Business trends: Managing risk and uncertainty: The importance of optimizing your value chain
Bibliographic record
Abstract
COVID-19 is disrupting the oil and gas industry Oil prices are falling, demand is down, supply is up, and storage capacity is limited In these difficult times, the industry must swiftly act to keep business viable to come back strong when conditions improve Therefore, refineries should take this opportunity during the slowdown to future-proof their operations so that they are in a better position to survive the next downturn There is no way to predict the length of the current pandemic, but there are things operators can do to help their refineries survive these difficult times, including evaluating opportunity crudes, employing blend optimization, monitoring operations, and optimizing operations Husky Energy, a Calgary-based integrated oil and gas company, identified the need to automate business processes, including integrating and standardizing its value chain activities across the downstream business The company selected a unified supply chain management software as a starting point This software will enable Husky’s team to plan and schedule its end-to-end downstream value chain The company adopted an operating model that featured integrated optimization, increasing the total revenue and gross margin captured across the entire value chain The technology provides an enterprise cloud solution that enhances collaboration, agility, and transparency across the value chain This allows Husky to make decisions that deliver added value to its integrated business
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.
How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".