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International audienceThis paper proposes an integrated framework to analyse Business-to-Business relationships using data mining techniques such as K-Means based on particle swarm intelligence (PSO) and Association Rule (AR). It constructs relationship rules of holistic performance enhancement road map. The data set of relationships between enterprise and its direct customers was used for demonstration. The results of the case study in France show, how domain managers powerfully use the graphical analysis results to provide the holistic performance improvement and weakness resolution relationship rules. In the long run, Supply Chain managers are able to use this framework to design and adjust their units to meet the customer needs. This paper introduces and explains a new idea of measuring performance along the supply chain from a collaborative perspective. The extended model is adapted from our previous model and from a Balanced Score Card (BSC) model. It provides a tool to measure tangible and intangible values between partners

The general context of this study is the parameterizing of the production system. The aim is to regulate the physical flow according to objectives, usually expressed in terms of performance indicator values. Performance control requires mastery of the consequences of the choices made. It then becomes essential to be able to articulate the potential effects of the various decisions. The existing approaches to solving this problem all imply the use of means of simulation capable of assessing the value of the performance indicators for each configuration of the action parameters. Taking this assumption as a basis, and focusing on the user, a multiple criteria approach is developed towards the understanding and schematization of the behaviour of a given system. The particularity of this approach lies in combining both data analysis and user expertise. The instrumentation approach uses tools for data analysis and assistance in decision making to achieve an original visualization of the structure of performance levels in a plan, called the plan of preferences.