scholarly journals Coordination mechanisms of a three-layer supply chain under demand and supply risk uncertainties

Author(s):  
Bibhas Giri ◽  
Joyanta Kumar Majhi ◽  
Kripasindhu Chaudhuri

This paper considers a newsvendor model for a single product to focus on the importance of coordination under demand and supply uncertainties where the raw materials are procured from two unreliable suppliers without any emergency resource; the main supplier (which is cheaper but more unreliable) is prone to random supply disruption and, therefore, it can satisfy all or nothing of the buyer's order, while the backup supplier (which is expensive but less unreliable) is prone to random yield and, therefore, can satisfy only a random fraction of the buyer's order. From the numerical results, we observe that it would be optimal to over-utilize the backup supplier and under-utilize the main supplier if the maximum growth in supply risk results from supply disruption. On the other hand, when the growth in supply risk occurs mainly due to increase in yield risk, the optimal risk mitigation strategy would be to increase the use of the backup supplier and decrease the use of the main supplier.  We propose the price only contract and a new revenue sharing contract to mitigate demand and supply uncertainties in the decentralized model, and observe that the revenue sharing contract can fully coordinate the supply chain with win-win outcome for all entities involved in the supply chain.

2012 ◽  
Vol 2012 ◽  
pp. 1-14 ◽  
Author(s):  
Subrata Saha ◽  
Sambhu Das ◽  
Manjusri Basu

We explore coordination issues of a two-echelon supply chain, consisting of a distributor and a retailer. The effect of revenue-sharing contract mechanism is examined under stock-time-price-sensitive demand rate. First, we investigate relationships between distributor and retailer under noncooperative distributor-Stackelberg games. Then we establish analytically that revenue sharing contact is able to coordinate the system and leads to the win-win outcomes. Finally, numerical examples are presented to compare results between the different models.


2014 ◽  
Vol 697 ◽  
pp. 482-487
Author(s):  
Shi Ying Jiang ◽  
Chun Yan Ma

Background on two stages green supply chain consisting of a manufacturer and a retailer, considering the degree of risk aversion and product greenness, consumer preferences and other factors, the centralized decision-making game model and manufacturer-leading Stackelberg game model are established.Then two game models are compared. The interaction of product greenness, wholesale price, product price,and risk aversion utility for manufacturers and retailers are also disscussed. Finally, the revenue sharing contract is applied to coordinate the green supply chain . The results show that:(1) In the centralized decision-making model, there is a critical value of the product green degree; (2)In manufacturer-leading Stackelberg game model, the higher the green degree of the product, the higher the manufacturer's wholesale price,and the wholesale price increases as risk aversion degree of manufacturers improves;(3)The revenue sharing contract can coordinate this type of green supply chain under manufacturers risk-averse.


2012 ◽  
Vol 452-453 ◽  
pp. 282-288 ◽  
Author(s):  
Yan Huo

This paper developed a three-level supply chain network equilibrium model with multi products and multicriteria based on corporate social responsibility through integrating the maximization of economic benefits, the maximization of social utility and the minimization of environment pollution under revenue-sharing contract. We analysed competitive behaviour of manufactures and retailers in a no cooperative competitive and described the multicriteria decision-making behaviour using Nash equilibrium theory and the weighted value function. Using product utility functions of brand differentiation and consumer preferences from product price, transaction cost and corporation social responsibility to analyse product choice in a market, and we developed the optimization conditions of each tier and whole network by variational inequality method. At last we illustrated the model with several numerical examples.


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