Research on Logistics Cost Control of E-commerce Enterprise from the Perspective of Value Chain– A Case Study of Pinduoduo
Bibliographic record
Abstract
Recently, with the rapid development of social platforms, social e-commerce enterprises are also rising. However, in the process of development, logistics cost has become a big constraint. Taking Pinduoduo as an example, this paper adopts case analysis method and literature review method to study how to help e-commerce enterprises control logistics costs from the perspective of value chain. By analyzing, this paper finds that for internal value chain of Pinduoduo, it faces problems of inadequate supervision of delivery cost, unreasonable freight rate, and high reverse logistic cost. For external value chain, Pinduoduo faces problems of low loyalty from users, high competitiveness from competitors and imperfect sinking market value chain. Aiming at these problems, this paper puts forward the following suggestions. For internal value chain, Pinduoduo should adopt JIT and ABC method, strengthen the supervision of delivery cost, and improve the efficiency of after-sale service. For external value chain, Pinduoduo needs to establish strong relationship with suppliers and customers. Most importantly, forming an infallible information system is essential.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".