Forecasting warranty performance in the presence of the ‘maturing data’ phenomenon
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Résumé
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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Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,002 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle