Refining the ISO 9126 Model for Enhanced Decision Support System Evaluation in the Manufacturing Industry
Bibliographic record
Abstract
In the increasingly competitive landscape of the manufacturing industry, leveraging advancements in information technology has become pivotal for gaining a competitive edge.Specifically, the furniture manufacturing industry has adopted decision support systems (DSS) to meet diverse information needs.However, user concerns regarding these systems, including information reporting speed, order reception, and the accuracy of raw material usage calculations, persist.The development of any system necessitates the application of quality assurance standards that align with user requirements.In light of these concerns, the measurement of software quality emerges as an essential strategy to assuage user doubts about the DSS.Employing the ISO 9126 standard as an assessment parameter provides a reliable framework for evaluating the quality of the DSS.Although the ISO 9126 model possesses several characteristics, this study focuses exclusively on two: functionality and reliability.The anticipated outcome of this research is a refined understanding of user satisfaction pertaining to the implementation of the DSS.Consequently, this study's findings may contribute to the DSS's credibility, specifically concerning the speed of reporting, order reception, and the precision of raw material usage calculation.This, in turn, can influence the decision-making process in production, thereby enhancing the overall effectiveness of the manufacturing industry.
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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 | 0.097 | 0.167 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".