A conceptual framework to understand retailers'logistics and transport organization-illustrated for groceries'goods movements in France and Germany
Bibliographic record
Abstract
The article proposes a conceptual framework for analyzing the key drivers for retailers’ transport demand more systematically. Certainly, a better understanding of the reasons behind the growth in transport demand is crucial in formulating effective measures to manage and reduce emissions. Regarding to retailer-led supply chains, analyzing the retailers’ transport demand and its key drivers seems to be indispensable. This issue is even more important on an urban scale as retailers have a high share of urban freight transport. \nThe authors’ main assumption is that retailers’ transport strategies are dependent to non-transport-related context, both internally to the non-transport strategies and also externally to the company’s environment. A classification of the context in three layers is suggested: \n• The macro-context represents retailers’ upstream context which includes the competitive and regulatory framework of retail industry, as well as consumers’ consumption patterns. \n• The meso context refers to the retailer’s sectorial dynamic. It includes aspects such as the vertical integration of wholesale stage or new relationships with suppliers e.g. through the development of own-brand products \n• The micro context is related to the strategy of the individual retailer and encompasses economic strategies (type of retail format, marketing area etc.). \nFurthermore, the paper illustrates the chains of interdependencies between different layers of context and their consequences for logistics and transport organization, using the example of food retailers in France and Germany. As one result, the influence of retailers’ sales related strategies on logistics and transport can be demonstrated.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".