A Product Quality Forecasting using Autoregressive Moving Average

A.T. Bon and N.A. Hamid (Malaysia)


: ARIMA, forecasting, quality, product, control chart


This research is a research on product quality control chart modeling and forecasting based on product manufacturing of automotive break specification at the front and rear end using time series analysis. The motive of this research is to build a model using Model Autoregressive Moving Average (ARIMA) analysis, which can then be used to explain the changeability level of product quality in the times ahead. Product quality control chart X-bar and R is on the methods of explaining and controlling the level of product quality. The daily data sample data, n=2 of three years will be analyzed using the SPSS software. With the help of this software, 12 ARIMA (p,d,q) models will be simulated for every data sample. In this research, a comparison among the ARIMA (p,d,q) models will be carried out to choose the best forecasting model. The models ARIMA (1,1,2), ARIMA (2,1,3), ARIMA (3,1,1) and ARIMA (3,1,3) that are chosen will be used to test the entire sample and comparison results will be obtained. Product quality forecast data one year ahead is carried out by using the chosen models and the findings show that product quality for one year ahead is in a controlled state. While the behavioral pattern of the charts show a downward pattern for the X-bar chart and an upward pattern for the quality control chart R. In all, this research proves that ARIMA forecasting can be used in building models and product quality forecasting in the future more accurately.

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