Advertisement

Stochastic Option Bundling and Bundle Pricing

  • Ralph Fuerderer
  • Arnd Huchzermeier
  • Linus Schrage
Chapter

Abstract

In many production and services industries, bundling is the widespread practice of offering a number of products or services in a single package at an attractive price. In the automobile industry, a basic car model is offered along with options such as air conditioning, sun-roof, metallic exterior colors, and so forth, so-called free-choice or free-flow options. In general, the customer is able to purchase option packages which consist of a number of single options offered at a reduced bundle price. As far as most customer segments are concerned, equipment sales are the manufacturer’s main source of profit. Thus, it is vital to decide on the pricing and the initial selection of free-choice options to be offered by the manufacturer. Due to substantial product and process development lead times, this task has to be carried out at least several months before production actually starts. Currently, accurate forecasting of demand for particular car types or option combinations is extremely difficult. The car manufacturer can hedge his risk of not matching the individual preferences of the customers for the car bundles offered, by providing a wide selection of free-choice options. However, from a manufacturing perspective, this product strategy is rather questionable. Moreover, variant-dependent costs are primarily determined by the number and the design of option combinations a customer can purchase with his basic car. Economies of scope exist among complementary options, e.g., a front door can be equipped with a power mirror more easily if it also has a power window. However, these cost synergies can only be exploited if customers do select (by chance) certain option combinations.

Keywords

Choice Behavior Consumer Surplus Reservation Price Master Problem Profit Function 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Adams, W. J. and J. L. Yellen (1976). “Commodity Bundling and the Burden of Monopoly.” Quarterly Journal of Economics, Vol. 90, 475–498.CrossRefGoogle Scholar
  2. Aleksandrov, P. (1965). Combinatorial Topology. Graylock Press, Baltimore (MD).Google Scholar
  3. Bazaraa, M.S., H. D. Sherali and C. M. Shetty (1993). Nonlinear Programming. Wiley Interscience Series in Discrete Mathematics and Optimization, New York.Google Scholar
  4. Braibant, V. and C. Fleury (1985). “An Approximation Concept Approach to Shape Optimal Design, Computer Mehtods.” Applied Nechanics and Engineering, Vol. 53, 119–148.Google Scholar
  5. Cornuejols, G., M. L. Fisher and G. L. Nemhauser (1977). “Location of Bank Accounts to Optimize Float: An Analytic Study of Exact and Approximate Algorithms.” Management Science, Vol. 23, 789–810.Google Scholar
  6. Dieudonné, J. (1969). Foundations of Modern Analysis. Academic Press, New York.Google Scholar
  7. Dobson, G. and S. Kalish (1993). “Heuristics for Pricing and Positioning a Product-line Using Conjoint and Cost Data.” Management Science, Vol. 39, 160175.Google Scholar
  8. Eppen, G.D., W. A. Hanson and R. K. Martin (1991). “Bundling — New Products, New Markets, Low Risk.” Sloan Management Review, Vol. 32, 7–14Google Scholar
  9. Fuerderer, R. (1996). Optimal Component and Option Bundling under Demand Ridk — Mass Customization Strategies in the European Automobile Industry. Gabler, Wiesbaden.Google Scholar
  10. Green, P. E. and A. M. Krieger (1992). “An Application of a Product Positioning Model to Pharmaceutical Products.” Marketing Science, Vol. 11, 117–132.CrossRefGoogle Scholar
  11. Hanson, W. A. and R. K. Martin (1990). “Optimal Bundle Pricing.” Management Science, Vol. 36, 155–174.CrossRefGoogle Scholar
  12. Hanson, W. A. and R. K. Martin (1994). “Optimizing Multinomial Logit Profit Functions.” Graduate School of Business, University of Chicago.Google Scholar
  13. Kohli, R. and R. Sukumar (1990). “Heuristics for Product-Line Design Using Conjoint Analysis.” Management Science, Vol. 36, 1464–1478.CrossRefGoogle Scholar
  14. Rosen, J. B. (1960). “The Gradient Projection Method for Nonlinear Programming, Part I, Linear Constraints.” SIAM Journal of Applied Mathmatics, Vol. 8, 181–217.Google Scholar
  15. Rosen, J. B. (1961). “The Gradient Projection Method for Nonlinear Programming, Part II, Nonlinear Constraints.” SIAM Journal of Applied Mathmatics, Vol. 8, 514–532.Google Scholar
  16. Schmalensee, R. (1984). “Gaussian Demand and Commodity Bundling.” Journal of Business, Vol. 57, 211–230.CrossRefGoogle Scholar
  17. Schubert, H. (1969). Topologie. Teubner Verlag, Stuttgart.Google Scholar
  18. Stigler, G. J. (1963). “United States vs. Loew’s Inc.: A Note on Block Booking.” The Supreme Court Review, Vol. 152, 152–157.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1999

Authors and Affiliations

  • Ralph Fuerderer
    • 1
  • Arnd Huchzermeier
    • 2
  • Linus Schrage
    • 3
  1. 1.International Technical Development CenterAdam Opel AGRuesselsheimGermany
  2. 2.Otto-Beisheim-Graduate School of ManagementWHUVallendarGermany
  3. 3.Graduate School of BusinessThe University of ChicagoChicagoUSA

Personalised recommendations