Lot of Companies is involving in the activities of the Supply Chain that is will used for different functions like company process management and relation between the suppliers and customers agents. The decision-tree based approach is used to make learn and recognize the logical methods of a tree structure. A state-of-the-art supply chain management  gives the coding rules as well as the Logical rules features needed by the system. Each attribute is classified and tested during the layout analysis and Logical features are collected and compared to the company’s synthetic data set. The Agent-Based Modeling method is employed in the study is the Improved Chi -squared Automatic Interaction Detection" (I-CHAID method). All the user needs are tested around 50 dataset attribute contents belonging to the SCM with I-CHAID method; this will be representing the lower error rate for determining the logical labels are less than 5%. And also the efficiency like precision, recall, error rate will calculate and explains in detail functioning of I-CHAID with respect to the company’s supply chain management.
Title = "Supply Chain Enhancement Using Improved Chaid Algorithm for Classifying the Customer Groups",
Journal ="International Journal of Computer Applications Technology and Research(IJCATR)",
Volume = "6",
Pages ="349 - 409",
Year = "2017",
Authors ="C.P.Balasubramaniam , Dr.R.Gunasundari"}
Lot of Companies is involving in the activities of the Supply Chain that is will used for different functions like company process management and relation between the suppliers and customers agents
Supply Chain Management is mainly based on the two core ideas. The first idea is that practically every product that reaches an end user represents the cumulative effort of multiple organizations
Supply chain management develops coordinating and integrating the flows on both within and among companies
CART algorithm described as the three different procedural categories like growing a large tree, pruning the sequence of nested sub-trees, and ?nally selecting a best-sized tree.