Comparison between ANN and Multiple Linear Regression Models for Prediction of Warranty Cost

Nowadays, warranty has its own priority in business strategy for a manufacturer to protect their benefit as well as the intense competition between the manufacturers. In fact, warranty is a contract between manufacturer and buyer in which the manufacturer gives the buyer certain assurances as the condition of the property being sold. In industry, an accurate prediction of warranty costs is often counted by the manufacturer. Underestimation or overestimation of the warranty cost may have a high influence to the manufacturers. This paper presents a methodology to adapt historical maintenance warranty data with comparison between Artificial Neural Network (ANN) and multiple linear regression approach

Hazard rate models for early warranty issue detection using upstream supply chain information

This research presents a statistical methodology to construct an early automotive warranty issue detection model based on upstream supply chain information. This is contrary to extant methods that are mostly reactive and only rely on data available from the OEMs (original equipment manufacturers). For any upstream supply chain information with direct history from warranty claims, the research proposes hazard rate models to link upstream supply chain information as explanatory covariates for early detection of warranty issues. For any upstream supply chain information without direct warranty claims history, we introduce Bayesian hazard rate models to account for uncertainties of the explanatory covariates. In doing so, it improves both the accuracy of warranty issue detection as well as the lead time for detection. The proposed methodology is illustrated and validated using real-world data from a leading global Tier-one automotive supplier

Applying computer vision for detection of diseases in plants

Early detection and quantification of diseases in food plants are critical to agriculture industry and national food security. However, limitation in technology and cost has limited the success of applying Computer Vision in Plant Science. This research builds on the recent advance of Machine Learning, GPU and smartphones to tackle the problem of fast and low cost diagnosis of plant disease. In particular, we choose soybean as the subject for applying automatic disease detection. The reason is because soybean is an important crop for the state of Iowa and an important source of food for America. The plant is however, highly vulnerable to several type of diseases. This thesis consists of two sub-analyses of soybean diseases, which are: First, detection of a single disease in soybean, particularly Sudden Death Syndrome (SDS) with high detail (including location and severity). Second, detection of multiple diseases in soybean, using mobile phones which are resource- constraine

Development of a neural network approach for the assessment of the performance of traffic sign retroreflectivity

The goal of the research was to develop a new predictive tool for assessing the performance of traffic sign retroreflectivity and to compare the developed tool with the existing linear regression models. Retroreflectivity decreases as sign sheeting ages. Currently Louisiana Department of Transportation and Development (LADOTD) replace signs with low reflectivity based on driver complaints. This practice might have resulted in premature sign replacement (removal of signs with several years of in-service life still remaining) or in non-replacement of signs that are not in compliance with LADOTD minimum reflectivity standards. In this study, both neural network models and regression models were developed to predict reflectivity of Engineering and High Intensity Grade signs. The LADOTD traffic sign inventory data of Ascension Parish traffic signs were used for model development, validation and comparison. The performance of the developed neural network models (NN models) was compared to the developed regression models (R2 models) and also to the existing retroreflectivity regression models (R1 models) developed by Wolshon et al. The R1 models were developed for traffic signs placed along Interstate and State Highway routes in the districts of New Orleans, Baton Rouge, Lafayette, and Shreveport. Also, the usability of the neural network models developed in the study was analyzed based on the data collected by Wolshon et al to develop the linear regression R1 models. The results of this study demonstrated the feasibility of using ANNs in predicting the retroreflectivity of Type I and Type III sign sheeting. The independent variables found to be statistically significant variables in explaining the performance of traffic signs retroreflectivity included age of the sign, sheeting type, and background color of sign sheeting. A comparison of the models developed with two different specifications involving different sets of independent variables showed that the models including all the variables (i.e., Age, Edge of Pavement Distance, Sign Orientation, Sign Background Color, and Sheeting Type) increased the explanatory power of the models by little. However, it was recommended to use of all deterioration variables whose effects are not non-existent