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PurposeThe purpose of this paper is to present a four‐phase dynamic feedback model for supply partner selection in agile supply chains (ASCs). ASCs are commonly used as a response to increasingly dynamic markets. However, partner selection in ASCs is inherently more complex and difficult under conditions of uncertainty and ambiguity as supply chains form and re‐form.Design/methodology/approachThe model draws on both quantitative and qualitative techniques, including the Dempster‐Shafer and optimisation theories, radial basis function artificial neural networks (RBF‐ANN), analytic network process‐mixed integer multi‐objective programming (ANP‐MIMOP), Kraljic's supplier classification matrix and principles of continuous improvement. It incorporates modern computer programming techniques to overcome the information processing difficulties inherent in selecting from amongst large numbers of potential suppliers against multiple criteria in conditions of uncertainty.FindingsThe model enables decision makers to make efficient and effective use of the vastly increased amount of data that is available in today's information‐driven society and it offers a comprehensive, systematic and rigorous approach to a complex problem.Research limitations/implicationsThe model has two main drawbacks. First, practitioners may find it difficult to match supplier evaluation criteria with the strategic objectives for an ASC. Second, they may perceive the model to be too complex for use when speed is of the essence.Originality/valueThe main contribution of this paper is that, for the first time, it draws together work from previous articles that have described each of the four stages of the model in detail to present a comprehensive overview of the model.

AbstractThis paper presents and tests a model designed to provide feedback and continuous improvement during the process of supplier selection in agile supply chains (ASCs). In ASCs suppliers typically need to be changed or added to frequently in response to dynamic market conditions. The model seeks to capitalise on the increased application of the supplier section process by applying principles of continuous improvement and organisational learning. Its aim is to support organisational decision‐makers in their efforts to optimise the performance of the supply chain by ensuring that only the most appropriate suppliers are selected at all times. The model was tested by application within two simulation groups of MBA and masters level international management students who have significant business experience. The results of a questionnaire, used to assess the efficacy of the model, showed that the participants found the model was likely to have significant benefits when used in practice. Copyright © 2009 John Wiley & Sons, Ltd.

The triple helix of university–industry–government relations has been accepted as a significant determiner of innovation. For the economic progress of the industrialised nations, the cooperation between academia and industry is terribly crucial. For the purpose of profit maximisation by means of such cooperation, achieving a healthy and productive cooperation is of vital importance. Science and technology (S&T) cooperation platform is an original cooperative mode in East China, which is similar to the technology transfer office. This article researches the cooperation relation and makes a thorough inquiry of the influence of cooperation on high-tech firms. Based on an investigation of 552 high-tech firms and 56 universities, we found that the high-tech firms can improve economic situation via the cooperation with universities on the platform. Moreover, principal discoveries demonstrate that the economic performance of high-tech firms is positively related with the co-operation projects. Furthermore, with a view to making policies and raising probable improvements to cooperative efficiency between academia and industry, this study also finds that high-tech firms selecting distinct cooperation models on the platform will bring different effects on the economic performance of firms.