The Use and Impact of Manufacturing Productivity Improvement Tools and Methodologies within the Automotive Component Industry
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 presents the results of a study which was undertaken with the overarching objective of investigating the use and impact of manufacturing productivity improvement approaches within automotive component suppliers. It has the specific aims of identifying (i) the level of understanding and use of continuous improvement tools and management methodologies within organisations and (ii) the factors which could contribute to them failing to achieve the results expected. The results of a survey of 161 automotive suppliers are presented. The survey investigated all elements of selection, training and implementation of tools and methodologies. This paper highlights that the adoption of continuous improvement tools and methodologies is widespread and discusses the relatively high failure rates. The reasons which may contribute to failure are presented and discussed, with the major findings being a lack of suitable resources, a lack of understanding and training within senior personnel and a non-strategic approach to the application of tools and methodologies.
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 | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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