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Study of the insurance contract based on supply chain members attitude towards risk

  • School of Management, University of Science and Technology of China, Hefei 230026, China

Cite this:
https://doi.org/10.3969/j.issn.0253-2778.2016.11.011
  • Received Date: 04 December 2015
  • Accepted Date: 10 April 2016
  • Rev Recd Date: 10 April 2016
  • Publish Date: 30 November 2016
    Abstract

  • Abstract

    Considering that the traditional supply chains wholesale price mechanism cant coordinate the supply chain, which leads to poor profits for both the supplier and retailer, from the perspective of supply chain members attitude towards risk, a supply chain contract was proposed which combines the insurance mechanism. The contract is based on the wholesale price mechanism model, introducing two characteristic parameters (insurance price r and the retailer bear the risk of loss proportion θ) to characterize the uncertainty of market demand. Our study has shown that the introduction of the contract can coordinate the supply chain and improve the overall profits. In addition, the effects of agents risk preferences and negotiating powers on the distribution of supply chains excessive profits were also investigated.

    Abstract

    Considering that the traditional supply chains wholesale price mechanism cant coordinate the supply chain, which leads to poor profits for both the supplier and retailer, from the perspective of supply chain members attitude towards risk, a supply chain contract was proposed which combines the insurance mechanism. The contract is based on the wholesale price mechanism model, introducing two characteristic parameters (insurance price r and the retailer bear the risk of loss proportion θ) to characterize the uncertainty of market demand. Our study has shown that the introduction of the contract can coordinate the supply chain and improve the overall profits. In addition, the effects of agents risk preferences and negotiating powers on the distribution of supply chains excessive profits were also investigated.
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    • References(10)
  • [1]
    KLPPELBERG C, MIKOSCH T. Large deviations of heavy-tailed random sums with applications in insurance and finance[J]. J Appl Prob, 1997, 34(2): 293-308.
    [2]
    CHEN Y, ZHANG W. Large deviations for random sums of negatively dependent random variables with consistently varying tails[J]. Stat Prob Lett, 2007, 77(5): 530-538.
    [3]
    CHEN Y, YUEN K C, NG K W. Precise large deviations of random sums in presence of negatively dependence and consistent variation[J]. Methodol Comput Appl Probab, 2011, 13(4): 821-833.
    [4]
    LIU L. Precise large deviations for dependent random variables with heavy tails[J]. Stat Prob Lett, 2009, 79(9): 1 290-1 298.
    [5]
    ALEKEVICIENE A, LEIPUS R, IAULYS J. Tail behavior of random sums under consistent variation with application to the compound renewal risk model[J]. Extremes, 2008, 11: 261-279.
    [6]
    YANG Y, LEIPUS R, IAULYS J. Precise large deviations for compound random sums in the presence of dependence structures[J]. Comp Math Appl, 2012, 64(6): 2 074-2 083.
    [7]
    CHEN Y, YUEN K C. Precise large deviations of aggregate claims in a size-dependent renewal risk model[J]. Insurance: Mathematics and Economics, 2012, 51(2): 457-461.
    [8]
    BI X, ZHANG S. Precise large deviations of aggregate claims in a risk model with regression-type size-dependence[J]. Stat Prob Lett, 2013, 83(10): 2 248-2 255.
    [9]
    ANSCOMBE F J. Large sample theory of sequential estimation[J]. Proc Camb Phil Soc, 1952, 48: 600-607.
    [10]
    WANG K, YANG Y, LIN Q. Precise large deviations for widely orthant dependent random variables with dominatedly varying tails[J]. Front Math China, 2012, 7(5): 919-932.
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    [1]
    KLPPELBERG C, MIKOSCH T. Large deviations of heavy-tailed random sums with applications in insurance and finance[J]. J Appl Prob, 1997, 34(2): 293-308.
    [2]
    CHEN Y, ZHANG W. Large deviations for random sums of negatively dependent random variables with consistently varying tails[J]. Stat Prob Lett, 2007, 77(5): 530-538.
    [3]
    CHEN Y, YUEN K C, NG K W. Precise large deviations of random sums in presence of negatively dependence and consistent variation[J]. Methodol Comput Appl Probab, 2011, 13(4): 821-833.
    [4]
    LIU L. Precise large deviations for dependent random variables with heavy tails[J]. Stat Prob Lett, 2009, 79(9): 1 290-1 298.
    [5]
    ALEKEVICIENE A, LEIPUS R, IAULYS J. Tail behavior of random sums under consistent variation with application to the compound renewal risk model[J]. Extremes, 2008, 11: 261-279.
    [6]
    YANG Y, LEIPUS R, IAULYS J. Precise large deviations for compound random sums in the presence of dependence structures[J]. Comp Math Appl, 2012, 64(6): 2 074-2 083.
    [7]
    CHEN Y, YUEN K C. Precise large deviations of aggregate claims in a size-dependent renewal risk model[J]. Insurance: Mathematics and Economics, 2012, 51(2): 457-461.
    [8]
    BI X, ZHANG S. Precise large deviations of aggregate claims in a risk model with regression-type size-dependence[J]. Stat Prob Lett, 2013, 83(10): 2 248-2 255.
    [9]
    ANSCOMBE F J. Large sample theory of sequential estimation[J]. Proc Camb Phil Soc, 1952, 48: 600-607.
    [10]
    WANG K, YANG Y, LIN Q. Precise large deviations for widely orthant dependent random variables with dominatedly varying tails[J]. Front Math China, 2012, 7(5): 919-932.
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