Abstract

The Supply chain can describe the activities that are involved in the chain, or the companies, or the different functions. In literature there are a lot of models describing the Supply chain from different perspectives. Currently supply chains performance measurement systems suffer from being too inward looking, ignoring external environmental factors that might affect the overall supply chain performance when setting new targets. The most efficient Supply chain is the one that has the lowest possible cost and at the same time meet the customer’s expectations on service like delivery precision and lead-time. In this study decision based technique C4.5 is improved using correlation coefficient of Kendall for effective classification. The correlation coefficient of Kendall is adapted to improve the system. The C4.5 not only produce discrete attributes, but also continuous ones can be handled, handling incomplete training data with missing values and it is prune during the construction of trees to avoid over-fitting. The accuracy is calculated by sensitivity and specificity for the proposed and existing technique for the textile synthesis dataset. Obtained results will prove the efficiency of this proposed technique based on its accuracy.

Keywords:

Decision trees , fuzzy classification , Kendall, supply chain,

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