Last Mile Delivery Collaboration Proposal to Achieve Delivery Cost Efficiency in E-Commerce
Bibliographic record
Abstract
The E-commerce market in Indonesia has grown significantly in the last couple of years and proved by the emerging of key players in the e-commerce field. This phenomenon affects the improvement of the logistic sector which is one of the backbones of E-Commerce. According to the data, logistic sectors contribute 24% to Indonesia’s gross domestic product (GDP) and 25% to logistic business income which is acquired from delivery services of E-Commerce goods (PwC, 2019). On the other hand, Indonesia is an archipelago country consisting of 17.500 islands with a width of 1.905-million kilometers square. With the geographical condition of Indonesia, it becomes a challenge for E-Commerce logistic industry practitioners. To overcome these problems, corporations should take actions such as system improvement and also collaboration to decrease distribution facility establishment cost, transportation facility establishment, cost-saving seen from distribution distances, total manpower, and lastly is improvement and maintenance system in each logistic corporation. To ensure success in collaboration, corporations should show commitment to the customers by giving the best and cheapest services in order to guarantee the success of the collaboration. Other things which can be done to achieve the success are by showing collaboration commitment to other E-Commerce logistic service providers by showing the willingness to share information and data about each other and lastly, the existence of collaborator whose or which responsible for connecting both parties, whether it is third party or information collaborating system.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.031 | 0.008 |
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".