Study On Dynamic Feedback Model For Partner Selection In Agile Supply Chains

Today's more dynamic business environment increases the need for greater agility in supply chains, which increases both the importance and frequency of partner selection decision-making. Agile supply chains (ASCs) need to be highly flexible in order to reconfigure quickly in response to changes in their environment. The use of ASCs has become more common in today's increasingly dynamic markets. However, partner selection decisions are inherently more complex and difficult under the conditions of uncertainty and ambiguity created as supply chains form and re-form. This study presents a four-phase conceptual model for supply partner selection in ASCs, which comprise Partner selection preparation, Pre-classification, Final selection and Application feedback. It draws on a range of quantitative and qualitative techniques. Building on existing literature the model draws on both quantitative and qualitative techniques in its four phases to present a comprehensive and systematic approach to this increasingly important task. These include application of 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 the principles of continuous improvement. The resulting model offers a comprehensive and systematic approach to tackling this increasingly important task.During the Partner selection preparation phase, previous research has suggested that the application of the Dempster-Shafer and optimisation theories offers a way of solving this problem under conditions of resource constraints. This study advances this approach by offering a simplified yet thorough, rigorous yet still practical method for formulating criteria to use in partner selection decision-making in agile supply chains.During the Pre-classification phase, this study develops a model that helps overcome the information processing difficulties inherent in screening a large number of potential suppliers in the early stages of the selection process. Based on RBF-ANN, the model enables potential suppliers to be assessed against multiple criteria using both quantitative and qualitative measures.During the Final selection phase, this study proposes a two-stage approach, based on the application of ANP-MIMOP model, to solve the final selection problem of partner selection in ASCs. A key requirement of an ASC is that its constituents (suppliers, producers, distributors, etc.) can combine and react to fast changing customer demand as efficiently and effectively as possible. An ASC needs to adopt the most appropriate supply chain structure and assign the most suitable order quantities to the most appropriate supply partners in any given circumstance. In the first stage, an ANP methodology is applied to calculate the priorities of different criteria for partner selection. Secondly, using these priorities, a MIMOP method is used to determine the supply chain structure and optimize the allocation of order quantities.During the Application feedback phase, this study presents and tests a model designed to provide feedback and continuous improvement during the process of partner selection process in ASCs. 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 the most appropriate suppliers are selected at all times.Empirical illustrative examples are used to demonstrate the models and approaches, obtain insights into its application. 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.