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Investigating a long tail in retail vehicle sales

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  • Brabazon, Philip G
  • MacCarthy, Bart

Abstract

The concept of the long tail in demand distributions has generated significant research interest because of its potential importance for producers, distributors and retailers. Studies to date have focused on information goods sold through internet channels. Here we study long tail effects in vehicle sales sold through conventional car dealerships. The willingness of customers to compromise on, and/or wait for their requested vehicle specifications are identified as critical factors in vehicle purchases. An extensive empirically based simulation study is conducted to investigate these demand-side factors. The results show how the characteristics of the customer population affect the observed pattern in vehicle sales. The interaction effects of supply-side factors are also highlighted, in particular the effect of the replenishment policy used in the fulfillment system. The study also analyzes the distortion of the underlying demand signal in the sales distribution and the lead time and degree of compromise experienced by different customer populations. Significant managerial implications are highlighted, including the dangers of using the sales distribution as a definitive indicator of demand, the need for the order fulfillment process to align with the characteristics of the market, and the negative effects of focusing replenishment on a small subset of the most popular variants.

Suggested Citation

  • Brabazon, Philip G & MacCarthy, Bart, 2012. "Investigating a long tail in retail vehicle sales," Omega, Elsevier, vol. 40(3), pages 302-313.
    • Handle: RePEc:eee:jomega:v:40:y:2012:i:3:p:302-313
    DOI: 10.1016/j.omega.2011.07.005
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    1. Bentley, R. Alexander & Madsen, Mark E. & Ormerod, Paul, 2009. "Physical space and long-tail markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 388(5), pages 691-696.
    2. Frank M. Bass, 1969. "A New Product Growth for Model Consumer Durables," Management Science, INFORMS, vol. 15(5), pages 215-227, January.
    3. Paul E. Green & Abba M. Krieger, 1985. "Models and Heuristics for Product Line Selection," Marketing Science, INFORMS, vol. 4(1), pages 1-19.
    4. Vijay Mahajan & Eitan Muller & Frank M. Bass, 1995. "Diffusion of New Products: Empirical Generalizations and Managerial Uses," Marketing Science, INFORMS, vol. 14(3_supplem), pages 79-88.
    5. Erik Brynjolfsson & Yu (Jeffrey) Hu & Michael D. Smith, 2003. "Consumer Surplus in the Digital Economy: Estimating the Value of Increased Product Variety at Online Booksellers," Management Science, INFORMS, vol. 49(11), pages 1580-1596, November.
    6. Daniel Corsten & Thomas Gruen, 2005. "On Shelf Availability: An Examination of the Extent, the Causes, and the Efforts to Address Retail Out-of-Stocks," Springer Books, in: Georgios J. Doukidis & Adam P. Vrechopoulos (ed.), Consumer Driven Electronic Transformation, pages 131-149, Springer.
    7. Peres, Renana & Muller, Eitan & Mahajan, Vijay, 2010. "Innovation diffusion and new product growth models: A critical review and research directions," International Journal of Research in Marketing, Elsevier, vol. 27(2), pages 91-106.
    8. A. Gürhan Kök & Marshall L. Fisher, 2007. "Demand Estimation and Assortment Optimization Under Substitution: Methodology and Application," Operations Research, INFORMS, vol. 55(6), pages 1001-1021, December.
    9. Fenner, Trevor & Levene, Mark & Loizou, George, 2010. "Predicting the long tail of book sales: Unearthing the power-law exponent," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(12), pages 2416-2421.
    10. Meyr, H., 2004. "Supply chain planning in the German automotive industry," Publications of Darmstadt Technical University, Institute for Business Studies (BWL) 36062, Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL).
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    Cited by:

    1. MacCarthy, Bart L. & Ovutmen, Tamer, 2015. "Using a central Vehicle Holding Compound (VHC) in an open pipeline automotive order fulfilment system: A simulation study," International Journal of Production Economics, Elsevier, vol. 170(PB), pages 590-601.
    2. Mou, Shandong & Robb, David J. & DeHoratius, Nicole, 2018. "Retail store operations: Literature review and research directions," European Journal of Operational Research, Elsevier, vol. 265(2), pages 399-422.

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