A loss-averse retailer–supplier supply chain model under trade credit in a supplier-Stackelberg game

Wu, Cheng-Feng; Liu, Xin; Li, Annan

doi:10.1016/j.matcom.2020.10.025

Cited by 23 publications

(13 citation statements)

References 61 publications

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“…To show the literature related to this work, Table 1 is presented with relevant keywords. [8] Castellano et al [19] Shi et al [55] Govindarajan et al [13] Fan et al [31] Wu et al [29] Bi et al [56] This paper…”

Section: Literature Reviewmentioning

confidence: 97%

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Transfer of Risk in Supply Chain Management with Joint Pricing and Inventory Decision Considering Shortages

Khan

Sarkar

2021

Mathematics

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This study is the first to consider a distribution-free approach in a newsvendor model with a transfer of risk and back-ordering. Previously, in many articles, discrete demand is considered. In this model, we consider a newsvendor selling a single seasonal item with price-dependent stochastic demand. Competition in markets has forced the retailer and manufacturer to coordinate in decentralized supply chain management. A coordination contract is made between a retailer and manufacturer to overcome the randomness of demand for a short-life-cycle product. The retailer pays an additional amount per product to transfer the risk of unsold items. The manufacturer bears the cost of unsold products from the retailer. Shortages are allowed with back-ordering costs during the season. The distribution-free model is developed and solved with only available demand data of mean and standard deviation. Stackelberg’s game approach is used to calculate the optimal ordering quality and price. This model aims to maximize expected profit by optimizing unit selling price and ordered quantity through coordination. To illustrate that the model is robust, numerical experiment and sensitivity analyses are conducted for both decentralized and centralized supply chain management. For applicability of the model in the real-world business scenario, managerial insights are provided with sensitivity analysis.

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Section: Literature Reviewmentioning

confidence: 97%

“…They calculated the ordered quantity, the wholesale price, and the interest rate. Wu et al [29] presents a model with a risk-averse retailer and a supplier who offers a loss-sharing and trade credit under the supplier Stackelberg game. The decision variables in their model are the order quantity and loss sharing.…”

Section: Literature Reviewmentioning

confidence: 99%

Transfer of Risk in Supply Chain Management with Joint Pricing and Inventory Decision Considering Shortages

Khan

Sarkar

2021

Mathematics

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“…e * D,∆m − e * D = − λ m (1 + λ r )τh(a − c) 2 h[4(1 + λ m ) 2 + 4(1 + λ r ) + 2λ m (1 + λ r )] − [(1 + λ m ) 2 + (1 + λ r )]τ 2 (4h − τ 2 ) < 0By Equations (10) and(32), and combining with 0 < τ 2 /h < 2 yields:e * D,∆ r − e * D = − 4τhλ r (1 + λ m )(a − c) 4h(1 + λ r )(2 + λ m + λ r ) − [(1 + λ r ) 2 + (1 + λ m )]τ 2 (4h − τ Differentiating e * D,∆ m with respect to λ m and λ r yields: de * D,∆m dλ m = 2hτ(a − c)(1…”

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confidence: 89%

“…Shalev's version has been received widespread attention in the field of game theory [24][25][26][27]. In particular, the value function and its variants that characterizes the loss-aversion preference of decision makers has been applied to supply chain decisions [28][29][30][31][32]. Until recent years, the impacts of loss-aversion preferences on decisions have increasingly received attention from an increasing number of scholars in the SCM [33][34][35][36].…”

Section: Introductionmentioning

confidence: 99%

Green Supply Chain Management with Nash Bargaining Loss-Averse Reference Dependence

Feng

Tan

et al. 2021

Mathematics

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This paper investigates a two-echelon green supply chain (GSC) with a single loss-averse manufacturer and a single loss-averse retailer. Since the Nash bargaining solution exactly characterizes endogenous power and the contribution of the GSC members, it is introduced as the loss-averse reference point for the GSC members. Based on this, a decision model of the two-echelon GSC with loss aversion is formulated. The optimal strategies of price and product green degree are derived in four scenarios: (a) the centralized decision scenario with rational GSC members, namely the CD scenario; (b) the decentralized decision scenario with rational GSC members, namely the DD scenario; (c) the decentralized decision scenario with the GSC members loss-averse, where the manufacturer’s share is below its own loss-averse reference point, namely the DD(∆m ≥ πm) scenario; (d) the decentralized decision scenario with the GSC members loss-averse, where the retailer’s share is below its own loss-averse reference point, namely the DD(∆r ≥ πr) scenario. Then, a comparative analysis of the optimal strategies and profits in these four scenarios is conducted, and the impacts of loss aversion and green efficiency coefficient of products (GECP) on the GSC are also performed. The results show that (i) GECP has a critical influence on the retail price and the wholesale price; (ii) the GSC with loss aversion provide green products with the lowest green degree; (iii) the retail price, the wholesale price and product green degree are decreasing monotonically with the loss aversion level of the GSC member without incurring loss; (iv) furthermore, the effect of the loss aversion level of the GSC member with incurring loss on the optimal strategies is related to GECP and the gap between the GSC members’ loss aversion levels.

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“…In the supply chain of agriculture and forestry, the output of agricultural products and forest products is uncertain due to the influence of climate, diseases, and insect pests. Some literature has focused on supply chain contract design under the condition of uncertain supply and demand [8][9][10].…”

Section: Introductionmentioning

confidence: 99%

Supply Chain Coordination with Capital-Constrained and Loss Aversion Retailer under Stochastic Demand and Supply

Yueli¹,

Lu²,

Xueyun³

2022

Mathematical Problems in Engineering

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In the face of increasingly complex market environment, supply chain members pay attention to the possibility of obtaining expected profits and various risks while considering profit maximization. The behaviors of decision makers are often influenced by their psychological preferences, and loss aversion is one of the basic characteristics of human economic behavior. We research the supply chain coordination problem with capital constraint and loss-averse retailer in the case of stochastic production and demand. In order to address the loss aversion, a novel utility function is structured according to the theory of mental account. Under wholesale price contract, the optimal expected utility of supply chain under decentralized decision is smaller than that under centralized decision due to the retailer’s constrained capital. Based on that, a model is constructed by making revenue sharing contract act as the dual role of supply chain coordination and delayed payment strategy without seeking external financing or internal financing, and the contract conditions and optimal decisions of the coordinated supply chain are solved. Theoretical analysis indicates that when the contract parameters are properly selected, even though the retailer’s capital is constrained, the revenue sharing contract can not only coordinate the supply chain but also realize the arbitrary distribution of supply chain profits within a certain range. Finally, the change of decision variables and expected utility or profit with contract parameters is analyzed by several numerical examples.

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A loss-averse retailer–supplier supply chain model under trade credit in a supplier-Stackelberg game

Cited by 23 publications

References 61 publications

Transfer of Risk in Supply Chain Management with Joint Pricing and Inventory Decision Considering Shortages

Transfer of Risk in Supply Chain Management with Joint Pricing and Inventory Decision Considering Shortages

Green Supply Chain Management with Nash Bargaining Loss-Averse Reference Dependence

Supply Chain Coordination with Capital-Constrained and Loss Aversion Retailer under Stochastic Demand and Supply

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