Forecasting warranty performance in the presence of the ‘maturing data’ phenomenon
Why this work is in the frame
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Bibliographic record
Abstract
Abstract Forecasting of warranty performance helps car engineers to fine-tune their strategies for warranty cost reduction. The forecasting process involves prediction of not only the specific months-in-service (MIS) warranty performance at a certain future time, but also future MIS values. However, the 'maturing data' phenomenon that causes a warranty performance measure at specific MIS values to change with time make such forecasting challenging. Although dynamic linear models have been used for forecasting warranty performance, the focus mainly has been to utilize previous-model-year vehicle data for the analysis. In this paper, we apply a neural network model to forecast year-end warranty performance in the presence of the 'maturing data' phenomenon. We use a special type of neural network, viz. radial basis function (RBF), and optimize its parameters by minimizing training and testing errors through planned experimentation. This application shows the effectiveness of RBF neural networks to forecast warranty performance in the presence of the 'maturing data' phenomenon. Keywords: Warranty performanceMaturing data or warranty growthRadial basis functionNormalized root mean square errorSignal-to-noise ratio Acknowledgement This research is being partially funded from a grant from the Ford Motor Company. Bharatendta Rai has a Ph.D. in Industrial Engineering from Wayne State University, Detroit and is currently working at Ford Motor Company as quality and reliability engineer. His research interests include field reliability studies from automobile warranty datasets, product and process improvements through robust design and forecasting applications using artificial neural networks and wavelets. He has a master's in quality, reliability, and OR from the Indian Statistical Institute (India) and another master's in statistics from Meerut University (India). He also worked at the Statistical Quality Control division of Indian Statistical Institute as a SQC specialist and carried out consulting and training assignments for various industries across India during 1993–2000. He is a member of ASQ, INFORMS, and IIE. Nanua Singh is a professor and Director of Product Development Laboratory in the Department of Industrial and Manufacturing Engineering since January 1993. He was professor and Head of the Department at the University of Windsor, Canada and taught there for six years till December 1992. Before joining University of Windsor he taught at IIT Delhi (India) for seven years. He has written three books, over 70 journal papers in various journals including IIE Transactions and over 50 conference proceeding papers. Dr. Singh is presently working in the areas of Robust Engineering, Concurrent Engineering and Smart Product Modeling in a Knowledge-based Engineering environment.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 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