Analyses spatiales d’un réseau de distribution de points de vente : application à une entreprise canadienne de meubles distribués aux etats-unis
Bibliographic record
Abstract
In the current context of globalization, setting up and maintaining an efficient distribution network is essential for companies. Several criteria are used to judge the quality of a network. Among these, the adequacy between the location of the points of sale and the position of the customers, namely, the coverage of the market, is preponderant. Data mining techniques make it possible to segment a market based on the socioeconomic characteristics of customers in different geographic areas. In this article, we propose to set up spatial analysis measures and to use a geographic information system (GIS) to qualify the coverage intensity of a company's distribution network. As part of our study, we study the distribution network of a high-end furniture company with outlets in the United States. Ultimately, this involves comparing, geographic area by geographic area, the results of customer segmentation reflecting the commercial potential with recovery indicators. This model integrating the two series of results becomes a decision support tool for the company in the deployment of its network.
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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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| 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".