Analyzing supplier quality management practices in the construction industry

• Published in: Quality engineering Vol. 28; no. 2; pp. 175 - 183
• Main Authors: AlMaian, Rufaidah Y., Needy, Kim LaScola, Walsh, Kenneth D., Alves, Thaís da C. L., Scala, Natalie M.
• Format: Journal Article
• Language: English
• Published: Milwaukee Taylor & Francis 02-04-2016; Taylor & Francis Ltd
• Subjects: analytic hierarchy process; Construction industry; Decision making models; Hierarchies; multivariate data analysis; principal component analysis (PCA); Principal components analysis; Quality control; Quality management; supplier quality management (SQM); Suppliers; Validation studies
• ISSN: 0898-2112; 1532-4222
• DOI: 10.1080/08982112.2015.1086927

Supplier quality management (SQM) is an important function in the construction industry. Many construction organizations place high importance on using quantitative analyses to select effective SQM practices that ensure that materials, assemblies, and fabricated equipment for the construction project are within quality specifications. However, traditional quantitative analysis methods may be limited because the process of acquiring enough data to conduct the analyses is time consuming and costly. This article discusses the use of principal component analysis (PCA) to analyze a number of SQM practices from construction organizations known for their effective SQM. PCA is useful in this study because the data available for analysis are small in size and multivariate. SQM practices are discussed extensively and validated with subject matter experts (SMEs) using the analytical hierarchy process (AHP). We show that supplier's work observation, supplier performance rating, inspection effort tracking, and inspection and testing plans are important practices for SQM. We propose a quantitative methodology that can be used by quality engineers to analyze small sample size data. The research also describes how AHP, an analysis method based on expert judgment, can be used to validate and support the conclusions drawn from small sample size analyses. Identification of important SQM practices can benefit construction professionals with limited resources.