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In implementing Six Sigma and/or Lean Six Sigma, a practitioner often faces a dilemma of how to select the subset of root causes from a superset of all possible potential causes, popularly known as root cause analysis (RCA). Generally one resorts to the cause and effect diagram for this purpose. However, the practice adopted for identification of root causes is in many situations quite arbitrary and lacks a systematic, structured approach based on the rigorous data driven statistical analysis. This paper aims at developing a methodology for validation of potential causes to root causes to aid practitioners.

Discussion has been made on various methods for identification and validation of potential causes to root causes with the help of a few real life examples for effective Lean Six Sigma implementation.

The cause and effect diagram is the frequently adopted method for identifying potential causes out of a host of methods available for such identification. The method of validation depends on the practitioners’ knowledge on the relationship between cause and effect and controllability of the causes.

The roadmap thus evolved for the validation of root causes will be of great value to the practitioners as it is expected to help them understand the ground reality in an unambiguous manner resulting in a superior strategy for cause validation and corrective actions.

The purpose of this paper is to explore the role of qualitative research in causality, with particular emphasis on process causality. In one paper, it is not possible to discuss all the issues of causality, but the aim is to provide useful ways of thinking about causality and qualitative research. Specifically, a brief overview of the regularity theory of causation is provided, qualitative research characteristics and ontological and epistemological views that serve as a potential conceptual frame to resolve some tensions between quantitative and qualitative work are discussed and causal processes are explored. This paper offers a definition and a model of process causality and then presents findings from an exploratory study that advanced the discussion beyond the conceptual frame.

This paper first conceptually frames process causality within qualitative research and then discusses results from an exploratory study that involved reviewing literature and interviewing expert researchers. The exploratory study conducted involved analyzing multiple years of literature in two top human resource development (HRD) journals and also exploratory expert interviews. The study was guided by the research question: How might qualitative research inform causal inferences in HRD? This study used a basic qualitative approach that sought insight through inductive analysis within the focus of this study.

The exploratory study found that triangulation, context, thick description and process research questions are important elements of qualitative studies that can improve research that involves causal relationships. Specifically, qualitative studies provide both depth of data collection and descriptive write-up that provide clues to cause-and-effect relationships that support or refute theory.

A major conclusion of this study is that qualitative research plays a critical role in causal inference, albeit an understated one, when one takes an enlarged philosophical view of causality. Equating causality solely with variance theory associated with quantitative research leaves causal processes locked in a metaphoric black box between cause and effect, whereas qualitative research opens up the processes and mechanisms contained within the box.

This paper reframed the discussion about causality to include both the logic of quantitative studies and qualitative studies to demonstrate a more holistic view of causality and to demonstrate the value of qualitative research for causal inference. Process causality in qualitative research is added to the mix of techniques and theories found in the larger discussion of causality in HRD.

This paper attempts to develop a hybrid cause and effect diagram (CED) and interpretative structural model (ISM) for root cause analysis in quality management. The proposed model overcomes the weakness of the CED in reliably articulating hierarchical cause–effect Relationships.

A focus group discussion (FGD) among quality experts in the case company to establish relationships between the determined causes.

The hybridization of the CED and ISM allowed the causes to be ordered more clearly to determine potential root causes as well as presenting these causes more comprehensively.

The paper has been one of the very few attempts to improve the CED approach. As such, this paper employs the ability of the ISM to order concepts in a hierarchical structure, which is useful in determining root causes.

The purpose of this paper is to go a step further from the traditional 5 Whys technique by adding three more legs during the root cause analysis stage – occurrence, human and systemic issues that contribute toward the problem, hence the term 3 × 5 Whys. Performing individual 5 Whys for these three components enables to identify deeper root cause(s) that may spawn across multiple groups within an organization.

Cause-and-effect analysis used during traditional root cause investigations within an 8D or Lean six sigma project is used as a theoretical foundation. Examples from different industries are presented showing the 3 × 5 Why’s framework and advantages it brings to the organization along with identifying shortcomings and suggestions to make it more effective.

If properly used this integrated methodology will reveal higher order systemic causes (e.g. policies or management decisions) stemming from lower lever symptoms (e.g. defective parts, procedural errors). Effective execution of this methodology can provide tremendous results in defect reduction, yield improvement, operational efficiency improvement and logistics management type of projects. Resolving higher level sources of problems allows an organization to evolve itself and maintain a competitive edge in the market.

Adopting this quality management technique in start-up companies entails some challenges and other implications have been discussed with SWOT analysis.

Examples from various sectors using 3 × 5 Why approach have been presented that show that this methodology provides deeper insight into root causes which could be affecting multiple groups in an organization. Using this technique effectively is found to be beneficial to resolve issues in operations management, logistics, supply chain, purchasing, warehouse operations, manufacturing, etc.

This methodology has a human component which often results in some sort of resistance as not all working professionals think alike when it comes to accountability and ownership of issues. This may hinder root cause analysis and subsequent corrective actions implementation.

This study is unique in its in-depth real-world case studies demonstrating the need for taking a deep dive approach to root cause analysis by understanding specific, system and human components responsible for causing the failure mode.

Risk factors related delay hinder the schedule performance of most construction projects in the world. It is a critical challenge to realize the advantages of prefabricated construction projects (PCPs) under the negative effect of schedule delay. This paper aims to propose an exhaustive list of risk factors impeding the progress of PCPs and evaluate the collected risk factors based on the cause–effect relations. The ultimate goal is to improve the understanding of the complex relations among various risk factors related delay in PCPs, and also offer managers a reference on aspect of schedule risk management.

This paper proposes a hybrid method of GT–DEMATEL–ISM, that is combing grounded theory, DEMATEL (decision-making trial and evaluation laboratory) and ISM (Interpretative Structural Modeling), to collect, evaluate and structure risk factors related delay for PCPs. The research procedure of this methodology is divided into three stages systematically involving qualitative and quantitative analysis. In the first stage, GT is utilized to implement qualitative analysis to collect the risk factors leading to schedule delay in PCPs. While, the quantitative analysis is to analyze and evaluate the collected risk factors based on the cause–effect relations in the next two stages evaluation by the DEMATEL focuses on quantifying the priority and intensity of the relations between factors. Additionally, ISM is employed to construct the hierarchical structure and graphically represent the pairwise relations between factors.

The outcome of qualitative investigation by grounded theory proposes a theoretical framework of risk factors related delay for PCPs. The framework contains three levels of category, namely, core category, main category and initial category and provides a list of risk factors related delay. Following this finding, evaluation results by the DEMATEL classify factors into cause and effect groups and determine 11 critical delay risk factors. Meanwhile, the findings show that risks referring to organizational management issue foremost impact the progress of PCPs. Furthermore, a systemic multilevel hierarchical structure model is visually constructed by ISM to present the pairwise linkages of critical factors. The model provides the risk transmission chains to map the spread path of delay impact in the system.

The contribution of the study involves twofold issues. Methodologically, this research proposes a hybrid method GT–DEMATEL–ISM used to identify and analyze factors for a complex system. It is also applicable to other fields facing similar problems that require collecting, evaluating and structuring certain elements as a whole in a comprehensive perspective. The theoretical contribution is to fill the relevant research gap of the existing body of knowledge. To the best knowledge of the authors, this paper is the first attempt to integrate qualitative and quantitative research for risk analysis related delay and take the insight into the whole process of PCPs covering off-site manufacture and on-site construction. Furthermore, the analysis of findings provided both a micro view focusing on individual risk factor and a managerial view from a systematic level. The findings also contribute the effective information to improve the risk management related schedule delay in PCPs.

The purpose of this paper is to improve the yield of a particular model of a car windshield, as the organization faces losses due to poor performance and rejection.

The Six Sigma DMAIC (define, measure, analyze, improve and control) methodology is used to reduce variation and defects in the process. It is a methodology based on data-driven and fact-based analysis to find out the root cause of the problem with the help of statistical analysis. A worst performing model is selected as a case study through the scoping tree. The preprocess, printing, bending and layup process defects are reduced by analyzing the potential causes and hypothesis testing.

This paper describes Six Sigma methodology in a glass manufacturing industry in India for automotive applications. The overall yield of a car windshield achieved 93.57 percent against the historical yield of 88.4 percent, resulting in saving 50 lacs per annum. Due to no rework or repairing in the glass, low first-time yield causes major losses. Process improvement through focused cross-functional team reduces variation in the process. Six Sigma improves profitability and reduces defects in the automotive glass manufacturing process.

This case study is applied in automotive glass manufacturing industries. For service and healthcare industries, a similar type of study can be performed. Further research on the common type of processor industry would be valuable.

The case study can be used as a problem-solving methodology in manufacturing and service industries. The tools and techniques can be used in other manufacturing processes also. This paper is useful for industries, researchers and academics for understanding Six Sigma methodology and its practical implementation.

This case study is an attempt to solve automobile glass manufacturing problems through DMAIC approach. The paper is a real case study showing benefits of Six Sigma implementation in the manufacturing industry and saving an annual cost of 50 lacs due to rejections in the process.

The purpose of this paper is to propose a new software tool to support design for quality (DfQ) in the electronics manufacturing sector where quality and reliability are critical.

An integrated modelling framework that enables complete and realistic representations of manufacturing systems is proposed. A software tool, developed based on this framework, offers two modules to support DfQ: simulation and root cause analysis. This paper focuses on the latter.

Integrated models enable tracing back effects to their root causes. Software tools based on these, kind of models can provide support in finding and eliminating the cause of a particular effect. This capability can be used to perform DfQ in an effective and accurate way.

The approach proposed strongly depends on the quality models within the integrated modelling framework. The models currently available are little and simplified. Future work includes the enrichment of the software by developing and more quality models.

The adoption of the proposed approach in an industrial context requires formalised information to fulfil the data required by the integrated modelling framework.

The main contribution of the paper is the integrated modelling framework definition and its implementation in the form of a software tool. The adoption of this tool in printed circuit assembly companies can support the solution of real quality problems and consequently, help to optimise manufacturing systems in the domain.

This research seeks to identify and apply techniques that can be used in a supply chain context to diagnose the causes of variability in delivery lead time.

A literature review was conducted and a number of quality management (QM), techniques were selected as candidates for diagnosing delivery time variability. A case study of the application of these techniques is provided on the UK‐based defence supply chain that supported UK operations in the Iraq war of 2003.

Candidate QM techniques for diagnosing delivery time variability were identified, namely: Process Chart; Histogram; Failure Mode and Effect Analysis; and Cause and Effect Analysis. These techniques were successful in enabling the diagnosis of the causes of delivery time variability in the context of the case study investigated.

The work illustrates how QM techniques can be employed to address issues with supply chains, not least with regard to the important problem of variability in delivery leadtime. In practice, this highlights benefits that result to practitioners in order to improve the performance of operations in a dynamic setting, such as the defence supply chain studied here.

This work has value in presenting the findings of an in‐depth case study on the application of QM techniques in a multi‐echelon supply chain setting. It is also original in employing the FMEA technique together with an end‐customer perspective to assess the effect of failure modes in operations across a supply chain. FMEA also provided the means to examine supply chain risk, thus providing a research instrument for deploying risk as a lens. The application of QM techniques in this novel setting provides support for their application beyond the conventional setting of internal operations.

Several well‐known managerial accounting performance measurement models rely on causal assumptions. Whilst users of the models express satisfaction and link them with improved organizational performance, academic research, of the real‐world applications, shows few reliable statistical associations. This paper seeks to provide a discussion on the “problematic” of causality in a performance measurement setting.

This is a conceptual study based on an analysis and synthesis of the literature from managerial accounting, organizational theory, strategic management and social scientific causal modelling.

The analysis indicates that dynamic, complex and uncertain environments may challenge any reliance upon valid causal models. Due to cognitive limitations and judgmental biases, managers may fail to trace correct cause‐and‐effect understanding of the value creation in their organizations. However, even lacking this validity, causal models can support strategic learning and perform as organizational guides if they are able to mobilize managerial action.

Future research should highlight the characteristics necessary for elaboration of convincing and appealing causal models and the social process of their construction.

Managers of organizations using causal models should be clear on the purposes of their particular models and their limitations. In particular, difficulties are observed in specifying detailed cause and effect relations and their potential for communicating and directing attention. They should therefore construct their models to suit the particular purpose envisaged.

This paper provides an interdisciplinary and holistic view on the issue of causality in managerial accounting models.

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.