IMPLEMENTING ECONOMIC CAPITAL IN AN INDUSTRIAL COMPANY: THE CASE OF MICHELIN
Bibliographic record
Abstract
Economic capital (also referred to as “risk capital” or “risk‐based capital”) is the amount of capital, generally in the form of equity or equity equivalents, that is necessary to provide an adequate cushion against lower‐than‐expected operating results. Over the last two decades, the concept has taken root among banks, particularly in determining the amount of capital needed to protect against financial distress in the event of unexpectedly large credit losses. Michelin is in the vanguard of industrial companies that are beginning to apply economic capital concepts. The company uses an option‐pricing approach that effectively allows the market to identify the level of economic capital that is expected to maximize corporate value. Michelin has also begun the process of attributing economic capital to individual business units and activities. By so doing, the company is able to use a single, company‐wide hurdle rate for all projects and business units. Thus, instead of raising the discount rate when evaluating riskier projects and businesses, management assigns them larger amounts of economic capital (and, hence, a higher charge for use of that capital). The use of economic capital to evaluate ongoing activities and contemplated investments makes it more likely that decisions will translate into increased shareholder value. A case in point is outsourcing. As illustrated in an example analyzing the company's decision to sell but continue sourcing from a textile factory, outsourcing decisions typically reduce a firm's required amount of economic capital—and thus an analysis based on the use of economic capital provides a more realistic picture of the expected value added from such transactions.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.013 | 0.009 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".