Economic production quantity and process quality: a multivariate approach

Abstract

PurposeThe purpose of this paper is to study the interaction of economics of production with process quality, when multiple key quality characteristics are present. Specifically, the paper aims to analyse the possibility of investing in a production process to reduce its variances and the impact on a multivariate quality loss function.Design/methodology/approachA bivariate inventory‐planning model is developed, in which the optimal investment for reducing process variances and the optimal lot size are jointly determined. A case study with industrial data is presented to illustrate the possible solution procedures and the potential advantages of the proposed model.FindingsIt is found that by using the previous approaches to analyse the interaction between the economics of production and process quality, a company will underestimate the cost of quality, especially the expected external failure cost (quality loss), and ultimately invest less into the prevention activities to improve the process.Originality/valueThe proposed model can help managers to compare different production processes and also guide the managers towards better choices for process improvement. To the best of our knowledge, this paper is the first to integrate the economic production quantity (EPQ) problem with the process quality consideration for products with multiple quality characteristics.

An integrated approach for the optimization of tolerance design and quality cost

“…Ouyang et al (2002) presented options of investment to achieve an improvement of process quality, setup cost reduction and lead-time reduction for an imperfect production process. Chan, Ibrahim, and Lochert (2005) proposed an investment modeling approach with a multivariate quality loss function http://dx.doi.org/10.1016/j.cie.2015.05.003 0360-8352/Ó 2015 Elsevier Ltd. All rights reserved.…”

“…Taguchi, Elsayed, and Hsiang (1989) proposed a quadratic loss function to model the relationship between the costs of poor quality and quality performance, where the quality performance is measured by the distance of the quality measurement to the target value. Using Taguchi loss function in statistical process control methods has been addressed by Chan et al (2005), Chen (2006), Tsou (2006) and Abdul-Kader et al (2010). A new optimization strategy method, the SNDOS method, is established in this study to cover three realistic concerns:…”