ENTERPRISE RISK MANAGEMENT: THE CASE OF UNITED GRAIN GROWERS
Bibliographic record
Abstract
Enterprise risk management (ERM) refers to the identification, quantification, and management of all of a company's risks within a unified framework. This approach is much more comprehensive than traditional risk management practice, where different types of risk are managed by different people using different tools. The authors evaluate the advantages and disadvantages of ERM and then describe how United Grain Growers (UGG), a major farm service provider in Western Canada, established such an approach. Extensive risk identification and measurement indicated that the volatility of UGG's earnings was driven to a large extent by changes in the volume of its grain shipments, which in turn were principally due to variation in weather. After first considering the use of weather derivatives to hedge the risk, the company ended up purchasing an insurance contract, bundled with its traditional insurance coverage, that pays UGG if its grain volume is unexpectedly low. The potential for moral hazard that can make insurance an expensive proposition was limited by basing payoffs on industry grain shipments rather than the company's shipments. The bundled approach served to expand and integrate UGG's insurance coverage, while eliminating redundant coverage. Besides economizing on insurance costs, another valuable aspect of enterprise risk management is as a source of information about the operations of the firm. By providing managers with a better understanding of their business and events that can undermine the firm's strategic objectives, ERM can lead to better operating decisions as well as a more efficient approach to risk retention and risk transfer.
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 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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".