Quality improvement of a categorical response with weight effect consideration

Purpose - No methodology has been directly proposed to address the parameter optimization problem with weight effect on the categorical response. The aim of this paper is to propose a suitable procedure to address such a problem. Design/methodology/approach - The computation of aggregation weight and neural network modeling technique were employed into forming the core architecture of the proposed approach. The consistency and difference of the weight effect between several experts or professionals can be included into the weight computation. The backpropagation neural network model is chosen to model the non-linear relationship among the control factors, the probability, and the accumulated probability of categories for a qualitative response. Findings - Weight effect for different categories of a qualitative response significantly exists in L/F manufacturing process. Including such weight effect into the L/F manufacturing analysis can achieve the parameter optimization and enhance their quality improvement. Originality/value - This paper can be viewed as the first to address the parameter optimization problem for the categorical response with the weight effect consideration. The proposed approach can aid engineers making necessary decisions about quality improvement.