Consumer returns policies with endogenous deadline and supply chain coordination

Xu, Lei; Li, Yongjian; Govindan, Kannan; Xu, Xiaolin

doi:10.1016/j.ejor.2014.09.049

Cited by 126 publications

(47 citation statements)

References 47 publications

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“…Yet, exceptions exist, such as Ülkü et al () and Ülkü and Gürler (), that assume consumer valuation is distributed uniformly over

([], 0, \overset{v}{true})

, where

\overset{v}{true}

is equal to the sum of intrinsic valuation and positive extrinsic valuation received from the length of return time window set by the retailer. A similar approach is taken in Xu, Li, Govindan, and Xu (), where the incremental valuation received from return policy time leniency is added to both lower and upper bounds of the uniformly distributed intrinsic valuation parameter. Overall, this approach facilitates the assumption that customers are more likely to buy a product if the return window is longer.…”

Section: Analytical Researchmentioning

confidence: 99%

See 1 more Smart Citation

Taking stock of consumer returns: A review and classification of the literature

Abdulla

Ketzenberg

Abbey

2019

J of Ops Management

110

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Coincident with the rapid growth of consumer returns and their corresponding importance in the retail marketplace, academic interest in the area of consumer return policy design has significantly increased. In fact, the growth in academic publications has been tremendous, with almost half of the published works appearing within the past 6 years. The influx of new and evolving research spans across multiple disciplines and various methodologies. To provide clarity for the continued evolution of the field, we provide a comprehensive review and classification of the literature predicated on a holistic conceptual framework. The scope of the review includes all peer reviewed journal articles published prior to the end of 2018, along with any working papers cited therefrom, that specifically address (a) managerial decision‐making related to return policies or (b) consumer behavior in response to such decision‐making. Examining the state of the literature and practice on return policy design through the lens of a unified conceptual framework—a framework that spans both analytical and empirical research—reveals numerous managerial and theoretical opportunities for future research.

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“…Yet, exceptions exist, such as Ülkü et al () and Ülkü and Gürler (), that assume consumer valuation is distributed uniformly over

([], 0, \overset{v}{true})

, where

\overset{v}{true}

Section: Analytical Researchmentioning

confidence: 99%

“…Xu et al () consider a retailer deciding on a return deadline, along with refund amount, price, and order quantity. This study shows that the length of the product life cycle and consumer return rate plays crucial roles in the optimal time leniency decision.…”

Section: Analytical Researchmentioning

confidence: 99%

Taking stock of consumer returns: A review and classification of the literature

Abdulla

Ketzenberg

Abbey

2019

J of Ops Management

110

View full text Add to dashboard Cite

show abstract

“…U ovom radu, KPI su: (1) kompleksnost okruženja, (2) kompleksnost proizvodnog procesa, (3) operativna kompleksnost, i (4) kompleksnost koja nastaje usled integracije entiteta (Xu, Li, Govindan & Xu, 2015). Vrednost kompleksnosti okruženja može da se proceni respektujući podatke o promenljivosti tražnje.…”

Section: Kompleksnostunclassified

“…Xu (2015), and they are as follows: (1) the complexity of the environment, (2) the complexity of the production process, (3) operational complexity, and (4) the complexity arising due to the integration of the entities. The value of the complexity of the environment can be assessed with respect to the data on the demand variability.…”

Section: Complexitymentioning

confidence: 99%

Model for the supply chain management based on the interval type-2 fuzzy numbers and the TOPSIS method

Tadić¹,

Djordjević²

2017

Ekon horizonti

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The Performance improvement that leads to an increase in business efficiency, both for the enterprises integrated in the supply chain and the entire supply chain, represents one of the basic strategic management problems. A solution to this problem, among other things, can be obtained by measuring and improving the performance of the supply chain, which simultaneously represents the basic purpose of this research study. The relative importance of performances and the values of their key performance indices are assessed by decision-makers. Their assessments are described by linguistic variables, which are modelled by interval fuzzy numbers type-2. The relative importance of performance is obtained by defining the fuzzy matrix of the relative importance of each pair of performances. The weight values of performances are calculated by means of the eigenvector method. Performance values are calculated by using the fuzzy middle-value operator. The rank of the enterprises, with respect to all of the considered performances as well as their weights, is determined by applying conventional TOPSIS. The ranking of the enterprises integrated in the supply chain can be marked as the main result of the research. On the basis of the obtained rank, appropriate measures can be taken to improve the performance of those enterprises that are rated the worst by respecting all the observed performances. The proposed model has been tested on the real life data from the automotive supply chain operating in Central Serbia.

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“…Yoo (2014) explore the relationship between consumer returns policy and product quality decisions in which the retailer decides the optimal consumer returns policy and the supplier makes a decision on the product quality. Xu et al (2015) examine consumer returns where the consumer valuation depends on both the refund amount and the return deadline. They derive the retailer's optimal decisions including pricing, ordering, refund amount, and return deadline.…”

Section: Coordination In the Presence Of Consumer Returnsmentioning

confidence: 99%

Pareto Improving Supply Chain Coordination Under a Money-Back Guarantee Service Program

2017

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Abstract. Money-back guarantee (MBG) is a well-known service offered by many retailers. Under the MBG policy, a purchased item by a consumer can be returned for a full refund. In this paper, we explore a supply chain (SC) system comprising of one retailer, which offers the money-back guarantee policy and faces a stochastic demand. With a given supply contract offered by the supplier, the retailer makes decisions on the order quantity and the market coverage of the money-back guarantee service. With reference to real-world practices, we examine three models, namely, (1) the traditional wholesale pricing model without the buyback contract, (2) the model with a buyback contract (between the supplier and the retailer) for unsold items, and (3) a dual-buyback (DB) contract model for both unsold and consumer returned items. We find that using the buyback contract alone for unsold items cannot achieve Pareto improving supply chain coordination, whereas the DB contract can. Numerical findings also indicate that the DB contract can lead to an expansion of the money-back guarantee service program to cover more customers. This paper's findings support the implementation of the DB contract, which is commonly seen in the industry, for efficient supply chain management in the presence of the money-back guarantee service program.

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Consumer returns policies with endogenous deadline and supply chain coordination

Cited by 126 publications

References 47 publications

Taking stock of consumer returns: A review and classification of the literature

Taking stock of consumer returns: A review and classification of the literature

Model for the supply chain management based on the interval type-2 fuzzy numbers and the TOPSIS method

Pareto Improving Supply Chain Coordination Under a Money-Back Guarantee Service Program

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