Artificial intelligence towards a smart automotive supply chain performance KPIs aligned with IATF 16949 standards
Bibliographic record
Abstract
Auto accessories such as car covers provide an added extra in automotive styling both in the look and construction. Any fault in these components will reduce customer satisfaction and result in higher warranty expenses among manufacturers. Automotive sector as per IATF 16949 requirements requires a very effective and strong control of its processes to reduce the defects and enhance productivity. Thus, improved methods for defect identification and higher levels of quality assurance during production are critical issues of current concern. This research focuses on the use of Artificial intelligence (AI) in the automotive industry with an emphasis of using computer vision for superior improvement of quality KPIs. The purpose is to provide an efficient system and organizational approach to the further optimization of the end-of-line inspection of covers for vehicles, and to improve the efficiency of the identification of defects under IATF 16949 regulations. This study is unique in adopting a case based on smart splicing technology implemented in the cutting area of the automobile manufacturing lines. This paper simultaneously applies AI and IoT in order to understand its degree of influence in the definitive performance KPIs. Insignificance may be identified through the application of linear regression used to analyze the correlation between the applied technology and subsequent performance gains. Experimental outcome shows a significant decline on the number of defects that are identified at the last inspection process as well as an improvement on the rate of production. AI particularly contributed to enhancement of inspection processes thereby minimizing non-value adding activities and hence improving overall quality of the products. The current study also encourages manufacturers to adopt intelligent technologies since the AI technologies implemented within the IATF 16949 standards can boost the automotive production quality and decrease the costs and customer dissatisfaction. The automotive industry has changed today due to the implementation of IoT and AI in manufacturing, as this work has shown, with an exciting horizon of the constant automation process and increasing quality indications to deliver on the promise of the redefined definition of success in this industry.
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How this classification was reachedexpand
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| 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 | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".