Using Multicriteria Decision Making Methods to Manage Systems Obsolescence
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Systems obsolescence may cause huge invisible internal cost through mis-judgment. It leads to many defects related to the manufacturing system and its environment. While its management is complex, composed by multiple factors and stakeholders, the current tools are still minimal and purely quantitative using cost optimization only. Considering different actors seems essential to ensure a reliable mitigation and resolution strategy. This paper aims to develop an MCDM model specific to obsolescence management by expanding decision criteria and using a non-compensatory and dynamically weighted ELECTRE III approach. The goal is to ensure a robust, sustainable and green manufacturing ecosystem. The MCDM tool was applied to the problem and performed in two case studies from the literature, using DIVIZ platform. The model results were compared to those from previous studies. They show that the decision made changes significantly affecting the manufacturing performance.
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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 | 0.001 | 0.001 |
| 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 it