# Online product returns risk assessment and management

## Abstract

Commonly viewed as a cost center from an operations perspective, product returns have the potential to strongly influence operating margins and business profitability, thus constituting a risk for online retailers. This work addresses the problem of how to assess and manage product returns costs using a risk analysis methodology. Online product returns are seen as a random phenomenon that fluctuates in severity over time, threatening the profitability of the online store. Therefore, the starting point is that this risk can be modeled as a future random stream of payments. Given one or many future time periods, we aim to assess and manage this risk by answering two important questions: (1) Pricing—or what dollar amount factored on top of the current price of goods sold online would cover the cost of product returns, and (2) Reserving—or how much capital does an online retailer need to reserve at the beginning of each period to cover the cost of online product returns. We develop our analysis for one period (a month) by a closed formula model, and for multi-period (a year) by a dynamic simulation model. Risk measurements are executed in both cases to answer the two main questions above. We exemplify this methodology using an anonymized archival database of actual purchase and return history provided by a large size US women’s apparel online retailer.

## Keywords

E-commerce Risk analysis Risk management Online product return Simulation## Mathematics Subject Classification

91B32 91B30 90B90## References

- Anderson E, Hansen K, Simester D (2009) The option value of returns: theory and empirical evidence. Market Sci 28:405–423CrossRefGoogle Scholar
- Che Y (1996) Customer return policies for experience goods. J Indus Econ XLIV:17–24Google Scholar
- Chen J, Bell P (2009) The impact of customer returns on pricing and orders decisions. Eur J Oper Res 195:280–295CrossRefGoogle Scholar
- Denuit M, Dhaene J, Goovaerts M, Kaas R (2005) Actuarial theory for dependent risks. Measures, orders and models. Wiley, USACrossRefGoogle Scholar
- Dowd K (2006) Measuring market risk. Wiley, USAGoogle Scholar
- Goh T, Varaprasad N (1986) A statistical methodology for the analysis of the life-cycle of reusable containers. IIE Trans 18:42–47CrossRefGoogle Scholar
- Griffis SE, Rao S, Goldsby TJ, Niranjan TT (2012) The customer consequences of returns in online retailing: an empirical analysis. J Oper Manag 30:282–294CrossRefGoogle Scholar
- Hess J, Chu W, Gerstner E (1996) Controlling product returns in direct marketing. Market Lett 7:307–317CrossRefGoogle Scholar
- Hess J, Mayhew G (1997) Modelling merchadise return in direct marketing. J Direct Market 11:20–35CrossRefGoogle Scholar
- Krapp M, Nebel J, Sahamie R (2013) Forecasting product returns in closed-loop supply chains. Int J Phys Distrib Logistic Manage 43:614–637CrossRefGoogle Scholar
- Mollenkopf DA, Rabinovich E, Laseter TM, Boyer KK (2007) Manaing internet product returns: a focus on effective service operations. (D.S. Institute, Ed.). Decis Sci 38(2):215–250CrossRefGoogle Scholar
- Mollenkopf D, Frankel R, Russo I (2011) Creating value through returns management: exploring the marketing-operations interface. J Oper Manage 29:391–403CrossRefGoogle Scholar
- Mukhopadhyay S, Setoputro R (2004) Reverse logistics in e-business. Optimal price and return policy. Int J Phys Distrib Logistics Manage 34:70–88CrossRefGoogle Scholar
- Ramanathan R (2011) An empirical analysis on the influence of risk on relationships between handling of product returns and customer loyalty in E-commerce. Int J Prod Econ 130:255–261CrossRefGoogle Scholar
- Toktay L, Wein L, Zenios S (2000) Inventory management of remanufacturable products. Manage Sci 46:1412–1426CrossRefGoogle Scholar
- Toktay L (2003) Forecasting product returns. In: Guide D, Van Wassenhove L (eds) Business aspects of closed-loop supply chains, vol 2. Carnegie Bosch Institute. International Management SeriesGoogle Scholar
- Toktay L, Van der Laan E, de Brito M (2004) Managing product returns: Informational issues and forecasting methods. In: Dekker R, Fleischmann M, Inderfurth K, Wassenhove L (eds) Reverse logistics: quantitative methods for close-loop supply chain. Springer-Verlag, BerlinGoogle Scholar
- Tsay A (2002) Risk sensitivity in distribution channel partnership: implications for manufacturers return policies. J Retail 78:147–160CrossRefGoogle Scholar
- Tse Y (2009) Non life actuarial models. Theory, methods and evaluation. Cambridge University Press, New YorkCrossRefGoogle Scholar