Reliability Simulation Mechanism Model of Big Data Mileage Prediction for Automotive Warranty

Abstract

Stiff competition among automotive manufacturers to secure market share results in a short period of product development until production. Thus, there is a gap of limited attention concerning effective early detection tools leveraging information technology to identify product quality by optimising warranty data towards expediting market action. A big data simulation analysis model of warranty prediction is proposed based on parameter mileage using the Weibull statistic platform. Input of warranty prediction analysis based on warranty historical data, continued with data cleaning and selection. The algorithm model is applied to support big data analysis based on the application of Weibull statistics. The product with the highest failure rate in terms of warranty amount and quantity, part number PN312, was selected. The shape ? value is 2.308, which matches the Rayleigh distribution with the shape ? of product failure at 403,948km when the incidence is 63%. The model orchestrated the future warranty outcome and consequences. Relatively, the warranty prediction system simulates the evaluation costs of poor quality. The development of a new prediction simulation model will enhance the application of QC tools, expedite the selection of poor-quality products, eliminate wasteful resources such as time and manpower, and simplify the investigation process