The concept of process maturity proposes that a process has a lifecycle that is assessed by the extent to which the process is explicitly defined, managed, measured and controlled. A maturity model assumes that progress towards goal achievement comes in stages. The supply chain maturity model presented in this paper is based on concepts developed by researchers over the past two decades. The Software Engineering Institute has also applied the concept of process maturity to the software development process in the form of the capability maturity model. This paper examines the relationship between supply chain management process maturity and performance, and provides a supply chain management process maturity model for enhanced supply chain performance.

As supply chains continue to replace individual firms as the economic engine for creating value during the twenty-first century, understanding the relationship between supply-chain management practices and supply chain performance becomes increasingly important. The Supply-Chain Operations Reference (SCOR) model developed by the Supply Chain Council provides a framework for characterizing supply-chain management practices and processes that result in best-in-class performance. However, which of these practices have the most influence on supply chain performance? This exploratory study investigates the relationship between supply-chain management planning practices and supply chain performance based on the four decision areas provided in SCOR Model Version 4.0 (PLAN, SOURCE, MAKE, DELIVER) and nine key supply-chain management planning practices derived from supply-chain management experts and practitioners. The results show that planning processes are important in all SCOR supply chain planning decision areas. Collaboration was found to be most important in the Plan, Source and Make planning decision areas, while teaming was most important in supporting the Plan and Source planning decision areas. Process measures, process credibility, process integration, and information technology were found to be most critical in supporting the Deliver planning decision area. Using these results, the study discusses the implications of the findings and suggests several avenues for future research.

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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.

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Purpose -The purpose of this paper is to provide a methodology for benchmarking supplier risks through the creation of Bayesian networks. The networks are used to determine a supplier's external, operational, and network risk probability to assess its potential impact on the buyer organization. Design/methodology/approach -The research methodology includes the use of a risk assessment model, surveys, data collection from internal and external sources, and the creation of Bayesian networks used to create risk profiles for the study participants. Findings -It is found that Bayesian networks can be used as an effective benchmarking tool to assist managers in making decisions regarding current and prospective suppliers based upon their potential impact on the buyer organization, as illustrated through their associated risk profiles.Research limitations/implications -A potential limitation to the use of the methodology presented in the study is the ability to acquire the necessary data from current and potential suppliers needed to construct the Bayesian networks. Practical implications -The methodology presented in this paper can be used by buyer organizations to benchmark supplier risks in supply chain networks, which may lead to adjustments to existing risk management strategies, policies, and tactics. Originality/value -This paper provides practitioners with an additional tool for benchmarking supplier risks. Additionally, it provides the foundation for future research studies in the use of Bayesian networks for the examination of supplier risks.

If you would like to write for this, or any other Emerald publication, then please use our Emerald for Authors service information about how to choose which publication to write for and submission guidelines are available for all. Please visit www.emeraldinsight.com/authors for more information. About Emerald www.emeraldinsight.comEmerald is a global publisher linking research and practice to the benefit of society. The company manages a portfolio of more than 290 journals and over 2,350 books and book series volumes, as well as providing an extensive range of online products and additional customer resources and services.Emerald is both COUNTER 4 and TRANSFER compliant. The organization is a partner of the Committee on Publication Ethics (COPE) and also works with Portico and the LOCKSS initiative for digital archive preservation. AbstractPurpose -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. Design/methodology/approach -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. Findings -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. Research limitations/implications -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. Practical implications -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. Originality/value -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.

This article examines the use of target costing as a means to improve the management of supply chains. A discussion of the shortcomings of traditional and activity‐based cost management approaches to supply chain management provides the basis for exploring the use of target costing within supply chains. Customer requirements and supply chain relationships are identified as key criteria for selecting the most appropriate method of target costing for supply chains. Price‐based, value‐based, and activity‐based cost management approaches to target costing are discussed, and recommendations for their use based upon customer requirements and supply chain relationships are offered. Conclusions are provided on the use of target costing to enhance a supply chain’s ability to improve customer satisfaction.