Time Series Analysis for Quality Improvement: a Soft Computing Approach

Quality improvement provides organizations with significant opportunities to reduce costs, increase sales, provide on time deliveries and foster better customer relationships. The design and manufacturing are among the critical processes for continuous quality improvement. Time series data collected from these processes are the useful source. While there are various techniques to explore these processes, Neural Networks (NN) approach is deemed as a promising alternative. However, as NN is a relatively new approach in quality engineering which is traditionally dominated by statistical analysis, there is still much doubt in its effectiveness compared with statistical modeling. The main focus here then is to construct a statistically reliable neural network model with an appropriate architecture to conduct the time series analysis. The purpose of this paper is thus two-fold. Firstly we develop the statistical interval analysis for neural network models which provide a statistical guide towards a reliable modeling architecture. Secondly, we apply the developed approach for quality improvement in various industries.