Combined forecasting approach for product quality based on support vector regression and gray forecasting model

Abstract

Forecasting product quality by incorporating customer satisfaction perception factors is an intriguing research area, which can promote the sustainable development of enterprises. To address small-sample random time series data, this study proposes a combined forecasting approach (CFA) for the product quality index that considers perception factors. The proposed approach is based on the support vector regression (SVR) and an improved gray forecasting model (GFM). First, the study constructs a system of perception factors related to defect parts per million (DPPM). Then, the key perception factors (KPF) are selected using the gray entropy relational degree, which is derived from gray relational analysis and information entropy. Then, a multivariable GFM is proposed based on the weighted Markov and the derived form of the gray model to reduce the forecasting error. Finally, a CFA is constructed considering KPF and optimized based on the SVR and the proposed GFM to forecast the DPPM. A case study of liquid crystal display is conducted to demonstrate the feasibility of the proposed CFA. The forecast error of the proposed CFA is 3.2%, which is better than those of GFM, SVR, and ARIMA (4.01%, 6.21%, and 9.89%, respectively). The comparison and discussion of methods demonstrate the superiority of the proposed approach for forecasting product quality.