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The study aims to identify the possible risk factors for electricity grids operational disruptions and to determine the most critical and influential risk indicators.

A multi-criteria decision-making best-worst method (BWM) is employed to quantitatively identify the most critical risk factors. The grey causal modeling (GCM) technique is employed to identify the causal and consequence factors and to effectively quantify them. The data used in this study consisted of two types – quantitative periodical data of critical factors taken from their respective government departments (e.g. Indian Meteorological Department, The Central Water Commission etc.) and the expert responses collected from professionals working in the Indian electric power sector.

The results of analysis for a case application in the Indian context shows that temperature dominates as the critical risk factor for electrical power grids, followed by humidity and crop production.

The study helps to understand the contribution of factors in electricity grids operational disruptions. Considering the cause consequences from the GCM causal analysis, rainfall, temperature and dam water levels are identified as the causal factors, while the crop production, stock prices, commodity prices are classified as the consequence factors. In practice, these causal factors can be controlled to reduce the overall effects.

From the results of the analysis, managers can use these outputs and compare the risk factors in electrical power grids for prioritization and subsequent considerations. It can assist the managers in efficient allocation of funds and manpower for building safeguards and creating risk management protocols based on the severity of the critical factor.

The research comprehensively analyses the risk factors of electrical power grids in India. Moreover, the study apprehends the cause-consequence pair of factors, which are having the maximum effect. Previous studies have been focused on identification of risk factors and preliminary analysis of their criticality using autoregression. This research paper takes it forward by using decision-making methods and causal analysis of the risk factors with blend of quantitative and expert response based data analysis to focus on the determination of the criticality of the risk factors for the Indian electric power grid.

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.

The adoption of the ISO 9001 Quality Management Systems Standard has proven to be a persistent and growing phenomenon in services and manufacturing, yet to date little research has been done that can indicate how far improved business performance can be attributed to it rather than counter‐intuitive causes. The paper aims to examine the evidence for the causal links between quality management system certification and improved performance in the empirical literature.

A method is proposed for testing how far performance improvement can be attributed to quality management system certification and how far attribution to other causes applies. This method is illustrated on a longitudinal study and then utilised to interpret the findings of other longitudinal studies.

It is concluded that although there is some evidence to indicate that quality management system certification has some causal influence on business performance, there is also evidence for the existence of a substantial mechanism whereby better performing firms self‐select to adopt certification. Possible causes for this mechanism are discussed.

The existence of a self‐select mechanism has profound implications for interpreting business performance achievements associated with quality management system certification because the benefits found may well be inflated by its presence. The authors suggest that richer theory is needed that can incorporate bi‐directional influences and new research is needed to explore the underlying causes of adoption selection effects.

The paper provides researchers with a method for testing and discussing causation influences on results. It provides evidence that a substantial part of the association found in the research on quality management system certification and business benefits may be due to counterintuitive causes.

The purpose of this paper is to provide a relatively straightforward approach of implementing standard statistical process control (SPC) concepts while instituting problem‐solving intonations in aero‐engine maintenance processes.

The inspection workflow approach is presented in order to aid in collecting and monitoring critical aero‐engine data. Observed defects are categorized according to a Pareto analysis assisted by a cause‐and‐effect diagram. A binomial process capability analysis is performed on nonconforming aero‐engines based on operating curves produced specifically for this case study. The time frame for experimental analysis is reflected in a span of six months.

It is found that a significant number of aero‐engines may be benefited by entering a more progressive maintenance program relying on predictive maintenance on the way to establishing a more effective Total Productive Maintenance scheme.

The case study showcases an approach to aero‐engine rejection statistical rates by accepting the fact that maintenance process may not be viewed as a process that may be limited to constant sampling.

For a long time, total quality management (TQM) tools have been deeply rooted in design, manufacturing and assembling of airliners and jet fighters alike. However, a comprehensive study focusing on the maintenance function of such complex machines may prove worthwhile now that an unstable global economy may prohibit extensive replacement of aging flying fleets.

With the lack of a prior practical unfolding in the field of genuine aero‐engine maintenance, this presentation aims to fill in the gap for engine rejection treatment. The variant operating curve notion introduced in the text is also a unique idea espoused for variable sampling situations when a binomial distribution is adopted.

The purpose of this paper is to identify and analyze the interdependence of project complexity factors (PCFs) in metro rail projects using the Decision-Making Trial and Evaluation Laboratory (DEMATEL). The study provides qualitative and quantitative analysis of project complexities factors and their relationships. The results of the study facilitate effective project planning, proactive risk management and informed decision-making by stakeholders.

This study employs a case-based method for identifying PCFs and a DEMATEL method for analyzing the interdependence of complexity factors in metro rail projects. Initially, PCFs were identified through an extensive literature review. To validate and refine these factors, semi-structured interviews were conducted with thirty experienced professionals, each having 5–20 years of experience in roles such as project management, engineering, and planning. Further, elevated and underground metro rail projects were purposefully selected as cases, for identifying the similarities and differences in PCFs. A questionnaire survey was conducted with various technical experts in metro rail projects. These experts rated the impact of PCFs on a five-point Likert scale, for the evaluation of the interdependence of PCFs. The DEMATEL technique was used to analyze the interdependencies of the PCFs.

Metro rail projects are influenced by project complexity, which significantly impacts their performance. The analysis reveals that “design problems with existing structures,” “change in design or construction” and “land acquisition” are the key factors contributing to project complexity.

The study of project complexity in metro rail projects is limited because most of the studies have studies on examining complexity in mega projects. The existing literature lacks adequate attention in identifying project complexity and its effects on metro rail project performance. This research aims to bridge this gap by examining project complexity and interdependencies in metro rail projects.

Defective parts in manufacturing is a serious issue faced by every manufacturer. Even after proper care in design, material selection and manufacturing of product, there exists a defective part. The purpose of this paper is to explore the quality of the manufacturing, and find the use of effective quality tools to reduce the part defect rate in an electrical parts manufacturing unit, thereby, reducing the replaced cost of defective parts.

With the help of quality initiatives, like total quality management (TQM) and Lean Six Sigma (LSS), the firms can produce quality product in each stage of production. The paper focuses on the primary data collected from the XYZ electric manufacturer.

The main finding of this case analysis is that by the effective use of quality tools, the defective part return rate can be reduced, because of which the firm can observe reduction in replaced cost of almost INR24 lakh. In addition, 10A switch part contributes more in replacement cost. Further, it adds to the 35 percent of the overall part rejection.

The study is more focused on particular type of switch product and can extend to other types of products. In addition, the analysis reveals the results of only 88 percent of the defective products.

The study provides results of the improved quality by effective use of quality tools and discusses the different types of defects in the electrical parts manufacturing. Introducing TQM and LSS to manufacturing can reduce the customer return rate to 1,300 parts per million (PPM) and even to 1,000 PPM in future.

The paper discusses the quality issues in the electrical manufacturer. Moreover, the case analysis briefs effective ways to improve the product quality and reduce the rejection rate.