PT. Pertamina Retail: Indonesian fuel retail expansion dilemma in pandemic COVID-19
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
Learning outcomes Students are expected to integrate decision-making tools and frameworks to create decisions under uncertainty. Students are expected to understand the general business process of fuel retail industry. Case overview/synopsis PT. Pertamina Retail (PTPR) is a subsidiary of PT. Pertamina, an Indonesian state-owned oil and natural gas company. In the first quarter of 2020, PTPR’s sales volume decreased due to the COVID-19 pandemic’s large-scale social restrictions. Iin Febrian was just appointed as President Director in March 2020; he must formulate a survival strategy facing COVID-19 pandemic uncertainties. The case elaborates on PTPR’s decision to expand immediately or hold. Scenarios and expected values have been given to simplifying the calculation of a decision tree. The case also challenges students to think critically on providing a strategy to survive during the COVID-19 pandemic and beyond using decision tree analysis and BCG Matrix or Ansoff Matrix. Complexity academic level BA level and MBA program in Decision Analysis Course or Strategic Management Course. Supplementary materials Teaching notes are available for educators only. Subject code CSS 11: Strategy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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