Using reliability information and neuro-fuzzy to predict warranty cost: a case study in fleet vehicle

Abstract

Nowadays, the great market competition makes that the companies look for high reliability and quality of the products manufacturing. The effective ways to ensure reliability signals of the product is by offering better warranty terms and period associated with sale of the product. In fact, warranty is a legal obligation of the manufacturer or dealer in connection with the sale of the product that defines the liability of the manufacturer or dealer in the event of the premature failure or defects of the product. The purpose of this paper is to propose a method for reliability analysis of the warranty data and to validate the warranty policy. In order to applied neuro-fuzzy approach by optimizing warranty cost and period, modelling the reliability of the product must be beneficial.