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Information sharing is an important component of cooperation in supply chain management. However, it has long been debated whether there is value in information sharing, how large the value is, if any, and what factors affect it. The purpose of the paper lies in investigating these three issues by comparing and analyzing 12 information models in supply chains.

To achieve the above purpose, the paper first presents a general information‐sharing model in supply chains and characterizes three major factors in the model (objective, supply chain partnership constraint, information sharing constraint). Based on the general model, 12 representative models are selected from the literature and their relationship and distinctions are compared and analyzed.

By insights from comparative analysis of these representative models, the paper concludes that information sharing in supply chains is valuable. However, the value and affecting factors are dependent on analytical methods. It would be meaningless simply to compare the numerical values.

The framework developed in this paper provides a useful guidance for the practical managers in evaluating and measuring the value of information‐sharing strategies.

The paper critically reviews representative information‐sharing models in supply chains. This work is helpful in answering some questions that have been long debated in this area and in inspiring new endeavors to overcome the limitations of current research.

As organizations increase their dependence on supply chain networks, they become more susceptible to their suppliers’ disaster risk profiles, as well as other categories of risk associated with supply chains. Therefore, it is imperative that supply chain network participants are capable of assessing the disaster risks associated with their supplier base. The purpose of this paper is to assess the supplier disaster risks, which are a key element of external risk in supply chains.

The study participants are 15 automotive casting suppliers who display a significant degree of disaster risks to a major US automotive company. Bayesian networks are used as a methodology for examining the supplier disaster risk profiles for these participants.

The results of this study show that Bayesian networks can be effectively used to assist managers in making decisions regarding current and prospective suppliers vis-à-vis their potential revenue impact as illustrated through their corresponding disaster risk profiles.

A limitation to the use of Bayesian networks for modeling disaster risk profiles is the proper identification of risk events and risk categories that can impact a supply chain.

The methodology used in this study can be adopted by managers to assist them in making decisions regarding current or prospective suppliers vis-à-vis their corresponding disaster risk profiles.

As part of a comprehensive supplier risk management program, organizations along with their suppliers can develop specific strategies and tactics to minimize the effects of supply chain disaster risk events.

The global electronic equipment industry has evolved into one of the most innovative technology-based business sectors to transpire in the last three decades. Much of its success has been attributed to effective supply chain management. The purpose of this paper is to provide an examination of external risk factors associated with the industry’s key suppliers through the creation of Bayesian networks which can be used to benchmark external risks among these suppliers.

The study sample consists of the suppliers to seven of the leading global electronic equipment companies. Bayesian networks are used as a methodology for examining the supplier external risk profiles of the study sample.

The results of this study show that Bayesian networks can be effectively used to assist managers in making decisions regarding current and prospective suppliers with respect to their potential impact on supply chains as illustrated through their corresponding external risk profiles.

A limitation to the use of Bayesian networks for modeling external risk profiles is the proper identification of risk events and risk categories that can impact a supply chain.

The methodology used in this study can be adopted by managers to assist them in making decisions regarding current or prospective suppliers vis-à-vis their corresponding external risk profiles.

As part of a comprehensive supplier risk management program, companies along with their suppliers can develop specific strategies and tactics to minimize the effects of supply chain external risk events.

To counteract the effects of global competition, many organizations have extended their enterprises by forming supply chain networks. However, as organizations increase their dependence on these networks, they become more vulnerable to their suppliers' risk profiles. The purpose of this paper is to present a methodology for modeling and evaluating risk profiles in supply chains via Bayesian networks.

Empirical data from 15 casting suppliers to a major US automotive company are analyzed using Bayesian networks. The networks provide a methodological approach for determining a supplier's external, operational, and network risk probability, and the potential revenue impact a supplier can have on the company.

Bayesian networks can be used to develop supplier risk profiles to determine the risk exposure of a company's revenue stream. The supplier risk profiles can be used to determine those risk events which have the largest potential impact on an organization's revenues, and the highest probability of occurrence.

A limitation to the use of Bayesian networks to model supply chain risks is the proper identification of risk events and risk categories that can impact a supply chain.

The methodology used in this study can be adopted by managers to formulate supply chain risk management strategies and tactics which mitigate overall supply chain risks.

The methodology used in this study can be used by organizations to reduce supply chain risks which yield numerous societal benefits.

As part of a comprehensive supplier risk management program, organizations along with their suppliers can develop targeted approaches to minimize the occurrence of supply chain risk events.