DESSAC: a decision support system for quantifying and analysing agility
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
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.003 |
| 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 | 0.001 |
| 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