Supplier Evaluation Model on SAP ERP Application using Machine Learning Algorithms

  • Authors

    • Manu Kohli
    2018-05-16
    https://doi.org/10.14419/ijet.v7i2.28.12951
  • Decision Tree, ERP, HANA, Machine learning, Naive Bias, Procurement, SAP, Supervised Learning, Supplier Evaluation, Supplier Ranking, Support Vector Machine, SVM

  • Abstract

    For business enterprises, supplier evaluation is a mission critical process. On ERP (Enterprise Resource Planning) applications such as SAP, the supplier evaluation process is performed by configuring a linear score model, however this approach has a limited success. Therefore, author in this paper has proposed a two-stage supplier evaluation model by integrating data from SAP application and ML algorithms. In the first stage, author has applied data extraction algorithm on SAP application to build a data model comprising of relevant features. In the second stage, each instance in the data model is classified, on a rank of 1 to 6, based on the supplier performance measurements such as on-time, on quality and as promised quantity features. Thereafter, author has applied various machine learning algorithms on training sample with multi-classification objective to allow algorithm to learn supplier ranking classification. Encouraging test results were observed when learning algorithms,(DT) and Support Vector Machine (SVM), were tested with more than 98 percent accuracy on test data sets. The application of supplier evaluation model proposed in the paper can therefore be generalised to any other other information management system, not only limited to SAP, that manages Procure to Pay process.

     

     

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  • How to Cite

    Kohli, M. (2018). Supplier Evaluation Model on SAP ERP Application using Machine Learning Algorithms. International Journal of Engineering & Technology, 7(2.28), 306-311. https://doi.org/10.14419/ijet.v7i2.28.12951

    Received date: 2018-05-17

    Accepted date: 2018-05-17

    Published date: 2018-05-16