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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.

A review of the academic literature on managerial promotions reveals that there has been a limited number of studies conducted on this subject. This study aims to identify key determinants used by managers in making managerial promotion decisions via Bayesian networks. It also seeks to explore the effects these determinants have on managerial promotion outcomes.

The researchers surveyed MBA students with significant work experience to assess the effect levels for 13 managerial promotion factors derived from a research study by Service and Lockamy. The participants were asked to assign a percentage effect level to these factors. Factor analysis was used to determine the most influential factors, and Bayesian networks were constructed to determine the probability of receiving a promotion based on these factors.

The results indicate that there are five key determinants which have the most influence on managerial promotions. They also indicate that managerial promotion outcomes were not significantly influenced by either the promoting manager's years of work experience, or the number of promotions witnessed.

The paper focuses on managerial and professional career advancement research, managerial promotion processes, and personnel development.

This study examines the performance consequences of aligning the competitive strategy of quality differentiation with each of the four fundamental industry life cycle stages: introduction, growth, maturity, and decline. Reports results from a questionnaire survey of CEOs in a mid‐western state in the USA. The findings suggest that small manufacturing firms can achieve a sustainable competitive advantage vis‐à‐vis quality differentiation across three stages of industry evolution: introduction, growth, and maturity.

Since its inception, cost accounting has provided data to managers for the development of internal organizational performance measures. In the mid 1980s, Dr Eli Goldratt introduced a new management philosophy called the theory of constraints (TOC). This philosophy contained a new set of performance measures which linked together the strategic objectives and operational capabilities of the organization. This linkage allows for the maximization of profits. Since its introduction, there has been a growing amount of evidence documenting TOC’s ability to more tightly link local decisions to organizational performance than those of traditional cost accounting. This research used a simple Gedunken experiment to evaluate the difference between strategy driven product‐mix decisions based on TOC accounting and traditional cost accounting. In all cases, the constraint‐based approach to costing outperformed the traditional approach based on cost accounting.

Over the past decade, increased logistics costs, improved packaging technology, and enhanced environmental regulations have caused logistics managers to reevaluate their packaging decisions. The impact of packaging decisions on logistics costs illustrates the need for strategic thinking in the assessment of packaging options. In addition, the combination of more demanding technological and environmental requirements by customers and governmental legislation suggests that packaging decisions must be viewed strategically within the logistics planning process. This paper examines the relationship between strategic packaging elements and the competitive edges on which firms can compete in the marketplace. The effects of packaging associated costs, advances in packaging technology, and the environmental movement are explored to highlight their strategic impact. Finally, a conceptual framework for assessing strategic packaging decisions in relation to a firm's competitive edges is presented.

The purpose of this paper is to examine the use of V‐A‐T analysis in the management of supply chain networks.

The paper provides a conceptual overview of V‐A‐T analysis as a procedure for categorizing manufacturing facilities, and explores the use of V‐A‐T analysis as a technique for the management of supply chain networks.

There are several challenges to the effective management of supply chain networks. However, organizations can overcome these challenges by understanding the nature of network control points as revealed through the application of V‐A‐T analysis on their supply chains.

The paper is conceptual in nature and must be validated through empirical research studies.

The concepts presented can be used by supply chain professionals to increase the likelihood of effective supply chain management within their organizations, and by supply chain researchers to further explore the use of V‐A‐T analysis as a tool for examining supply chain networks.

The paper makes a start in filling a void in the literature concerning how V‐A‐T analysis can be used as a tool to facilitate improved supply chain management.

The purpose of this paper is to provide a conceptual framework along with underlying propositions for the design and deployment of telemedicine projects which provide healthcare organizations with strategic benefits.

Field research conducted at four healthcare organizations along with academic literature in the areas of telemedicine and process management form the basis for the conceptual framework and propositions provided in this paper.

Telemedicine can be used as a process enabler for enhanced healthcare‐delivery systems. However, there are several challenges which must be considered prior to its implementation. The framework and propositions provided in the paper can be used to facilitate successful telemedicine project deployments.

The framework and propositions are derived from a small sample and must be validated through more rigorous empirical research studies.

The concepts presented in the paper can be used by healthcare planners to increase the likelihood of telemedicine deployment success within their organizations.

This paper begins to fill a void in the literature concerning how telemedicine can be used as a process enabler for improving healthcare‐delivery systems.

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.

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.

Since the 1980s, firms have searched for better ways to align their cost management systems with the realities of their business environments. The advent of accelerated global competitiveness, reduced product life cycles, rapid technological advancements, and inter‐organizational supply chains have drastically increased the need for more effective approaches to cost management. In recent years, activity‐based, constraint‐based, and hybrid cost management systems have been adopted by some firms to dampen the effects of their traditional (full‐absorption) costing methodologies. However, a holistic approach is needed to facilitate strategic cost management based upon organizational objectives, organizational needs and capabilities, and customer requirements. The Theory of Constraints provides the foundation for developing cost management systems that are global, integrative, and strategic in nature. A framework is presented in this paper for using a constraint‐based approach to strategic cost management.