Multi-Criteria Decision Making in Production Fields: A Structured Content Analysis and Implications for Practice
Bibliographic record
Abstract
As the complexity of decision-making problems and the competitiveness in which companies find themselves carrying out their activities increase, the need to use tools that can help Decision-Makers (DM) make more informed and more effective choices increases. Multi-Criteria Decision Making (MCDM) represents a valid decision support tool capable of simplifying the process of choosing, ranking or sorting the alternatives that characterize the problem. This work aims to investigate with a structured content analysis if MCDMs are used in an extremely complex and competitive sector such as the automotive sector. The work also aims to describe and explore in the existing literature the role that entrepreneurs (our decision-makers) play in the construction of MCDM methods. The results show that MCDMs are widely used in different application areas in the domain of interest and that the decision maker is involved in several phases of construction of the MCDM methods.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".