Supervised machine learning approach for effective supplier classification
Krishnan, Krishna K.
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R. Harikrishnakumar, A. Dand, S. Nannapaneni and K. Krishnan, "Supervised Machine Learning Approach for Effective Supplier Classification," 2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA), Boca Raton, FL, USA, 2019, pp. 240-245
Supplier assessment plays a critical role in the supply chain management, which involves the flow of goods and services from the initial stage (raw material procurement) to the final stage (delivery). Supplier assessment is a multi-criteria decision-making (MCDM) approach that requires several criteria for the proper assessment of the suppliers. When there are several criteria involved, it makes the supplier assessment process more complicated. For a comprehensive and robust assessment process, we propose the use of supervised machine learning algorithms to classify various suppliers into four categories: excellent, good, satisfactory, and unsatisfactory. In this paper, supervised learning (classification) algorithms are applied for a supplier assessment problem where a model is trained based on the previous historical data and then tested on the new unseen data set. This method will provide an efficient way for supplier assessment that is more effective in terms of accuracy and time when compared to MCDM approach. Classification algorithms such as support vector machines (with linear, polynomial and radial basis kernels), logistic regression, k-nearest neighbors, and naïve Bayes methods are used to train the model and their performance is assessed against a test data. Finally, the performance measures from all the classification methods are used to assess the best supplier.
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