Measuring the performance of retailers during the COVID-19 pandemic: Embedding optimal control theory principles in a dynamic data envelopment analysis approach
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Traditional retailers (bricks-and-mortar) have been continuously increasing online sales. However, not all retail companies were able to respond to the increasing sales with the same efficiency level as their competitors. This paper aims to propose a dynamic model – incorporating principles of Optimal Control Theory (OCT) into a Data Envelopment Analysis (DEA) model - for measuring the performance of retailing companies’ cost efficiency. It also aims to contribute through the application by investigating the impact of the pandemic on companies from the most prominent developing market in Latin America, Brazil. Twenty-one companies publicly traded in the São Paulo Stock Exchanges (B3) between the third quarter of 2018 (3Q2018) and the third quarter of 2020 (3Q2020) were investigated. Also, six measures - initial inventory cost (IIC), final inventory cost (FIC), net operating income (NOI), cost of goods sold (COGS), cost of the purchased product (CPP), and plant, property, and equipment (PPE) – were considered. In this way, the findings have implications for researchers and practitioners. Practitioners can discover which competitor(s) is (are) adopting the best practices at each operational aspect (e.g., inventory cost). Additionally, the proposed method can be replicated in other markets (developing or not) and for other categories of retailing companies (e.g., small- and middle-sized). Further research directions are presented, and their implications are discussed.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.052 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.012 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it