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An Aggregate Sales Model for Consumer Durables Incorporating a Time-Varying Mean Replacement Age

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  • Steffens, Paul R

Abstract

Forecasting category or industry sales is a vital component of a company's planning and control activities. Sales for most mature durable product categories are dominated by replacement purchases. Previous sales models which explicitly incorporate a component of sales due to replacement assume there is an age distribution for replacements of existing units which remains constant over time. However, there is evidence that changes in factors such as product reliability/durability, price, repair costs, scrapping values, styling and economic conditions will result in changes in the mean replacement age of units. This paper develops a model for such time-varying replacement behaviour and empirically tests it in the Australian automotive industry. Both longitudinal census data and the empirical analysis of the replacement sales model confirm that there has been a substantial increase in the average aggregate replacement age for motor vehicles over the past 20 years. Further, much of this variation could be explained by real price increases and a linear temporal trend. Consequently, the time-varying model significantly outperformed previous models both in terms of fitting and forecasting the sales data. Copyright © 2001 by John Wiley & Sons, Ltd.

Suggested Citation

  • Steffens, Paul R, 2001. "An Aggregate Sales Model for Consumer Durables Incorporating a Time-Varying Mean Replacement Age," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 20(1), pages 63-77, January.
    • Handle: RePEc:jof:jforec:v:20:y:2001:i:1:p:63-77
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    Cited by:

    1. Tsiliyannis, Christos Aristeides, 2018. "Markov chain modeling and forecasting of product returns in remanufacturing based on stock mean-age," European Journal of Operational Research, Elsevier, vol. 271(2), pages 474-489.
    2. Dray, Lynnette, 2013. "An analysis of the impact of aircraft lifecycles on aviation emissions mitigation policies," Journal of Air Transport Management, Elsevier, vol. 28(C), pages 62-69.
    3. Kaldasch, Joachim, 2011. "Evolutionary model of an anonymous consumer durable market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(14), pages 2692-2715.
    4. Tsiliyannis, Christos Aristeides, 2015. "Sustainability by cyclic manufacturing: Assessment of resource preservation under uncertain growth and returns," Resources, Conservation & Recycling, Elsevier, vol. 103(C), pages 155-170.
    5. Kaldasch, Joachim, 2015. "The Product Life Cycle of Durable Goods," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 10(2), pages 1-17.
    6. Riikonen, Antti & Smura, Timo & Töyli, Juuso, 2016. "The effects of price, popularity, and technological sophistication on mobile handset replacement and unit lifetime," Technological Forecasting and Social Change, Elsevier, vol. 103(C), pages 313-323.
    7. Kivi, Antero & Smura, Timo & Töyli, Juuso, 2012. "Technology product evolution and the diffusion of new product features," Technological Forecasting and Social Change, Elsevier, vol. 79(1), pages 107-126.
    8. Deleersnyder, B. & Dekimpe, M.G. & Sarvary, M. & Parker, P.M., 2003. "Weathering Tight Economic Times: The Sales Evolution Of Consumer Durables Over The Business Cycle," ERIM Report Series Research in Management ERS-2003-046-MKT, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.
    9. Chul-Yong Lee & Sung-Yoon Huh, 2017. "Technology Forecasting Using a Diffusion Model Incorporating Replacement Purchases," Sustainability, MDPI, vol. 9(6), pages 1-14, June.
    10. Meade, Nigel & Islam, Towhidul, 2006. "Modelling and forecasting the diffusion of innovation - A 25-year review," International Journal of Forecasting, Elsevier, vol. 22(3), pages 519-545.

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