Return policy and supply chain coordination with network-externality effect

Abstract

The benefits of a consumer return policy have been extensively studied in extent literature. This paper explores the potentially damaging impact of a return policy on the retailer. We develop an analytical framework and examine the economic impact of consumer return among consumers, retailer and supply chain. We distinguish three network-externality (NE) cases: no network externality, fixed network-externality and variable network-externality contingent on return amount to discuss the retailer’s selling price, refund and inventory policies. Our analysis derives the optimal policies and shows that they take the form of contingence model in which the policies depend on consumer initial return and NE return. We also examine the influence of the consumer return NE effect on buy-back contracts of the supply chain and show that while the traditional buy-back contract fails to coordinate the supply chain, the NE effect does not render the differentiated buy-back contract less effective. Finally, we extend our study to a heterogeneous consumer case.

Keywords:

• Supply chain coordination
• behavioural operation management
• network externality
• return policy
• buy-back contract

Acknowledgements

We acknowledge the support of (i) National Natural Science Foundation of China (NSFC), Research Fund No. 71302005 and 71672125, for L. Xu; (ii) National Natural Science Foundation of China(NSFC), Research Fund No. 71372100 and 71725004 for Y.J. Li.

Notes

1. Refer to https://www.cnbc.com/2016/12/16/a-260-billion-ticking-time-bomb-the-costly-business-of-retail-returns.html.

2. Refer to https://assets.kpmg.com/content/dam/kpmg/pdf/2016/02/omnichanel-retail-survey-2016.pdf.

3. Refer to http://www.tmall.com.

4. Refer to http://www.taobao.com.

5. When consumers observe other buyers return the product, their valuation will change with the return event, and this bring more consumer returns. Moreover, the more the returns are, the lower the consumer valuation is, and this is the consumer return network externality.

6. For details, please refer to the link: https://en.wikipedia.org/wiki/2009%E2%80%9311_Toyota_vehicle_recalls.

7. An experience good is the one for which the consumer does not know if it is a good match with his or her preferences until after purchase (Nelson, 1970). For this product, consumers willing to buy, need to buy and try the products to discover his own utility, no matter how many questions he asks of others and how much information he searches, because of the ‘experience goods’ nature of the product (Shulman, Coughlan, and Savaskan 2010, 2011).

8. This assumption on consumers' valuation before and after purchasing the product, makes our model capture both aggregate uncertainty (market size) and individual uncertainty (independent consumer valuations), and it is commonly used in literature on consumer valuation and consumer return (for more details, refer to Davis et al. 1998, Su 2009, Shulman et al. 2009, Li et al. 2014 etc.)

9. For details, please refer to http://www.toyota.com/recall and http://corporatenews.pressroom.toyota.com/safety-recall

10. Davis, Hagerty, and Gerstner (1998) consider a similar consumer return problem, and adopt a uniform distribution over [0,1] to examine the optimal solution. It is a special case of our paper when the return deadline is not considered. Shulman, Coughlan, and Savaskan (2009) also adopt a uniform distribution with a support of [0, 1], however, only consider the case with two horizontally differentiated products. Similarly, Shulman, Coughlan, and Savaskan (2010) also use this uniform distribution.