The impact of customer returns on supply chain decisions under various channel interactions | Annals of Operations Research Skip to main content
Log in

The impact of customer returns on supply chain decisions under various channel interactions

  • Published:
Annals of Operations Research Aims and scope Submit manuscript

Abstract

We examine a supply chain in which a manufacturer supplies a single product to a retailer who faces two forms of customer returns. We compare the impact of these two forms of customer returns on the decisions and profits of the manufacturer and the retailer under various types of channel interaction: Manufacturer Stackelberg (MS), Vertical Nash (VN), and Retailer Stackelberg (RS). We find that when the level of customer returns that are proportional to quantity sold is extremely high, the retailer prefers the MS rather than the RS channel interaction. We also examine the impact of the asymmetric customer returns information on the decisions of the manufacturer and the retailer and on profits under MS and VN channel interactions. We show that in the MS case, the retailer can decide whether or not to share customer returns information with its manufacturer without knowing the manufacturer’s estimates of customer returns and in the VN case, both the retailer and the manufacturer can decide whether or not to share/acquire the information based on observation of the other’s behavior. The issues of sharing this information are also discussed.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Subscribe and save

Springer+ Basic
¥17,985 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (Japan)

Instant access to the full article PDF.

Similar content being viewed by others

References

  • Anderson, E. T., Hansen, K., & Simister, D. (2009a). The option value of returns: theory and empirical evidence. Marketing Science, 28, 405–423.

    Article  Google Scholar 

  • Anderson, E. T., Hansen, K., Simister, D., & Wang, L. K. (2009b). How are demand and returns related? Theory and empirical evidence. Working paper, Kellogg School of Management, Northwestern University.

  • Biederman, D. (2005). Many happy returns. Journal of Commerce, December, 1–3.

    Google Scholar 

  • Blanchard, D. (2005). Moving forward in reverse. Logistics Today, 46, 1.

    Google Scholar 

  • Blanchard, D. (2007). Supply chains also work reverse. Industry Week, May 1.

  • Bonifield, C., Cole, C., & Schultz, R. (2010). Product returns on the internet: a case of mixed signals? Journal of Business Research, 63, 1058–1065.

    Article  Google Scholar 

  • Chen, J. (2011). The impact of sharing customer returns information in a supply chain with/without a buyback policy. European Journal of Operational Research, 213, 478–488.

    Article  Google Scholar 

  • Chen, J., & Bell, P. C. (2009). The impact of customer returns on pricing and order decisions. European Journal of Operational Research, 195, 280–295.

    Article  Google Scholar 

  • Chiang, W. K., Chhajed, D., & Hess, J. (2003). Direct marketing, indirect revenues: a strategic analysis of dual-channel supply chain design. Management Science, 49, 1–20.

    Article  Google Scholar 

  • Choi, C. S. (1991). Price competition in a channel structure with a common retailer. Marketing Science, 10, 271–296.

    Article  Google Scholar 

  • Choi, T. M., & Sethi, S. (2010). Innovative quick response programmes: a review. International Journal of Production Economics, 127, 1–12.

    Article  Google Scholar 

  • Esmaeili, M., Aryanezhad, M., & Zeephongsekul, P. (2009). A game theory approach in seller-buyer supply chain. European Journal of Operational Research, 195, 442–448.

    Article  Google Scholar 

  • Ferguson, M., Guide, M. Jr., & Souza, G.C. (2006). Supply chain coordination for false failure returns. Manufacturing & Service Operations Management, 8, 376–393.

    Article  Google Scholar 

  • Fiala, P. (2005). Information sharing in supply chain. Omega, 33, 419–423.

    Article  Google Scholar 

  • Hess, J., & Mayhew, G. (1997). Modeling merchandise returns in direct marketing. Journal of Direct Marketing, 11, 20–35.

    Article  Google Scholar 

  • Jeuland, A., & Shugan, S. (1983). Managing channel profits. Marketing Science, 2, 239–272.

    Article  Google Scholar 

  • Ketzenberg, M. E., Rosenzweig, E. D., Marucheck, A. E., & Metters, R. D. (2007). A framework for the value of information in inventory replenishment. European Journal of Operational Research, 182, 1230–1250.

    Article  Google Scholar 

  • Lau, H. S., & Lau, A. (1999). Manufacturer’s pricing strategy and return policy for a single period commodity. European Journal of Operational Research, 116, 291–304.

    Article  Google Scholar 

  • Lau, A., Lau, H. S., & Zhou, Y. W. (2007). A stochastic and asymmetric information framework for a dominant manufacturer supply chain. European Journal of Operational Research, 176, 295–316.

    Article  Google Scholar 

  • Lee, E., & Staelin, R. (1997). Vertical strategic interaction: implications for channel pricing strategy. Marketing Science, 16, 185–207.

    Article  Google Scholar 

  • McGuire, T., & Staelin, R. (1983). An industry equilibrium analysis of downstream vertical integration. Marketing Science, 2, 161–191.

    Article  Google Scholar 

  • Mitra, S. (2007). Revenue management for remanufactured products. Omega, 35, 553–562.

    Article  Google Scholar 

  • Mostard, J., & Teunter, R. (2006). The newsboy problem with resalable returns: a single period model and case study. European Journal of Operational Research, 169, 81–96.

    Article  Google Scholar 

  • Pasternack, B. A. (1985). Optimal pricing and returns policies for perishable commodities. Marketing Science, 4, 166–176.

    Article  Google Scholar 

  • Petersen, A., & Kumar, V. (2010). Can product returns make you money? MIT Sloan Management Review, 51, 85–89.

    Google Scholar 

  • Pralle, A., & Stalk, G. Jr. (2006). Returns: the ugly ducklings of retail. The Boston Consulting Group’s report.

  • Rogers, D. S., & Tibben-Lembke, R. S. (1999). Going backwards: reverse logistics trends and practices. Pittsburgh: Reverse Logistics Executive Council.

    Google Scholar 

  • Rogers, D. S., Lambert, D. M., Croxton, K., & Garcia-Dastugue, S. (2002). The returns management process. International Journal of Logistics Management, 13, 1–18.

    Article  Google Scholar 

  • Roy, C. (2009). Debunking the myths of customer returns and the use of liquidation channels. Retail/Catalog Online Integration. http://www.retailonlineintegration.com/article/debunking-myths-customer-returns-use-liquidation-channels-409310/1.

  • Sciarrotta, T. (2003). How PHILIPS reduced returns. Supply Chain Management Review, 7, 32–38.

    Google Scholar 

  • Strauss, M. (2006). Returns a $10-billion pain. Globe and Mail, November, B-7.

    Google Scholar 

  • Vlachos, D., & Dekker, R. (2003). Return handling options and order quantities for single period products. European Journal of Operational Research, 151, 38–52.

    Article  Google Scholar 

  • Wang, J., Lau, H. S., & Lau, A. (2009). When should a manufacturer share truthful manufacturing cost information with a dominant retailer? European Journal of Operational Research, 197, 266–286.

    Article  Google Scholar 

  • Yao, D. Q., & Liu, J. J. (2005). Competitive pricing of mixed retail and e-tail distribution channels. Omega, 33, 235–247.

    Article  Google Scholar 

  • Yuan, X. M., & Cheung, K. L. (1996). Modeling returns of merchandise in an inventory system. OR Spektrum, 20, 147–154.

    Google Scholar 

  • Yue, X., & Raghunathan, S. (2006). The impacts of the full returns policy on a supply chain with information asymmetry. European Journal of Operational Research, 180, 630–647.

    Article  Google Scholar 

  • Loss Prevention Research Council (2008). Customer returns in the retail industry.

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Jing Chen.

Appendix

Appendix

Proof of Proposition 4

With (19) and (20),

(A.1)
(A.2)

(A.1) and (A.2) give that when \(1 + 2c - 2\overline{\beta} > 0\), \(\frac{\partial \varPi_{M}^{\mathit{VN}}[w^{\mathit{VN}^{*}}(\overline{\alpha} ,\overline{\beta} )]}{\partial \overline{\alpha}} < 0\) and \(\frac{\partial \varPi_{R}^{\mathit{VN}}[w^{\mathit{VN}^{*}}(\overline{\alpha} ,\overline{\beta} )]}{\partial \overline{\alpha}} > 0\) while \(\frac{\partial \varPi_{M}^{\mathit{VN}}[w^{\mathit{VN}^{*}}(\overline{\alpha} ,\overline{\beta} )]}{\partial \overline{\alpha}} > 0\) and \(\frac{\partial \varPi_{R}^{\mathit{VN}}[w^{\mathit{VN}^{*}}(\overline{\alpha} ,\overline{\beta} )]}{\partial \overline{\alpha}} < 0\) when \(1 + 2c - 2\overline{\beta} < 0\). Equations (19) and (20) also give:

 □

Rights and permissions

Reprints and permissions

About this article

Cite this article

Chen, J., Bell, P.C. The impact of customer returns on supply chain decisions under various channel interactions. Ann Oper Res 206, 59–74 (2013). https://doi.org/10.1007/s10479-013-1326-3

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10479-013-1326-3

Keywords

Navigation