Framework to determine the quality cost and risk of alternative control plans in uncertain contexts

Abstract

In manufacturing companies, quality control plans are essential to fulfill quality requirements and have associated quality appraisal and failure costs. However, there are barriers to the determination of quality costs, limiting the ability of companies to establish quality control plans at the lowest cost and, consequently, becoming more competitive. The paper's objective is to develop a framework to define a quality control strategy for a manufacturing process, by selecting amongst alternative quality control strategies (e.g., 100% inspection, statistical process control, and no inspection), the one that minimizes quality costs. The cost of quality determination depends on many parameters; some of them are volatile and uncertain. The framework represents such uncertainty by intervals and through simulation defines the best quality control strategy. A risk indicator is also developed to represent the possibility that the quality control strategy defined may not be optimal. Sensitivity analysis is performed to identify the model parameters with more impact on the quality cost, allowing to create multidisciplinary teams through cooperation, can better characterize relevant uncertain parameters, among the many parameters Industry 4.0 era makes available to managers.