Classification Of Supply Chain Disruptions Using The Random Forest Method In The Logistics Industry Of UD Suwara Jaya

Supply chain disruptions are one of the factors that can hinder the smoothness of logistics activities, especially in companies that need to maintain distribution accuracy and daily operational stability. Usaha Dagang Suwara Jaya, as a business engaged in chicken distribution, faces operational conditions that may potentially experience disruptions, such as discrepancies between pickup quantities and order quantities, remaining stock, shrinkage, and other operational adjustments. This study aims to develop a classification model capable of categorizing daily operational status into two classes: Disruption and Non-Disruption using the Random Forest algorithm. The disruption labels in this study were obtained based on validation from Usaha Dagang Suwara Jaya as the research partner and domain expert. The research stages include operational data collection, data preprocessing, operational variable formation, training and testing data splitting using 80:20 and 70:30 scenarios, Random Forest model training, model performance evaluation, Feature Importance analysis, and Streamlit dashboard implementation. Model evaluation was conducted using a confusion matrix with accuracy, precision, recall, and F1-score metrics. The results show that the Random Forest model with the 80:20 data split scenario achieved the best performance, with an accuracy of 0.9070, precision of 0.8947, recall of 0.8947, and F1-score of 0.8947. Meanwhile, the 70:30 scenario obtained an accuracy of 0.8462, precision of 0.8800, recall of 0.7586, and F1-score of 0.8148. Based on the Feature Importance results, Remaining Stock, Shrinkage, and average_temperature were the most contributing features to the classification results. This study also produced a Streamlit-based dashboard that can be used to display classification results, prediction probabilities, operational data calculations, new dataset uploads, and prediction history. The developed model and dashboard can be used as a supporting medium for monitoring daily supply chain disruptions at UD Suwara Jaya.

Keywords: classification, feature importance, logistics industry, random forest, supply chain disruptions