Towards a New Continuous Improvement Organization Based on Simulation
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
The paper proposes a mathematical model to forecast the time needed during a continuous improvement (CI) project. To do this, the authors adapted the famous Lotka–Volterra model to the CI methodology. Also, the authors examined a Montreal company’s database to simulate the CI’s necessary time in the real world. Firstly, the model simulates a total of CI hours for the employees and the respective quantity of problems at the end of the project. And after, the authors compared the simulation results with the project real results. This work introduces a new model to forecast the CI’s necessary time and discuss an innovative way to implement a CI project. This work also considers the advantages and difficulties of achieving a CI project based on a simulation. One of the work’s main result is the discussion of the total CI hours performed by the employees: 130% of the simulation proposition. This quantity of hours leads to half of the problems estimated by the model: 42%. The normalized results showed a difference of 1.03% between the simulation and the observations in the real world.
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 | 0.001 | 0.001 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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