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A Study of Risk Assessment and Avoidance Strategies in Supplier Introduction

Published: 01 June 2024 Publication History

Abstract

We explore the optimal decision-making of a repurchase contract coordinated supply chain under stochastic price conditions with asymmetric cost-of-sale information and risk aversion of suppliers. A new repurchase contract model is constructed on the basis of the premise assumptions, solved and verified by simulation with examples, and the effects of information asymmetry and risk aversion on the relevant decision variables of the supply chain are considered after the coupling of information asymmetry and risk aversion. The results of the study show that: under the condition of random price, no matter whether the information is symmetric or not, as long as the suppliers are risk-averse, the decision variables related to the supply chain undergo bifurcation; no matter whether the market price is random or not, and no matter whether the suppliers are risk-averse or not, as long as the retailers conceal the information of private cost of goods sold, it will bring extra benefits to themselves, but it will bring damages to the suppliers and the entire supply chain; the The more asymmetric the information, the greater the oscillation of the relevant decision variables in the bifurcation mutation region. The bifurcation mutation phenomenon is a unique phenomenon after the coupling of market price stochasticity and supplier risk aversion; retailers are able to take advantage of the information asymmetry to bring additional benefits to themselves, but it will damage the interests of suppliers and the supply chain; the best countermeasure for suppliers to guard against this kind of detrimental self-interested behavior of retailers is to prompt retailers to make the cost of goods sold openly available in the least costly way by designing a cooperative mechanism; In addition, suppliers are better able to improve their own decision-making by responding to external risks with a smooth mindset.

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ICBAR '23: Proceedings of the 2023 3rd International Conference on Big Data, Artificial Intelligence and Risk Management
November 2023
1156 pages
ISBN:9798400716478
DOI:10.1145/3656766
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Published: 01 June 2024

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