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

Research and evaluation of the operating characteristics of used ship engine oil using the process parameter matrix method

The marine engine circulating oil change interval suggested by manufacturers is a guideline based on general scale statistics and laboratory testing. At the same time, the actual remaining oil life can significantly correct the time and money spent by the chief mechanic service on system maintenance. In the present work, a method has been developed that makes it possible to increase the service life of mechanisms and the reliability of ship equipment under operating conditions. The effect is achieved by identifying and analyzing the most significant and influential parameters of the lubricant used. An array of physical and chemical data on lubricants, taking into account the equipment's time to failure, is processed by a special computer program for monitoring the state of a marine engine in operating mode. The developed software package allows more accurate and timely maintenance of the SPP (ship power plant)

Warranty optimisation based on the prediction of costs to the manufacturer using neural network model and Monte Carlo simulation

Warranty is a powerful marketing tool, but it always involves additional costs to the manufacturer. In order to reduce these costs and make use of warranty's marketing potential, the manufacturer needs to master the techniques for warranty cost prediction according to the reliability characteristics of the product. In this paper a combination free replacement and pro rata warranty policy is analysed as warranty model for one type of light bulbs. Since operating conditions have a great impact on product reliability, they need to be considered in such analysis. A neural network model is used to predict light bulb reliability characteristics based on the data from the tests of light bulbs in various operating conditions. Compared with a linear regression model used in the literature for similar tasks, the neural network model proved to be a more accurate method for such prediction. Reliability parameters obtained in this way are later used in Monte Carlo simulation for the prediction of times to failure needed for warranty cost calculation. The results of the analysis make possible for the manufacturer to choose the optimal warranty policy based on expected product operating conditions. In such a way, the manufacturer can lower the costs and increase the profit

Warranty optimisation based on the prediction of costs to the manufacturer using neural network model and Monte Carlo simulation

Warranty is a powerful marketing tool, but it always involves additional costs to the manufacturer. In order to reduce these costs and make use of warranty's marketing potential, the manufacturer needs to master the techniques for warranty cost prediction according to the reliability characteristics of the product. In this paper a combination free replacement and pro rata warranty policy is analysed as warranty model for one type of light bulbs. Since operating conditions have a great impact on product reliability, they need to be considered in such analysis. A neural network model is used to predict light bulb reliability characteristics based on the data from the tests of light bulbs in various operating conditions. Compared with a linear regression model used in the literature for similar tasks, the neural network model proved to be a more accurate method for such prediction. Reliability parameters obtained in this way are later used in Monte Carlo simulation for the prediction of times to failure needed for warranty cost calculation. The results of the analysis make possible for the manufacturer to choose the optimal warranty policy based on expected product operating conditions. In such a way, the manufacturer can lower the costs and increase the profit