Demand and Revenue Management of Deteriorating Inventory on the Internet: An Empirical Study of Flash Sales Markets

• Published date: 01 September 2017
• Date: 01 September 2017
• DOI: http://doi.org/10.1111/jbl.12157
• Author: Anníbal C. Sodero,Elliot Rabinovich

Demand and Revenue Management of Deteriorating Inventory on

the Internet: An Empirical Study of Flash Sales Markets

Ann

ıbal C. Sodero

1

, and Elliot Rabinovich

2

1

University of Arkansas

2

Arizona State University

In this study, we investigate demand and revenue management of deteriorating inventory in flash sales markets (FSM). Retailers operating

FSM source excess inventories of products from secondary markets in fixed lot sizes and offer them as part of deals that get no replenish-

ments and expire after running for predetermined periods (e.g., 24 hr) or when they sell out, whichever occurs first. We develop a demand fore-

casting model that incorporates the effects of sentiments conveyed by consumers in discussion forum posts associated with different deals on

the deals’empirical demand rates. We then conduct a survival analysis to find that the empirical demand rates projected from our forecasting

model are significant predictors of the deals’actual time to stockout, even after controlling for their initial inventory provisions and markdowns.

We also find that the predicted effects of these demand rates on stockout times are stronger at low markdowns. Our investigation offers insights

into different strategies that sellers operating FSM can pursue to improve their inventory performance. These strategies involve decisions that

sellers must make both a priori, before deals start, and a posteriori, according to real-time detection of departures from projected demand rates

as deals run their course.

Keywords: Internet retailing; demand management; revenue management; predictive analytics; Bass model; survival analysis

INTRODUCTION

The inventory and marketing management literatures have long

acknowledged the importance of demand and revenue manage-

ment to improve the effective provisioning of inventory to con-

sumers (Talluri and Van Ryzin 2006; Waller et al. 2008)

through a better matching of supply and demand (Spulber 1996;

Rabinovich 2004; Williams and Waller 2011; Jin et al. 2015).

Demand and revenue management are of particular interest for

firms selling fixed and limited amounts of deteriorating invento-

ries that lose their value after finite selling periods (Ferguson and

Koenigsberg 2007; Bakker et al. 2012). Those firms will incur

undesirable overage costs when they are unable to sell all of the

inventory made available for the selling periods because they

will need to salvage the inventory surplus at a loss (Petruzzi and

Dada 1999; Cachon and K€

ok 2007). They will also incur unde-

sirable shortage costs when they sell out before the end of the

periods because, in such cases, they could have set higher prices

to capture demand from consumers with higher inventory valua-

tions (Bitran and Caldentey 2003). Thus, forecasting demand and

setting prices to clear this type of deteriorating inventory as late

as possible in the selling periods will have significant implica-

tions for the sellers’profitability.

Forecasting demand and setting prices is not an easy task for

firms selling deteriorating inventory over limited time periods,

though. As consumers accumulate information about the inven-

tory during the selling periods, they will modify their valuations

about the inventory’s worth and, consequently, their decisions

about whether to purchase it. When a firm is able to price dis-

criminate, it may attempt to entice consumers with changing

valuations about the inventory’s worth by dynamically pricing

that inventory during the selling period (Chatwin 2000; Bitran

and Caldentey 2003) or by distributing coupons (Olsen and Par-

ker 2008). However, when a firm is unable to price discriminate,

its ability to set market-clearing prices for the inventory will

depend predominately on the accuracy of its forecasts. Of these

two conditions (to price discriminate or not to price discrimi-

nate), the latter has received little attention in the literature. The

absence of meaningful guidance from the literature has motivated

us to engage in this research.

Our aim is to contribute to the literature on demand and rev-

enue management by investigating forecasting and pricing for

deteriorating inventory when a firm is unable to price discrimi-

nate and must sell its inventory during limited time periods. To

pursue our goal, we focus our study, without loss of generaliz-

ability, on flash sales markets (FSM). FSM are an Internet retail-

ing segment worth approximately $3.8 billion in the United

States alone (McKitterick 2015). Retailers operating FSM typi-

cally source inventories of products from secondary markets in

fixed lot sizes and offer them as part of deals that get no replen-

ishments and expire either after running for predetermined peri-

ods (e.g., 24 hr) or when they sell out, whichever occurs first

(Baird 2015; Ferreira et al. 2015).

In FSM, shoppers obtain information to form their valuations

about a deal’s worth in many ways. Shoppers may use the deal’s

price as a signaling mechanism of its worth (Wolinsky 1983;

Milgrom and Roberts 1986). Moreover, because search costs are

low on the Internet (Bakos 1997; Brynjolfsson and Smith 2000;

Brynjolfsson et al. 2011), shoppers may seek information about

a deal’s worth outside of the retailer’s website, for instance, by

browsing other retailers’websites to compare prices. Further-

more, shoppers may observe consumers’purchases of a particu-

lar deal in real time, for instance, when those consumers share

information about their purchases on social media (e.g.,

Facebook) or the retailer divulges that information on its website,

which will also signal that deal’s worth (Cowan et al. 1997; Manski

Corresponding author:

Ann

ıbal C. Sodero, Department of Supply Chain Management, Sam

M. Walton College of Business, University of Arkansas,

Fayetteville, AR 72701, USA; E-mail: asodero@walton.uark.edu

Journal of Business Logistics, 2017, 38(3): 170–183 doi: 10.1111/jbl.12157

© Council of Supply Chain Management Professionals