DESSAC: a decision support system for quantifying and analysing agility
Bibliographic record
Abstract
This paper traces the origin and development of agile manufacturing. The industrial sectors which have embraced agility are today's winners in the competitive markets. This situation warrants the need of assessing the activities to be undertaken to acquire agility. For this purpose, this paper advocates the adoption of a 20 criteria agile model. In order to implement this model effectively, the agility level at which a company currently operates needs to be quantified. For this purpose, a quantification model incorporated with the 20 criteria agile model was adopted from literature and proposed after refinement. Applying this refined quantifying model in real time practice is a time consuming and tedious process. In order to overcome this difficulty, a decision support system named DESSAC (DEcision Support System for quantifying Agile Criteria) was developed. DESSAC was demonstrated to a group of competent personnel of an electronics switch manufacturing company situated in India. These personnel could operate DESSAC without any difficulty. Their feedback indicated its practical feasibility. In conclusion this paper points out the limitations of this research and the scope for pursuing further researches to overcome them.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".