .

[30]  Roger J. Gagnon,et al.  Assessing advanced engineering technologies , 1997 .

[31]  Pinar Karaca-Mandic,et al.  Network Effects in Technology Adoption: The Case of DVD Players - eScholarship , 2003 .

[32]  Vijay Mahajan,et al.  A multi-attribute diffusion model for forecasting the adoption of investment alternatives for consumers , 1985 .

[33]  S. R. Dalal,et al.  A Choice-Based Approach to the Diffusion of a Service: Forecasting Fax Penetration by Market Segments , 1992 .

[34]  A K Shukla.  Fuelling future cars , 2005 .

[35]  Nobuo Nakajima.  Future Mobile Communications Systems in Japan , 2001, Wirel. Pers. Commun..

[36]  R. Grant.  Contemporary Strategy Analysis: Concepts, Techniques, Applications , 1991 .

[37]  H. Grupp,et al.  Technological Progress and Market Growth: An Empirical Study Based on the Quality-Ladder Approach , 2005 .

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

[39]  Vardit Landsman,et al.  The diffusion of a new service: Combining service consideration and brand choice , 2010 .

[40]  Lena Neij,et al.  Use of experience curves to analyse the prospects for diffusion and adoption of renewable energy technology , 1997 .

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

[42]  Vijay Mahajan,et al.  Chapter 8 New-product diffusion models , 1993, Marketing.

[43]  E. Anderson,et al.  Does Customer Satisfaction Matter to Investors? Findings from the Bond Market , 2008 .

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

[45]  John R. Hauser,et al.  Prelaunch forecasting of new automobiles , 1990 .

[46]  P. Cooper.  A Study of Innovators' Experience of New Product Innovation in Organisations , 2005 .

[47]  Adrien Presley,et al.  R&D project selection using the analytic network process , 2002, IEEE Trans. Engineering Management.

[48]  C. Shapiro,et al.  Technology Adoption in the Presence of Network Externalities , 1986, Journal of Political Economy.

[49]  Tugrul U. Daim,et al.  A taxonomic review of methods and tools applied in technology assessment , 2008 .

[50]  Pradeep K. Chintagunta,et al.  Effects of Brand Preference, Product Attributes, and Marketing Mix Variables in Technology Product Markets , 2006 .

[51]  John R. Hauser,et al.  Prelaunch forecasting of new automobiles : models and implementation , 1986 .

[52]  Steve Hoeffler,et al.  Measuring Preferences for Really New Products , 2003 .

[53]  Robert A. Peterson,et al.  Innovation Diffusion in a Dynamic Potential Adopter Population , 1978 .

[54]  C. Narasimhan.  Incorporating Consumer Price Expectations in Diffusion Models , 1989 .

[55]  L. Cooper.  A research agenda to reduce risk in new product development through knowledge management: a practitioner perspective , 2003 .

[56]  Tugrul U. Daim,et al.  A FRAMEWORK FOR MANAGING THE FORECASTING PROCESS , 2008 .

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

[58]  Manfred Stadler,et al.  R&D dynamics in the product life cycle , 1991 .

[59]  A. Kleinknecht,et al.  Innovative output, and a firm's propensity to patent.: an exploration of CIS micro data , 1999 .

[60]  Morton I. Kamien,et al.  Self-Financing of an R & D Project , 1976 .

[61]  J. M. Jones,et al.  Incorporating distribution into new product diffusion models , 1991 .