Bibliographic record
Abstract
In a mature industry like the Truck industry, competition is getting harder and harder. A few strong manufactures are doing there very best to cut cost in order to gain market shares from the others within the market. To be able to generate cost Savings Company must be flexible & prepare to adapt & implement new ideas. This thesis was carried out at the International Truck & Engine Corporation Garland Assembly Plant, Texas, which employs 1000 employees. The Plant Assembles Heavy duty & Severe service Trucks. The purpose of this Research is to Investigate, Study, & analyzes the existing process of steering wheel Alignment in order to give recommendations on what actions are needed for efficiently implementing six-sigma in the organization to Improve Process. The Analysis aims to reduce/eliminate customer complaints, PTD (Prior to delivery-Dealers) warranty & 0 to 90 days warranty (Customer) costs caused by Steering Wheel Alignment claims. Six-Sigma methodologies will be utilized to identify and correct the most complex problems. This product quality innovation methodology will provide a structured, disciplined, rigorous approach to process improvement consisting of five phases (DMAIC) D&barbelow efine, M&barbeloweasure, A&barbelownalyze, I&barbelowmprove, C&barbelowontrol where each phase is linked logically to the previous & next phase.Dept. of Industrial and Manufacturing Systems Engineering. Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis2006 .H372. Source: Masters Abstracts International, Volume: 45-01, page: 0436. Thesis (M.A.Sc.)--University of Windsor (Canada), 2006.
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.
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 | 0.010 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".