ERP-Integrated Supply Chain Analysis and Risk Management: A Machine Learning Approach

Pratiksha Agarwal ()
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Pratiksha Agarwal: SAP, Senior Product Marketing Manager

A chapter in Proceedings of the 2nd International Conference on Emerging Technologies and Sustainable Business Practices-2024 (ICETSBP 2024), 2024, pp 550-561 from Springer

Abstract: Abstract Integration and optimization of corporate activities inside the always shifting framework of supply chain management depend on Enterprise Resource Planning (ERP) technologies. Although there have been notable progress, the complexity of supply chain data makes precisely predicting and risk reduction challenging even with great advances. Late delivery is one such risk. Accurate prediction of delayed delivery would help a company’s output to be much enhanced as well as the customer delight. Still, modern techniques sometimes find it difficult to understand the many linkages and patterns in the data, which reduces performance to less than ideal. This study proposes to forecast delayed delivery using the Random Forest classification model. Our approach calls for thorough data preparation, which entails activities including date conversion, date resolution of missing values, one-hot encoding for categorical variables, and MinMaxScaler application to standardize numerical features. To do complete feature selection, the study also uses feature importance from the original models and association analysis. The hyperparameters are optimized and the performance of the random forest model is improved by the grid search approach. In order to find the most appropriate tactics, the study assesses the performance of logistic regression, support vector machines, linear discriminant, and Gaussian naive Bayes among other models. With an accuracy of 99.7%, a f1-score of 99.79%, and a recall of 99.59%, the random forest model shows to be better than preceding models. With an accuracy rate of 84.98%, a recall rate of 88.06%, and an F1-score of 86.01%, the GNB model shown below-average performance.

Keywords: Enterprise Resource Planning; Supply Chain Analysis; Risk Management; Machine Learning (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:spr:advbcp:978-94-6463-544-7_36

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DOI: 10.2991/978-94-6463-544-7_36

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