A Comparison of Buyback and Trade‐In Policies to Acquire Used Products for Remanufacturing

Date01 September 2017
Published date01 September 2017
A Comparison of Buyback and Trade-In Policies to Acquire Used
Products for Remanufacturing
Dwayne Cole
, Santosh Mahapatra
, and Scott Webster
Florida A&M University
Clarkson University
Arizona State University
We study how acquisition policies for used products as a source for a remanufactured consumer product affect system performance. We
introduce a consumer choice model of new product purchase and used product return, which is consistent with the classic Bass diffusion
model of sales over time. We capture new and remanufactured product sales, the evolution of the install base, and consumer return and repur-
chase decisions over the life cycles of new and remanufactured product. Our analyses lead to two main ndings on the acquisition policies.
First, if the buyback price is less than the margin of a new product, then a trade-in policy is likely to yield higher prot than a buyback policy.
Second, we show that the protability is highest when the time lag between the introduction of a new product and initial demand for a remanu-
factured version of the product is at or near the sweet spot, which is the age of the product at which the costs of acquisition and remanufactur-
ing are at minimum. Further, when the time lag between the introduction of a new product and initial demand for its remanufactured version is
near the sweet spot, then simple pricing methods are close to optimal.
Keywords: remanufacturing; returns acquisition policy; product life cycle
In this paper, we examine two common policies for the
acquisition of a used consumer product for remanufacturing:
buyback and trade-in. A buyback policy offers cash for a return
and a trade-in policy offers a credit to be applied to purchase a new
product (i.e., repurchase) from the rm. A trade-in program has the
advantage of linking each return to the sale of a new product. But,
due to the replacement purchase requirement, the cost to generate
return volume can be higher than a buyback policy. We examine
how the choice of policy affects system protthe total of new
and remanufactured product prot that is generated from return
transactions over the respective life cycles. Our goal is to lend
insight into factors that favor one policy over the other.
Sustainability concerns and the rise of extended producer
responsibility legislation have generated a growing interest in
product take-back and remanufacturing practices. Remanufac-
tured consumer products are becoming increasingly available and
are well established in home appliance and computer sectors,
among others. Apple and HP, for example, sell new and remanu-
factured versions of their computers (apple.com 2015, hp.com
2015c). Apple offers a trade-in credit for a return (poweron.com
2015). HP offers a trade-in program in Canada (hp.com 2015a)
and recently introduced a program in the United States that gives
customers the option of cash or a trade-in credit for a return
(hp.com 2015b).
The demand for remanufactured product, as well as supplies,
is inuenced by the products life cycle (PLC). We investigate
how the PLC dynamics of new and remanufactured products
impact the buyback and trade-in product acquisition policies over
time. A distinguishing feature of our analysis is the consideration
of time dynamics. In particular, we model how the quantity and
condition of used products and the market interest in remanufac-
tured products evolve over time, and how the new product sales
inuence this evolution. Our model of new and remanufactured
product sales over time reects empirically observed characteris-
tics (
Ostlin et al. 2009). Additionally, our models account for
customer heterogeneity that inuences the valuation of the new
product. Thus, our models reect the dynamics of the demand
for new product, the product install base, customer willingness to
return, and the demand for remanufactured products. Install base
refers to total volume of quality and age of products with the
Our paper makes three main contributions. First, we dene
buyback and trade-in models that capture the dynamics of new
and remanufactured product demand, reecting elements of real-
istic complexity that are important for logistics modeling (Wal-
ler and Fawcett 2012). The models can serve as a stepping
stone for future investigations into utilization of alternate acqui-
sition strategies while accounting for life cycle dynamics. We
indicate directions for future development at the end of the
Second, we offer several insights into effect of policy choice
on system performance. (i) We nd that there is little difference
in protability between a buyback program and a trade-in pro-
gram when the new product repurchase rate of buyback cus-
tomers is high. (ii) If the repurchase rate is not high, then the
relative performance of the acquisition policies can be found by
comparing the buyback price with the new product margin. For
the buyback policy to be more protable, the buyback price must
be more than the new product prot margin adjusted for the frac-
tion of customers resistant to repurchase and the cost to over-
come their resistance. Otherwise, the trade-in program is more
protable than the buyback program. (iii) If a product faces
Corresponding author:
Scott Webster, W.P. Carey School of Business, Arizona State
University, 300 E. Lemon St., Tempe, AZ 85287, USA; E-mail:
Journal of Business Logistics, 2017, 38(3): 217232 doi: 10.1111/jbl.12159
© Council of Supply Chain Management Professionals
signicant price reductions as it goes through its life cycle, then
the rm may benet from a mixed policy: A trade-in program in
the early stages of the PLC and at the very end of the life cycle
as a new generation of the product is being phased in, with a
buyback policy in the interim. A trade-in policy is likely to be
more protable than a buyback policy when the new product
price is high (e.g., in the early stages of the life cycle). And a
trade-in program at the very end of the life cycle offers the
advantage of spurring sales of next generation of the product.
Third, we compare the protability of two alternative pricing
algorithms: myopic and proactive. Myopic pricing sets used pro-
duct acquisition prices to maximize prot in each period with no
consideration of future. In contrast, proactive pricing considers
the future periods. We nd that when the remanufactured product
time lag and sweet spot (i.e., age of the product at which the
costs of acquisition and remanufacturing are at minimum) are
aligned, there is little loss in prot from using myopic pricing
instead of proactive pricing. Myopic pricing has the advantages
of (i) being much simpler to implement than proactive pricing
and (ii) not relying on accurate demand projections. A rm may
wish to consider these results when making marketing and pro-
duct design decisionsdecisions that can impact the time lag
and sweet spot, respectively.
The remainder of the paper is organized into ve sections.
The rst section outlines the related literature and how our inves-
tigation differs from past research. The second section presents
the models and the third section presents the analysis. The last
section summarizes the managerial insights from analysis and
offers suggestions for future research. A list of notation, assump-
tions, formulations, algorithms, and proofs can be found in
Appendices S1S6.
Trade-in and buyback policies of used products are commonly
practiced in durable goods industries (i.e., automotive, consumer
electronics, computers, and industrial equipment), and are well
studied in the marketing literature. The marketing literature
focuses on how trade-in and buyback programs affect the prof-
itability of new product sales. Earlier works in this area show
that the benets from offering trade-in programs are due largely
to market segmentation and price discrimination, as the rm is
able to price discriminate between owners and nonowners
(Levinthal and Purohit 1989; Van Ackere and Reyniers 1995;
Fudenberg and Tirole 1998; Okada 2001, 2006; Bruce et al.
2006; Rao et al. 2009). These papers are relevant because they
provide models of return volumes as a function of trade-in and
buyback prices. However, these papers only consider a two-
period setting where the trade-in prices are set in period two. We
consider a multiperiod setting where trade-in prices are set every
period. Furthermore, none of these papers incorporate the eco-
nomics of product take-back for remanufacturing and heterogene-
ity in new product purchase behavior that are central to our
In contrast to marketing literature, the closed-loop supply
chain (CLSC) literature, focuses on how trade-in and buyback
incentives affect the reverse ow of used products (e.g., for the
purposes of product recovery to support remanufacturing and/or
respond to take-back legislation). Product acquisition manage-
ment is an important function of CLSC systems with remanufac-
turing (Thierry et al. 1995; Guide and Jayaraman 2000; Klausner
and Hendrickson 2000; Flapper 2001; Rogers et al. 2012; Souza
2013). Several closed-loop operations studies evaluate prot and
the volume of product returns as a function of condition-depen-
dent acquisition price either under a buyback program (e.g.,
Guide et al. 2003; Bakal and Akcali 2006; Karakayali et al.
2007; Xu et al. 2012; Keyvanshokooh et al. 2013; Xiong
et al. 2014) or under a trade-in program (e.g., Ray et al. 2005;
Li et al. 2011). Li et al. (2011) show that consideration for pro-
duct characteristics and customer heterogeneity results in better
forecast of trade-in acquisitions. The past studies mainly solve
for optimal condition-dependent acquisition prices of used prod-
ucts and the product selling prices for remanufactured or new
products. In general, acquisition and selling prices are set when
one or more of the following restrictions apply: (i) prot is maxi-
mized in a single period, (ii) quality and age of the used prod-
ucts are not considered, (iii) used product supply constraints are
not included, and (iv) customer valuation of existing products is
not considered. The main distinguishing features between past
papers and our work are as follows: (i) we compare and contrast
both buyback and trade-in programs; (ii) we consider the acquisi-
tion pricing over the life cycle of a product rather than a single
periodthis enables inclusion of the supply constraints while
accounting for age of the product; and (iii) we account for cus-
tomer valuation of existing product.
We utilize an innovation diffusion model similar to the one
proposed by Bass (1969) to forecast the demand and supply
dynamics over the PLC. The Bass diffusion model forecasts dur-
able product sales quite accurately even though it does not con-
sider price elasticity of demand and does not include the price as
a decision variable (Chien et al. 2010). Prices generally decline
across the PLC stages over time (e.g., Curry and Riesz 1988).
When the prices change more or less consistently, the original
Bass (1969) model ts the generalized diffusion process that con-
siders price as a decision variable (Bass et al. 1994). Several
empirical studies validate that price as a decision variable in the
diffusion models does not signicantly enhance the modelst
or predictive validity (e.g., Bottomley and Fildes 1998). Kama-
kura and Balasubramanian (1987) forecast adoption of new pro-
duct while including replacement purchase in a diffusion model
and considering price as an exogenous variable. Consistent with
these studies, we consider price as an exogenous variable in our
new product demand diffusion model, consider replacement pur-
chase, and allow the new product prices to change at a constant
The PLC explains how the sale of a new product grows,
matures, and declines over time (Mahajan et al. 1990). The PLC
also affects the availability of used products and the demand for
remanufactured products over time. Thus, PLC dynamics inu-
ences both forward and reverse operations (e.g., collection,
recovery, and remarketing strategies). Tibben-Lembke and
Rogers (2002) and
Ostlin et al. (2009) describe the general rela-
tionship between new product sales, used product returns, and
remanufactured product demand, and discuss how these relation-
ships vary over the PLC.
218 D. Cole et al.

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