Comparative Analysis of Supervised Learning Algorithms for Delivery Status Prediction in Big Data Supply Chain Management
Bibliographic record
Abstract
This study addresses the problem of predicting delivery status in supply chain data, a critical task for optimizing logistics and operations. The dataset, which includes multiple features like order details, product specifications, and customer information, was pre-processed using oversampling to address class imbalance, ensuring that the model could handle rare cases of late or canceled deliveries. The data cleaning process involved handling missing values, removing irrelevant columns, and transforming categorical variables into numerical formats. After pre-processing and cleaning, five machine learning models were applied: Logistic Regression, Random Forest, SVM, K-Nearest Neighbors (KNN), and XGBoost. Each model was evaluated using metrics such as accuracy, precision, recall, and F1-score. The results showed that XGBoost outperformed the other models, achieving the highest accuracy and providing the most reliable predictions for the delivery status. This makes XGBoost the best choice for supply chain data analysis in this context. This study contributes to the growing application of machine learning in supply chain optimization by identifying XGBoost as a robust model for delivery status prediction in large datasets. For future research, exploring hybrid models and advanced feature engineering techniques could further improve prediction accuracy and address additional challenges in supply chain optimization, especially in the context of real-time data processing and dynamic supply chain environments.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".