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This study aims to emphasize utilization of Predictive Six Sigma to achieve process improvements based on machine learning (ML) techniques embedded in define, measure, analyze, improve, control (DMAIC). With this aim, this study presents selection and utilization of ML techniques, including multiple linear regression (MLR), artificial neural network (ANN), random forests (RF), gradient boosting machines (GBM) and k-nearest neighbors (k-NN) in the analyze and improve phases of Six Sigma DMAIC.

A data set containing 320 observations with nine input and one output variables is used. To achieve the objective which was to decrease the number of fabric defects, five ML techniques were compared in terms of prediction performance and best tools were selected. Next, most important causes of defects were determined via these tools. Finally, parameter optimization was conducted for minimum number of defects.

Among five ML tools, ANN, GBM and RF are found to be the best predictors. Out of nine potential causes, “machine speed” and “fabric width” are determined as the most important variables by using these tools. Then, optimum values for “machine speed” and “fabric width” for fabric defect minimization are determined both via regression response optimizer and ANN surface optimization. Ultimately, average defect number was decreased from 13/roll to 3/roll, which is a considerable decrease attained through utilization of ML techniques in Six Sigma.

Addressing an important gap in Six Sigma literature, in this study, certain ML techniques (i.e. MLR, ANN, RF, GBM and k-NN) are compared and the ones possessing best performances are used in the analyze and improve phases of Six Sigma DMAIC.

This study aims to show the effectiveness and applicability of artificial intelligence applications in the measurement and evaluation of university services. Universities can gain competitive advantage through providing their students with quality services in various aspects, such as bookstores, dormitories, recreation centers as well as cafeterias. Among these facilities, university cafeterias are places where students spend a significant amount of time. Therefore, this study aims to integrate artificial intelligence application in the evaluation of university cafeteria services based on students' perceptions with two-stage structural equation modeling (SEM) and artificial neural network (ANN) approach.

An artificial intelligence based SEM-ANN hybrid approach was used to determine the factors that have significant influence on student satisfaction, sufficiency-of-services and likelihood-of-recommendation. Data were collected from 373 students through a face-to-face questionnaire. Initially, four service quality dimensions were attained through factor analysis. Then, hypotheses, which were determined via literature review, were tested through SEM-ANN hybrid approach.

Incorporating the results of SEM analysis into the ANN technique resulted in superior models with good prediction performance. Based on four ANN models created and ANN sensitivity analyses conducted, significant predictors of satisfaction, sufficiency, reliability and recommendation are determined and ranked.

Prior studies have assessed service quality using traditional techniques, whereas, this study integrates artificial intelligence in the assessment of higher-educational institutions' services quality. Also, as a distinction from previous studies, this study ranked importance levels of predictor variables through ANN sensitivity analysis.

The purpose of this study is to analyze door-panel alignment defects seen in built-in ovens manufactured in one household appliances company's plant. Alignment defects in oven door panel substantially affect aesthetics of the product which is an important aspect in driving customer preference and satisfaction. Therefore, this study aimed to increase the initial 3.1 sigma level of oven-manufacturing process to at least 4 sigma level by decreasing a particular door-panel alignment defect, which constituted 67.7 percent of the overall alignment defects.

The goals were achieved through a structured Six Sigma implementation with lean element by utilizing various Six Sigma tools such as workflow, Pareto-analysis, measurement system analyses, control-charts, process capability analysis, cause-and effect-diagram and hypotheses tests. A non–value-added step was also eliminated through the lean approach.

Through Six Sigma implementation, the initial 3.1 sigma process performance level has been increased to 4.4 sigma level leading to substantial decrease in alignment defects.

In the quality management literature, not many papers directly deal with aesthetics and appearance problems of the products especially in the household appliances industry. Moreover, hypothesis testing is not frequently used in Six Sigma implementations in the literature. In addition to limited usage of hypothesis testing, very few studies conducted a thorough measurement system analysis. Considering these gaps in the Six Sigma literature, this study fills an important gap in research by implementing a detailed Six Sigma study, enhanced with hypothesis testing and a thorough measurement system analysis, on the aesthetics and appearance of the product.

Implementing new quality initiatives in organizations is challenging, as it requires managers and employees to adjust to new processes, methodologies and even mindsets. The purpose of this study is to investigate the relationship between quality management (QM) practices and readiness for change due to implementing new quality initiatives such as lean, six sigma and to determine which dimensions of QM are more important to change efficacy and change commitment.

The issues are examined in this study through the analysis of survey data obtained from US textile and apparel industry managers by using factor analysis, stepwise regression to construct path model and structural equation modeling.

This study identifies change readiness measures which are specific for quality implementations and establishes two constructs, namely, change commitment and change efficacy. The results indicate that as good Employee Relations increase, the level of organizational change commitment increases. If employees are engaged and empowered to provide excellent quality, then it is more likely that they will be committed to change due to implementing future quality initiatives. Moreover, a direct relationship between Customer Relationship Management and change efficacy is found. Companies with a strong customer orientation are more able to implement the quality initiatives that matter to their markets.

This study is unique in investigating the empirical relationship between QM practices and the dimensions of readiness for change due to implementing new quality initiatives via data from various organizations. This study empirically contributes to the QM literature with change readiness antecedents in quality implementation setting.

The purpose of this paper is to develop a supply chain management framework covering different business environment levels, that is, macro, micro and supply chain levels, and also to evaluate Haiti vs China as apparel-sourcing partners by assessing macro-level, supply-chain-level and micro-level environments from the US apparel buyers’ perspective.

In order to achieve this, first, a framework covering these business environment levels was developed and tested through path analysis. Prior to path analysis exploratory factor analysis was conducted to verify proposed factor structures. Data were collected using face-to-face interviews with a sample of 41 apparel companies that operate in the USA and source from China and Haiti.

This study found that both supply-chain-level and micro-level environments positively impact global supply chain performance. Supply-chain-level also has a direct effect on micro-level environment and macro-level environment has only a direct effect on supply-chain-level environment. Interviews and discussions based on this framework indicate that Haiti’s proximity to the USA, price, low-wage rates, small-order sourcing opportunities and good basic sewing skills are among Haiti’s strengths.

This study is unique in developing a multi-level environment framework for supply chain management and in comparing Haiti and China in terms of their supply chains to evaluate the potential competitiveness of Haitian apparel supply chain.

As opposed to general literature reviews, by narrowing down the context only around the resources related to Six Sigma tools, this study aims to offer a strong discussion about Six Sigma toolbox which has a vital role in the success of Six Sigma.

Based on a comprehensive literature research, the most used tools; classification of tools; flow of tools with respect to define, measure, analyze, improve and control (DMAIC) steps; tools as critical success factors and reasons of ineffective use of tools are reviewed. To stay focused and not to diverge from the research aim, 60 articles which are suitable to the context and flow of the discussion are selected during the construction of the study.

The study provides a detailed and integrated review of Six Sigma articles about tools. The most used tools are listed from different perspectives and resources, and the role of these tools has been discussed. After a broad review, a more practical and combined classification of Six Sigma tools is proposed. Next, the issue of using which tools during which steps of DMAIC is systematically addressed. Finally, emergence of tools as a critical success factor and the gaps in the literature related to tools of Six Sigma are pointed out.

Addressing important statistics and the facts related to the tools of Six Sigma helps new practitioners in particular to build a strategic filter to select the most proper tools throughout their projects.

This study is unique in investigating only Six Sigma toolbox and providing a literature review on this subject.

This study aims to provide a comprehensive insight into the role of the quality management (QM) systems in international suppliers (e.g. ISO, Total Quality Management, Lean and Six Sigma) on outsourcing decisions of buyer companies with regard to reshoring activities.

By the means of a comprehensive survey data collected within USA textile and apparel industry, the authors identify four quality factors and compare these factors for international and domestic suppliers by the means of linear discriminant analysis (LDA) and consequently define a reshore vector. Next, the most-effective QM system within international suppliers is analyzed by the means of a new application based on LDA.

The results reveal that international suppliers perform worse for all quality factors compared to domestic USA suppliers, which may leave the door open for reshoring activities. Furthermore, it is shown that Lean and Six Sigma within international suppliers lead to superior levels of quality because they are aligned along the most opposite directions against reshore vector. Therefore, it is claimed that Lean and Six Sigma within the international suppliers can inhibit reshoring activities of US companies.

The willingness of US textile companies to assist their international suppliers and the challenges on this subject are discussed on the basis that supplier development (or quality management) programs can be strong alternatives to reshoring activities.

QM systems in suppliers are analyzed with a new method within a new context based on reshore phenomenon, which provides an essential point of view for academic and industrial environments.