Bullwhip effect in the oil and gas supply chain: A multiple-case study
Bibliographic record
Abstract
The bullwhip effect has been extensively studied in the retail, wholesale and manufacturing industries. However, it has been rarely explored in the context of resource extraction industries such as oil and gas, despite their economic impact and distinct features. This paper investigates the factors that impact the bullwhip effect in the oil and gas supply chain using case study evidence from six companies in North America, which cover refining and marketing, exploration and production, integrated oil and gas, and drilling. For each type of company studied, the operational causes of the bullwhip effect proposed in the literature and other factors of influence are examined. The findings indicate that the existing theories of the bullwhip effect have limitations in explaining the phenomenon in the oil and gas industry. Information sharing, a widely advocated countermeasure of the bullwhip effect may not be relevant in the integrated oil and gas company. Regarding the factors that drive or mitigate the bullwhip effect in different types of companies in the oil and gas supply chain, seven propositions are developed and several additional findings are obtained. All of these results enable better understandings of the bullwhip effect in academia, oil and gas organisations and related industries, and may provide guidance for potential countermeasures in practice.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.002 |
| 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".