Purpose – The 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/approach – The 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.
Findings – The 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/implications – The 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/value – The 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.