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

• Author: Scott Webster,Dwayne Cole,Santosh Mahapatra
• Date: 01 September 2017
• DOI: http://doi.org/10.1111/jbl.12159
• Published date: 01 September 2017

A Comparison of Buyback and Trade-In Policies to Acquire Used

Products for Remanufacturing

Dwayne Cole

1

, Santosh Mahapatra

2

, and Scott Webster

3

1

Florida A&M University

2

Clarkson University

3

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 findings 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 profit than a buyback policy.

Second, we show that the profitability 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

INTRODUCTION

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 firm. 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 profit—the total of new

and remanufactured product profit 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 influenced by the product’s 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

influence this evolution. Our model of new and remanufactured

product sales over time reflects empirically observed characteris-

tics (

€

Ostlin et al. 2009). Additionally, our models account for

customer heterogeneity that influences the valuation of the new

product. Thus, our models reflect 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

consumers.

Our paper makes three main contributions. First, we define

buyback and trade-in models that capture the dynamics of new

and remanufactured product demand, reflecting 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

paper.

Second, we offer several insights into effect of policy choice

on system performance. (i) We find that there is little difference

in profitability 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 profitable, the buyback price must

be more than the new product profit 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

profitable 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:

scott.webster@asu.edu

Journal of Business Logistics, 2017, 38(3): 217–232 doi: 10.1111/jbl.12159

© Council of Supply Chain Management Professionals

significant price reductions as it goes through its life cycle, then

the firm may benefit 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 profitable 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 profitability of two alternative pricing

algorithms: myopic and proactive. Myopic pricing sets used pro-

duct acquisition prices to maximize profit in each period with no

consideration of future. In contrast, proactive pricing considers

the future periods. We find 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 profit 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 firm may

wish to consider these results when making marketing and pro-

duct design decisions—decisions that can impact the time lag

and sweet spot, respectively.

The remainder of the paper is organized into five sections.

The first 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 S1–S6.

RELATED LITERATURE

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 benefits from offering trade-in programs are due largely

to market segmentation and price discrimination, as the firm 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

analysis.

In contrast to marketing literature, the closed-loop supply

chain (CLSC) literature, focuses on how trade-in and buyback

incentives affect the reverse flow 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 profit 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) profit 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

period—this 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 fits 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 significantly enhance the model’sfit

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

rate.

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 influ-

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.