Purpose - The purpose of this paper is to explore the relationship between multivariate process capability indices and loss functions for both nominal-the-best and smaller-the-better cases, so the likelihood and consequences resulting from the nonconforming of a manufacturing process or an environmental system can be evaluated simultaneously. Design/methodology/ approach - In this paper, the authors present a new approach of correlated risk assessment by linking the multiple process capability indices and loss functions, in which the multivariate process capability indices and multivariate loss functions describe the likelihood and consequences as a result of nonconformities in multivariate manufacturing or environmental system, respectively. Then, the associated relationship equations are developed using multivariate methods. Moreover, a step-by-step procedure is provided to facilitate the implementation of the correlated risk assessment. Findings - Given the multivariate process capability indices, the authors show that the expected loss can be estimated by developed relationship equations and two numerical examples are also given, to demonstrate how the correlated manufacturing and environmental risks can be properly assessed by linking the multivariate process capability indices and multivariate loss function. Practical implications - The risk information of likelihood and expected loss, classified in the four planning zones of a strategic planning matrix, provides practising managers and engineers with a decision-making tool for prioritizing their quality improvement projects when conducting risk assessment for any multivariate process or environmental system. Originality/value - Once the existing quality/environmental problems and their Key Performance Indicators are identified, one may conduct risk assessment by applying the relationship equations to evaluate the impact of correlated risk on manufacturing processes or multiple environmental emissions inside company; this can lead to the direction of continuous improvement for any industry.
All Science Journal Classification (ASJC) codes
- Management Information Systems
- Industrial relations
- Computer Science Applications
- Strategy and Management
- Industrial and Manufacturing Engineering