The purpose of this paper is to challenge the common claim of “infinite variety” being demanded in the marketplace by measuring not just how much variety theoretically could be produced, but how much is actually demanded by the customer. To this effect, the authors propose and validate market‐based variety measures with empirical data and in a second step, extend their analysis by applying these measures and empirically testing the impact of variety mitigation strategies, such as postponement and options bundling.
The authors analyse production and sales data of 226,106 passenger cars, comprising of three models of one vehicle manufacturer sold across four global market regions. The theoretical variety is compared with actual variety for each model‐market combination, and these data are linked to actual production and sales records.
The authors propose and validate product variety measures based on actual customer orders, and empirically demonstrate how these measures can be used to assess the impact of late configuration and option bundling strategies, and find that these are generally valid, but that their applicability is contingent upon the respective variety distribution profile.
Analyses are developed within the context of a single firm and industry, although an attempt was made to counter this weakness by considering models from the volume, niche and premium market segments.
The paper highlights how actual variety differs from theoretical variety in practice, which in turn co‐determines the effectiveness of mitigation strategies applied by firms.
The paper's main contribution is to propose and empirically test a set of novel measures of product variety: the average repetition ratio and a specification Pareto curve, both of which complement and enhance one's understanding of product variety and its impact on manufacturing and distribution systems.
Stäblein, T., Holweg, M. and Miemczyk, J. (2011), "Theoretical versus actual product variety: how much customisation do customers really demand?", International Journal of Operations & Production Management, Vol. 31 No. 3, pp. 350-370. https://doi.org/10.1108/01443571111111955Download as .RIS
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