PT. Pertamina Retail: Indonesian fuel retail expansion dilemma in pandemic COVID-19
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.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.029 | 0.003 |
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".