B2C E-commerce for Home Appliance in The Brazilian Market: A Cost Efficiency Investigation through a DEA-OCT Model
Bibliographic record
Abstract
Objective: to investigate the cost efficiency of inventory management for publicly home appliance retail companies in the Brazilian market that operate, among other channels, with B2C (Business-to-Costumer) and discuss the possible impacts of e-commerce on inventory management. Method: the first step, construction of a descriptive summary of Magazine Luiza and seven other competitors; the second step, analysis of the optimal cost efficiency of companies' inventory management, through an integrated and dynamic model of Data Envelopment Analysis (DEA) and Optimal Control Theory (OCT). Amounts declared in the Quarterly Financial Statements between the fourth quarter of 2010 and the second quarter of 2018 were considered.Main Results: the discovery of best practices and the fact that, despite its extraordinary appreciation, Magazine Luiza was not a reference in inventory management and its costs during the analyzed period as a whole.Relevance/Originality: this article is an initial step to address the lack of studies on e-commerce for emerging markets and inventory management and its costs for e-commerce.Methodological Contributions: the proposition of a methodology that can be used by other researchers to assess the cost efficiency of inventory management and the impact of e-commerce, highlighting best practices.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.005 | 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".