Balanced Scorecard and Strategic Map Applied to Portfolio Management
Bibliographic record
Abstract
Abstract In June 2014, the oil price began an unpredictable steep decline after more than three years of being over the barrier of the 100 USD/Barrel (Brent). By the end of that year, the commodity lost 43% of its market value, reaching at the beginning of 2016 a minimum value of 27.88 USD/Barrel (Brent). The purpose of this paper is not to explain the forces that determine the price of the crude oil, but the question is: How many companies were prepared in June 2014 to face a 75% reduction in the value of their projects? How many of them could be defined as resilient organizations, with the ability to "survive adversity and to thrive in a world of uncertainty?"1 There is a valuable bibliography related to Reservoir Management, defined as the process that "optimizes the interaction between data and decision making during the life cycle of a field".2 Nevertheless, this position assumes that the top management has already decided the group of projects to carry out. The first pillar is missing: how to build a portfolio that enables executives, given a specific scenario, to select the proper combination of projects and to truly execute the optimal strategy for the company. While Reservoir Management works on each project separately, Portfolio Management brings the systemic approach in "understanding and exploiting the interplay among both existing and potential projects"3 This paper put forward the Balanced Scorecard and the Strategic Map applied to Portfolio Management as the nexus between Portfolio and Reservoir Management, and as the tools that enable executives to select the best strategy to cushion market volatility and translate it into actions.
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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 | 0.009 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.014 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".