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This paper considers the problem of assigning workers to manufacturing cells in order to maximize the effectiveness of the organization. Organization effectiveness is assumed to be a function of the productivity, output quality, and training costs associated with a particular worker assignment. Traditionally, these worker assignments have been based only on the technical skills of the workers. The proposed model also includes human skills and permits the ability to change the skill levels of workers by providing them with additional training. The problem is formulated as a mixed integer programming problem. A total of 32 test problems were developed and varied with regard to the total training time, the available training time for each worker, the training costs, the productivity coefficients and the quality level coefficients. Results indicate that this model provides better worker assignments than one that only considers technical skills. [ABSTRACT FROM AUTHOR]

This paper describes the development of predictive models for glass production at a regional manufacturing company. The objectives of the models are to predict the actual batch tonnage produced per week from the glass furnace based on the planned production schedule. Four modelling methods were explored: (i) linear regression; (ii) nonlinear regression; (iii) artificial neural network using back-propagation; and (iv) radial basis function neural network. Using 175 cases of production schedule data and subsequent furnace output, the two neural network-based prediction models resulted in lower average absolute error and lower maximum absolute error than the linear or nonlinear regression models. Accurate neural network-based prediction models of furnace output will subsequently be used in the overall production planning system by utilizing estimates of furnace output to determine the necessary energy, raw material, repair and personnel requirements of the glass manufacturing facility. [ABSTRACT FROM AUTHOR]

Results presented are part of a larger mixed-mode study on supplier quality practices focusing on the cross-analysis of interviews, focus groups, and a questionnaire, which collected actual data about timing, costs, practices, and non-conformances associated with purchase orders (POs) from Engineering Procure and Construct (EPC) projects for four material types considered relevant to projects built by EPC contractors based in the United States. The present discussion specifically focuses on the analysis of the non-parametric dataset obtained from the POs to investigate a subset of companies labelled as highly effective companies (HECs). This contribution is unique as it used a mixed-mode approach to collect data beyond construction sites and investigate the product/supplier side of construction processes to advance knowledge of how materials can be sent to construction project sites free of defects and rework and indicates practices that contribute to this goal. When compared to other companies in the dataset, HECs engage in more hours of observation in suppliers' facilities, communicate more often with suppliers, put more effort and time in the planning stages of the project, use more practices related to supplier quality surveillance and supplier quality management, and find non-conformances earlier in the project. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The process of assuring the procured and fabricated materials for an engineer-procure-construct (EPC) project are within their quality requirements is challenging because nearly every EPC construction project is complex and distinctive from previous projects in terms of its size, supply chain, and materials usage. In particular, EPC projects include contractors, subcontractors, and suppliers who collaboratively perform the engineering design for the project, procure the required materials and equipment, and then construct. Many (or most) construction organizations, representing owners and contractors, place high importance on documenting and tracking the quality performance of their suppliers as part of their sup plier quality management (SQM) to ensure that procured and fabricated materials are within the quality specifications. However, these organizations still face problems with their SQM, evidenced by the large number of rework tasks and replacement efforts for the supplied materials in the EPC projects. This highlights the importance of investigating the current supplier quality practices to explore effective practices to deliver products with the expected quality and zero rework. In this paper, six structured interviews were conducted with contractors, and 92 SQM documents including procedures and reports from 21 owners and contractors in the EPC industry were analyzed to identify the current practices used by these organizations with regard to SQM, and the most effective practices that construction engineering managers could borrow to improve the existing SQM in the EPC projects. The contribution of this research can be used by stakeholders in the construction industry to improve SQM within their organizations. Researchers can also benefit from this research to better understand SQM practices within the construction industry. [ABSTRACT FROM AUTHOR]

This study was funded by the Construction Industry Institute (CII) to investigate effective supplier quality practices that can reduce rework associated with supplied components in engineering-procure-construct (EPC) projects. The motivation was to analyze how the level and amplitude of inspection in different stages of a supply chain (from fabrication to mechanical completion) affect the resulting quality of products as measured by the number of nonconformances (NCs) found in its different stages. Quantitative data were collected using an instrument used by CII member organizations to submit data on actual purchase orders and analyzed using the Mann-Whitney (M-W) test on the nonparametric set of data obtained. Following the statistical analysis, interviews with subject matter experts, face-to-face presentations, and sharing of reports with the research team were used to clarify questions, interpret the data, and validate conclusions. The results provide insights regarding where inspection is most needed and indicate that some of the following supplier quality surveillance (SQS) practices are correlated with improved supplier quality: observation and inspection in the supplier's facility; development and use of a quality control or inspection and testing (QC/IT) plan; planning in concert with suppliers; increased communication before and during execution; and postproject completion assessments (PPCAs) of supplier performance. Results also showed that registered or certified suppliers do not show a statistically significant relationship to improved quality. Finally, the study contributes to explaining how process inspections are directly linked to the number of resulting product nonconformances, identifying which practices used by suppliers and owners/contractors are more effective in reducing the number of nonconformances in a product, and identifying potential indicators that can be collected during the execution of purchase orders and associated with the final quality of products. [ABSTRACT FROM AUTHOR]