Analysis of E-Commerce Process in the Downstream Section of Supply Chain Management Based on Process and Data Mining
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Most businesses today use ecommerce stores and/or ecommerce platforms to carry out online marketing and sales activities. The rapid increase in the volume of E-commerce sales transactions normatively causes various problems that occur, especially in this case the buyer or consumer. Consumers expressed dissatisfaction in their e-commerce delivery experience. Customers often complain to sellers in the marketplace about the delay in sending the ordered package. This paper proposes a research model that is proposed in analyzing the datasets generated from the Downstream Supply Chain Management process, especially the process of selling and shipping E-Commerce goods to end customers. The mechanism used is collaborating process mining and data mining so that the resulting analysis becomes more powerful and better information is obtained compared to only analyzing separately. The results of the analysis in the case study of the E-commerce Costumer to Customer (C2C) marketplace show that process mining related to shipping goods can be explained by adding the results of data mining analysis from the datasets obtained, especially the processes in the Downstream Supply Chain Management Section.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it