A returns policy for distribution channel coordination of perishable items

Hahn, Kyu Hun; Hwang, Hark; Shinn, Seong Whan

doi:10.1016/s0377-2217(02)00753-1

Cited by 50 publications

(21 citation statements)

References 12 publications

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“…Yao, Yue, Wang, and Liu (2005) studied buyback contract with information sharing about the demand. Looking at the product property, Pasternack (1985) and Hahn, Hwang, and Shinn (2004) presented the buyback contract for perishable products. Bose and Anand (2007) studied two kinds of transfer prices, one of which is exogenous and another is unilaterally declared by one dominant party.…”

Section: Introductionmentioning

confidence: 99%

Coordinating a supply chain with backup supplier through buyback contract under supply disruption and uncertain demand

Giri

Bardhan

2014

International Journal of Systems Science: Operations & Logi

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This paper develops a supply chain model consisting of a single supplier and a single retailer. The market demand is assumed to be stochastic and the supplier is prone to disruption. The centralised model is provided as benchmark, and non-coordinated model is studied with the wholesale price-only contract. To enhance the total expected profit, we apply buyback contract and show that the chain may be coordinated thorough this contract, although the chain is vulnerable to disruption. To mitigate the effect of disruption, we further propose a backup supplier to be involved in the chain. We show that the backup supplier substantially enhances the profit of the retailer when there is no coordination between the primary supplier and the retailer. Furthermore, if the retailer is allowed to coordinate with the primary supplier, we show that the coordination remains effective for certain range of disruption probability and collapses thereafter. Numerical examples are given to illustrate the developed model.

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Section: Introductionmentioning

confidence: 99%

Coordinating a supply chain with backup supplier through buyback contract under supply disruption and uncertain demand

Giri

Bardhan

2014

International Journal of Systems Science: Operations & Logi

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“…Actually, product return is a necessary and unavoidable evil for business and reverse logistics [2], [7]. Various industries are thus forced to provide their customers with a guaranteed return policy [13], [14], [17]- [19], including even the pharmaceutical sector [10]. In practice, product return policies vary from strict to lenient.…”

Section: Introductionmentioning

confidence: 99%

“…Customers expect that businesses with the most generous type of return policy offer an unconditional refund [10], [15]. Certainly, a generous return policy can stimulate the purchasing decisions of customers, but it is, on the other hand, accompanied by a higher number of return transactions that incur additional administration and logistics costs [3].…”

Section: Introductionmentioning

confidence: 99%

An Agent-Based Expert System Architecture for Product Return Administration

Wang¹

2013

IEICE Trans. Inf. & Syst.

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SUMMARYProduct return is a critical but controversial issue. To deal with such a vague return problem, businesses must improve their information transparency in order to administrate the product return behaviour of their end users. This study proposes an intelligent return administration expert system (iRAES) to provide product return forecasting and decision support for returned product administration. The iRAES consists of two intelligent agents that adopt a hybrid data mining algorithm. The return diagnosis agent generates different alarms for certain types of product return, based on forecasts of the return possibility. The return recommender agent is implemented on the basis of case-based reasoning, and provides the return centre clerk with a recommendation for returned product administration. We present a 3C-iShop scenario to demonstrate the feasibility and efficiency of the iRAES architecture. Our experiments identify a particularly interesting return, for which iRAES generates a recommendation for returned product administration. On average, iRAES decreases the effort required to generate a recommendation by 70% compared to previous return administration systems, and improves performance via return decision support by 37%. iRAES is designed to accelerate product return administration, and improve the performance of product return knowledge management. key words: product return, intelligent agent, case-based reasoning, intelligent system design

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“…Marvel and Peck (1995), Lau and Lau (1999), Emmons and Gilbert (1998), Wang (2004), Hahn et al (2004), and Choi et al (2004) extended Pasternack's work to solve various manufacturer–retailer returns problems. More recent studies on these returns policies incorporated other managerial issues.…”

Section: Introductionmentioning

confidence: 99%

The impact of customer returns on decisions in a newsvendor problem with and without buyback policies

Chen¹,

Bell²

2010

International Transactions in Operational Research

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In this paper, we examine how customer returns influence the retailer's ordering decision, the manufacturer's wholesale price decision, and the profits of the manufacturer and the retailer, in a single‐period, stochastic demand (newsvendor) setting. When the manufacturer is a Stackelberg leader and the retailer is the follower, we also examine how to contract a buyback policy, where the manufacturer buys back both unsold inventory and customer‐returned products, so that both the manufacturer and the retailer are more profitable than if they operate independently. We also show how this work can be generalized to the case of multiple retailers.

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A returns policy for distribution channel coordination of perishable items

Cited by 50 publications

References 12 publications

Coordinating a supply chain with backup supplier through buyback contract under supply disruption and uncertain demand

Coordinating a supply chain with backup supplier through buyback contract under supply disruption and uncertain demand

An Agent-Based Expert System Architecture for Product Return Administration

The impact of customer returns on decisions in a newsvendor problem with and without buyback policies

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