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In this study, we develop a new paradigm, Circular Lean Six Sigma 4.0 (CLSS4.0) to promote manufacturing sustainability. This paper aims to provide a practical and holistic view of the drivers and barriers that can help companies design an integrated CLSS4.0 model. The paper is based on a qualitative exploratory study using multiple case studies within 12 Moroccan manufacturing firms conducted through semi-structured interviews with top executive managers. The results show that the drivers are related to expected operational and environmental performance, increasing customer requirements, gaining competitive advantage and market growth while barriers are related to insufficient tangible (finance, human and equipment) and intangible (skills and techniques) resources, data privacy, technical issues and management support. The proposed framework identifies the assessment of drivers and barriers and their attributes as a starting point for managers to lead the CLSS4.0 transformation, thereby contributing to its successful implementation. To the best of our knowledge, this study is among the very first studies to discuss the CLSS4.0 drivers and barriers. It could be useful to managers as a diagnostic tool to assess their ability to implement CLSS4.0 before investing in the initiative. [ABSTRACT FROM AUTHOR]

While Six Sigma is used in different disciplines to improve quality, Tony Badric and Elvar Theodorsson in a recent paper in CCLM have questioned Six Sigma application in medical laboratory concluding Six Sigma has provided no value to medical laboratory. In addition, the authors have expanded their criticism to Total Analytical Error (TAE) model and statistical quality control. To address their arguments, we have explained the basics of TAE model and Six Sigma and have shown the value of Six Sigma to medical laboratory. [ABSTRACT FROM AUTHOR]

The main purpose of this study is to investigate the role of Lean and Six Sigma initiatives on continuous quality improvement in the Malaysian Society for Quality in Health (MSQH) accredited hospitals. In particular, it investigates the relationship between top management support and teamwork. It also examines the influence of teamwork on Lean and Six Sigma initiatives. In this study, 450 survey questionnaires were distributed to twelve MSQH-accredited hospitals' staff using the Stratified Random Sampling method and received 251 useable responses constituting a 55.78 per cent response rate. The reliability and validity of the research variables were tested based on internal consistency, construct validity and discriminant validity by applying the SmartPLS 3.3.4 software. The relationships among various variables in the model were tested using Partial Least Squares Structural Equation Modeling (PLS-SEM). Overall, the study's findings reveal that Lean and Six Sigma initiatives positively and significantly impact the continuous quality improvement of MSQH-accredited hospitals. The findings also indicate that top management support has a positive and significant relationship with teamwork and vice versa, i.e. teamwork significantly affects Lean and Six Sigma initiatives. [ABSTRACT FROM AUTHOR]

The efficacious treatment of muscle and joint pain relies heavily on etofenamate (ETO) and benzyl nicotinate (BN), which possess robust anti‐inflammatory and pain‐relieving properties when paired with methylparaben (MP) or benzyl alcohol (BA). In this study, we have established and validated innovative RP‐UPLC methods for assessing ETO and BN in the presence of MP or BA in their dosage forms, employing eight green tools to evaluate their eco‐friendliness and effectiveness. Reversed phase‐ultra‐performance liquid chromatography (RP‐UPLC) technique employs a flow rate of 0.3 mL/min on Waters Acquity UPLC BEH Column (C18, 1.7 μm, 100 mm × 2.1 mm), detection at 254 nm using a photo diode array (PDA) detector and mobile phase of 0.05 M KH2PO4 buffer, acetonitrile, and methanol (50:15:35, v/v/v) adjusted pH 6.0 with 0.2% triethylamine. For ETO, BN, MP, and BA, the calibration curves were linear and ranged from 0.005 to 1.0, from 0.001 to 0.2, from 0.002 to 0.08, and from 0.0001 to 0.1 mg/mL, respectively. The correlation value was 0.9999, and the accuracy findings ranged from 98.81% to 100.56%. Consequently, the methodology has been successfully implemented in assay testing for the pharmaceuticals in the presence of the MP or BA, demonstrating the high selectivity of these approaches. The present study presents the Blue Applicability Grade Index (BAGI), an innovative approach that complements green metrics in practical white analytical chemistry. According to the International Council for Harmonisation (ICH) criteria, the procedures were effectively validated. [ABSTRACT FROM AUTHOR]

AbstractWhile traditional control charts play an important role in quality assurance activities, the work on such techniques evolved over the years particularly for early detection of process shifts. Many advanced level control charts have been proposed in the literature to even detect small shifts in the process as early as possible. In order to make use of the prior beliefs and likelihood of the observed data behavior, Bayesian control charts for parameter of the posterior distribution have been proposed by various authors. The Six Sigma-based control charts ensure smaller process variations by enforcing quality improvement activities and as a result, such processes would produce higher quality products with only 3.4 defects per million opportunities (DPMO) even after allowing a shift in the process up to ±1.5 times of process standard deviation. In this paper, an attempt has been made to develop Six Sigma-based Bayesian control charts. The proposed control charts incorporate various shift (or no-shift) combinations to prior and observed data behaviors to study their influence on the posterior parameter. Illustrative examples are also given for understanding the proposed approach. The performance evaluation and comparisons are also presented. The paper ends with a discussion and concluding remarks. [ABSTRACT FROM AUTHOR]

This study outlines the application of the Six Sigma methodology to rectify oil seal leaks in an automobile company's hollow drive shafts. Persistent issues with oil seepage through the shaft's oil seal led to clutch malfunctions, power failures, and subsequent breakdowns, resulting in high warranty costs, which negatively impacted the status of a firm in the market and the level of fulfillment experienced by its clients. Projects are crucial for translating an organization's strategic goals into achievable outcomes, serving as the cornerstone of success. We implemented a quality-driven effort using Six Sigma and the Theory of Inventive Problem Solving (TRIZ) to foster a culture of continuous improvement. Utilizing the procedures-oriented DMADV (Define, Measure, Analyze, Design, and Verify) technique, the oil seal leakage issue was addressed and resolved successfully from the gearbox. The Pugh matrix was used to choose a new design from various alternatives, a KANO model to evaluate customer demands, and a cause-and-effect diagram to analyze possible failure situations. The overall aim was to determine the failure situations that were most likely to occur. Through its deployment, we intended to improve both the product's dependability and the production process's efficiency. Following the design improvements, the defects observed in the assemblies dropped to 58, with no occurrences of oil seal leakage. It is plausible to conclude that these techniques can significantly enhance product quality. The study demonstrates the efficacy of Six Sigma methodologies in tackling intricate situations. [ABSTRACT FROM AUTHOR]

This study delves into the interplay between design strategies and their efficacy in harmonizing the functional and aesthetic aspects of industrial product design. It scrutinizes modern design tactics, delineating their features and benefits, and aims to forge a cohesive strategy that elevates both aesthetic appeal and functionality in industrial products. By examining literature on industrial design evolution, the merger of performance and aesthetics, and user experience, the research identifies five pivotal design strategies--Design Thinking, Agile Design, Lean Design, Six Sigma, and TRIZ theory--adopted by various industrial businesses. It utilizes qualitative methods to analyze these strategies' frameworks, pinpointing similarities and differences in their approach to design, alongside quantitative research through surveys with expert designers. The findings illuminate the distinct impacts of these strategies on industrial product development, offering insights into their potential to enhance both the utility and visual appeal of products. [ABSTRACT FROM AUTHOR]

The impact of Industry 4.0 (I4.0) and its enabling technologies in the Lean Six Sigma (LSS) approach have been widely discussed by practitioners and researchers in the last decade. However, current literature still lacks consistent knowledge about the joint application of I4.0, Artificial Intelligence (AI) and Augmented Reality (AR) in the context of LSS practices. It is expected that the symbiosis between these technologies and methods can generate new applications in the field of operational excellence. This study provides a theoretical landscape of how I4.0, AI and AR can boost digital LSS. A Systematic Literature Review (SLR) was conducted using articles published from 2011 to 2022 in the Web of Science and Scopus databases. In addition to the quantitative analysis performed with 313 selected articles, a thematic analysis covering 31 empirical works provides examples regarding how I4.0, AI and AR technologies can singularly support LSS practices. The SLR exposed trends on the research topics, the main thematic areas, and empirical evidence regarding how I4.0, AI and AR technologies have supported LSS practices. This study can help practitioners identifying new applications of I4.0 technologies and AI algorithms in order to expand the traditional toolbox and methods used in LSS. [ABSTRACT FROM AUTHOR]

Background: Central venous catheters (CVC) are used for dialysis in end-stage renal disease patients, presenting a significant risk for Catheter-Related Bloodstream Infections (CRBSI). While Lean Six Sigma has been effective in reducing CRBSI, its efficacy outside intensive care units (ICU) remains less explored. This study aims to evaluate the effectiveness of Lean Six Sigma in mitigating CRBSI risks among non-ICU hemodialysis patients. Methods: The study was conducted in a nephrology department, focusing on patients undergoing hemodialysis with temporary CVC from February to December 2021. The Lean Six Sigma method, using Define-Measure-Analyze-Improve-Control (DMAIC) methodology, was implemented in 2022 to reduce CRBSI incidence. The 2021 CRBSI rate served as the benchmark, with a goal to reduce it by the end of 2022. Value-stream mapping, Fishbone Diagrams, and Root Cause Analysis identified potential CRBSI causes. After implementing targeted improvements, CRBSI rates before and after the intervention were compared. Results: The Lean Six Sigma method significantly decreased CRBSI incidence from 12.79 to 2.32 per 1,000 catheter-days following the implementation of targeted interventions ( =4.60, P = 0.05). This improvement was observed comparing February-December 2021 with January-December 2022. Conclusion: The findings demonstrate the effectiveness of the Lean Six Sigma method in non-ICU settings, suggesting broader applicability in hemodialysis patient care. [ABSTRACT FROM AUTHOR]

Lean Six Sigma is a methodology that combines Lean manufacturing and Six Sigma into a single framework for process improvement. This combination merges their tools and techniques to overcome their deficiencies while achieving bottom-line improvement. However, existing literature emphasizes general LSS implementation with very little work on modeling the interaction between Leanness and Six Sigma. This work developed a multi-level model containing leanness and Six Sigma levels to generalize LSS implementation. Three hierarchical levels—Six Sigma (level 3), leanness (level 2), and a fattening level (level 1—Sigma quality level) are defined for two models (parametrized and non-parametrized). Aggregate measures (process efficiency, asset intensity, production time, etc.) are considered at levels 2 and 3 to achieve the desired optimization goal at that level. For testing, three process measures [asset intensity (AI), planned stoppages (PS), wastes and rework (WR)] from the manufacturing data of a global brand spanning 18 months were analyzed. The maximum log-likelihood method was used to estimate the model parameters and they converged to 85.2%, 11.2%, and 8.87% for AI, PS, and WR respectively, with a strong correlation between AI and PS. A metaheuristic algorithm was extended to solve both models. The obtained process yield (0.111111 and 0.4132471), defects per million opportunities (DPMO) (12,426 and 18,046), and sigma level (3.6 and 3.74) for the parametrized and non-parametrized models respectively indicated optimal model performance and a need for the strategic use of LSS tools in improving the production output. Also, further reduction in wastes, stoppages, and downtime was observed upon tuning the parametric variables. This model provides a mathematical approach to analyzing LSS implementation and provides a basis for future adaptation to any organization. [ABSTRACT FROM AUTHOR]

Process capability analysis is a valuable tool in quality assurance, but deviations from normal distribution necessitate adjustments to basic process capability indices. Process control literature offers solutions for non-normality, with data transformation being a common approach. The Box-Cox transformation (BCT) is often used to normalize non-normal data, relying on maximum likelihood estimation (MLE) to determine the transformation parameter, lambda. Alternative methods exist for estimating the single transformation parameter lamda, employing goodness-of-fit tests instead of the MLE method. This study explores two expressions within the Box-Cox transformation (BCT), encompassing both optimal and rounded values of lambda. The primary goal is to identify an effective method for transforming non-normal data into a distribution closer to normality through goodness-of-fit tests, aiming to obtain accurate estimates for process capability analysis in alignment with six sigma standards. Furthermore, this study focuses on the influence of utilizing both optimal and rounded values of lambda when transforming non-normal data to normal, and how these lambda values impact the estimates of process capability analysis. The findings reveal that methods such as Shapiro-Wilk's (SW) and Artificial Covariate (AC) outperform the MLE method. Moreover, employing the optimal lambda value during data transformation leads to improved estimates of process capability. Data simulation and analysis were conducted using Minitab software and the R programming language. [ABSTRACT FROM AUTHOR]

Objectives: This study aimed to evaluate the analytical performance of coagulation tests (prothrombin time (PT/INR), activated partial thromboplastin time (aPTT), fibrinogen, and D-dimer) using the Six Sigma methodology, focusing on identifying areas for improvement to enhance healthcare quality. Methods: Internal quality control (IQC) and external quality control (EQC) data from September 2023 to February 2024 were collected for coagulation parameters analyzed by four Cobas T711 analyzers. Sigma values were calculated using IQC, EQC, and total allowable error (TEa) data. The sigma value for each parameter was calculated according to the formula "(TEa%--bias %)/CV%." The outpatient sample analyzers were labeled A1, A2, and A3. The one that analyzes samples from emergency and intensive care patients was labeled B. Results: Across analyzers A1, A2, A3, and B, sigma values varied for different coagulation parameters. Notably, the D-dimer parameter consistently exhibited excellent performance (sigma >6) for all analyzers. In contrast, some analyzers showed poor performance for aPTT and PT parameters at level 1 (A1 and A3 for aPTT, B for INR). Fibrinogen performance varied, with some analyzers showing excellent performance (sigma >6) and others falling below acceptable levels. Conclusion: By identifying areas of low performance, particularly in aPTT and INR parameters, this study highlights the importance of continuous quality improvement in laboratory testing. Addressing issues identified through the Six Sigma methodology can enhance the reliability of laboratory results and ultimately improve patient care. Further research and initiatives focused on analytical process improvement are needed to achieve higher quality standards in laboratory testing. [ABSTRACT FROM AUTHOR]

Objectives: The Six Sigma methodology is also frequently used by clinical laboratories as an objective and quantitative way to measure quality. In our study, we aimed to evaluate the analytical performance of 80 tests using the Six Sigma methodology according to the CLIA (Clinical Laboratory Improvement Amendments) 2019, RICOS BV (Dr. Carmen RI-COS Biological Variation) Desirable, and EFLM BV (European Federation of Clinical Chemistry and Laboratory Medicine Biological Variation) Desirable criteria. Methods: The sigma values of 80 tests were calculated according to the TEa (Total Allowable Error) limits allowed by all three references using internal quality control and external quality control data. They were calculated monthly for 12 months, and the annual average was taken. Sigma values were calculated with the Six Sigma formula. Results: Considering the total number of goals reached, the highest success rate of 60% was achieved according to the CLIA goals, while the lowest success rate of 36% was obtained according to the EFLM BV Desirable criteria. Although exactly the same laboratory data are used, this gap between the sigma values obtained according to the selected reference is especially noticeable in tests such as Na (Sodium), K (Potassium), Cl (Chloride), Calcium, HbA1c (Hemoglobin A1c), and Troponin T. Conclusion: The Six Sigma protocol is one of the effective and universal tools for evaluating the performance of clinical laboratories. However, one of its biggest limitations is the lack of standardization in tolerance limits. The obtained performance varies according to the preferred reference. Therefore, we think that in the Six Sigma methodology, it is more feasible to select Total Allowable Error criteria from different references according to their suitability for the test. [ABSTRACT FROM AUTHOR]

Project bundling is an innovative project-delivery approach that bundles and groups different infrastructure projects into a single contract. Project bundling provides numerous opportunities and benefits for agencies and states while maintaining, rehabilitating, and repairing their infrastructure assets. Although previous research provided insights on some aspects of project bundling, none have identified the success factors needed to enhance the performance of bundled projects after the project bundling decision has been made. This study aims to fill this gap through identifying, studying, and prioritizing the success factors needed to efficiently bundle several infrastructure projects together. First, a survey was developed and distributed to numerous experts to examine the likelihood of occurrence and relative impact of 25 success factors. Second, the reliability of the survey and its results were assessed and validated using internal and external reliability assessment measures. Third, various statistical analysis tests were performed to examine the alignment between the perspectives of different project stakeholders in relation to the various project-bundling success factors. Fourth, the criticality of the different project-bundling success factors was quantified. Fifth, the success factors were ranked and prioritized to identify the key and most critical factors. The results showed that the most critical project-bundling success factors are (1) having well-defined design features; (2) having a complete project design prior to construction; (3) requiring prior knowledge or experience with similar project size and scope (by the contractor and the design agency); (4) having clear design control requirements; and (5) maintaining constant coordination and communication between all project stakeholders. This study adds to the body of knowledge by quantifying and studying the criticality of different success factors needed to enhance the performance of bundled projects. This study also provides decision-makers with the required knowledge and guidance on the key success factors that influence project-bundling performance. [ABSTRACT FROM AUTHOR]

Lean methodologies, a system widely used in the manufacturing industry, have demonstrated numerous benefits and added significant value to the industry. In the same way that other industries have benefitted from the application of Lean techniques, the engineering projects sector can also realise significant improvements by integrating Lean principles into its processes and operations. The study aims to gain a comprehensive insight into the application of the Lean concept, with a focus on the South African engineering projects sector. The findings of a thorough literature review, focussing on Lean techniques, technology, and the projects sector, examined key variables that have an impact on the effective implementation of Lean methodologies within this industry. The variables studied are skills and expertise, active work methods, and leadership style. The study also explores the impact of digital technologies on Lean implementation, exploring how these technologies enhance the integration of Lean practices. The study includes descriptive statistical analysis, exploratory factor analysis, and a confirmatory factor analysis of the data. A significant relationship is found between variables influencing Lean implementation and the effectiveness of Lean practices within the South African engineering projects sector. [ABSTRACT FROM AUTHOR]

Purpose: The paper aims to identify the critical constraints that impede heritage building (HB) facility managers from discharging their duties effectively and develop an index model to guide HB maintenance management (HBMM) practitioners to the critical constraints. Design/methodology/approach: A literature review was conducted to identify HBMM constraints. Facilty management practitioners assessed the constraints' significance through an online survey. The factor analysis was used to shortlist and group the constraints, and the constraint clusters were analyzed by the fuzzy synthetic evaluation technique. A significant index cluster to determine HBMM constraints criticality was generated using the linear additive model. Findings: Embracing a total of 16 HBMM constraints, the three clusters identified are: (1) managerial and inadequacy constraints, (2) pressure and bureaucracy constraints and (3) HB peculiarities constraints. Based on the generated significant index, the HB peculiarities cluster was identified as the most significant. Research limitations/implications: The study was conducted in a particular jurisdiction, limiting the generalizability of the result. Future research should address this limitation by covering more jurisdictions. Practical implications: The significant index model (SIM) developed enables HBMM practitioners to objectively assess the criticality of HB constraints and facilitates them to effectively strategize and allocate resources for HBMM. Originality/value: The SIM, which transforms subjective judgment into the objective assessment of the HBMM constraints' criticality, can assist practitioners, policymakers and other HBMM stakeholders in implementing strategies for the sustainability of HBs. [ABSTRACT FROM AUTHOR]

The article presents the discussion on critical role of measurement in science, engineering, and conformity assessment, highlighting its importance in generating valid test and inspection reports. Topics include the relationship between measurement error and uncertainty, the three categories of measurement errors; and the significance of the ISO/IEC 17025 standard in ensuring the competency of testing and calibration laboratories.

The article is a book review of "Customer-Driven Project Management: Building Quality into Project Processes, 2nd Ed." by Bruce T. Barkley and James H. Saylor. The book emphasizes the importance of placing the customer at the center of projects and businesses in the digital era. It provides a comprehensive guide for implementing customer-driven project management, including building quality into project processes and tools, and offers insights on continuous improvement and leadership development. The reviewer highlights the value of the book's case studies and the eight-step process for customer-driven project management. The book is recommended for project managers, business leaders, and anyone involved in project planning. [Extracted from the article]

Parts with a large depth-to-diameter ratio play a critical role in the military, aerospace, and automotive industries. However, accurately measuring their inner surface profile remains challenging owing to the lack of adequate and accurate sensors. We developed a multi-ring structured light system to obtain three-dimensional data of inner contours, such as inner diameters, which are crucial for ensuring component performance and safety. In this study, we proposed three simple yet effective techniques to improve the multi-ring beam structured measurement system. First, we designed a distortion correction method to calibrate the imaging system. Second, a two-step calibration approach was used to calibrate the multi-ring projection. Meanwhile, we benchmarked the effects of different light sources on image speckles. The calibration results demonstrated that the coefficient of determination (R-2) used for line fitting exceeded 0.999. Moreover, the measurement experimental results show that the uncertainty of less than 10 µm and the smallest measurable pipe inner diameter can reach 15 mm, demonstrating that our methods are promising for improving the accuracy of structured light optical sensing systems. This system satisfies the measurement requirements and can be immediately utilized to meet the high demand for inner contour measurements in industrial applications. [ABSTRACT FROM AUTHOR]

Purpose: Green, lean and six sigma (GLSS) practices are widely used and well accepted techniques that have the capability to improve the economic, social and environmental performance of Pakistani small and medium enterprises (SMEs). However, implementation of these practices in an integrated approach has not yet been witnessed in Pakistani SMEs due to a variety of challenges. To overcome the implementation challenges, this study has analyzed the impact of GLSS implementation over the sustainability in Pakistani SMEs. Design/methodology/approach: This study consists of two phases. In phase 1, interviews were conducted to scrutinize the elements of GLSS implementation to simplify the model and in phase 2, a questionnaire survey was conducted to collect the data from the SMEs. The partial least squares structural equation modeling (PLS-SEM) approach is used to analyze the relationships among the latent variables and constructs. Findings: Results showed that, leadership for GLSS, understanding GLSS techniques and technology upgradation are considered the most important elements for GLSS implementation in Pakistani SMEs. The environmental and social perspectives have been given more weightage compared to economical perspective. This inferred that there is a need to focus more on environmental and social perspectives in SMEs as compared to economic perspectives to achieve sustainable growth. Moreover, the results of the hypothesis testing revealed that GLSS implementation has a significant positive impact over the sustainability in SMEs considering the Pakistani scenario (β = 0.529, STDEV = 0.078, t = 6.81, p = < 0.001). Originality/value: This study is the first of its kind for Pakistani SMEs. The structural model developed in this study for Pakistani SMEs will help practitioners to understand the important elements of GLSS implementation and sustainability dimensions. [ABSTRACT FROM AUTHOR]

This study offers an innovative solution to address performance issues in the manufacturing process of garlic salt within a condiment-producing SME. A hybrid Lean/Six Sigma model utilizing a Surface Tension Neural Network (STNN) was implemented to control temperature and relative humidity in real-time. The model follows the Define, Measure, Analyze, Improve, Control (DMAIC) methodology to identify root causes and correlate them with waste. By integrating statistical tools, artificial intelligence, and engineering design principles, alternative solutions were evaluated to minimize waste. This document contributes to existing knowledge by demonstrating the integration of an STNN with the Lean/Six Sigma framework in condiment production, an area with limited empirical research. It underscores the benefits of advanced AI technologies in enhancing traditional process optimization methods. The STNN model achieved 97.31% accuracy for temperature classification and 97.37% for humidity, outperforming a Naive Bayes model, which attained 90% accuracy for both. The results showed a 3.15% increase in yield, saving 39.7 kg of waste per batch. Additionally, a 2.13-point improvement at the Six Sigma level was achieved, reducing defects per million opportunities by 551.722. These improvements resulted in significant cost savings, with a reduction in waste-related losses amounting to USD 1585 per batch. The study demonstrates that incorporating artificial intelligence into the Lean/Six Sigma methodology effectively addresses the limitations of traditional statistical methods. Significant improvements in yield and waste reduction highlight the potential of this approach, enhancing operational efficiency and profitability, and fostering sustainable manufacturing practices critical for SMEs' competitiveness and sustainability in the global market. [ABSTRACT FROM AUTHOR]

Purpose: This project aims to optimise a secondary agricultural company's reporting and data lifecycle by providing self-help business intelligence at an optimal price point for all business users. Design/methodology/approach: A design for Lean Six Sigma approach utilising the define, measure analyse, design and verify methodology was utilised to design a new reporting and data product lifecycle. Findings: The study found that this approach allowed a very structured delivery of a complex program. The various tools used assisted greatly in delivering results while balancing the needs of the team. Practical implications: This study demonstrates how improving data analysis and enhanced intelligence reporting in agribusinesses enable better decision making and thus improves efficiencies so that the agribusiness can leverage the learnings. Social implications: Improving data analysis increases efficiency and reduces agrifood food wastage thus improving sustainability and environmental impacts. Originality/value: This paper proposes creating a standardised approach to deploying Six Sigma methodology to correct both the data provisioning lifecycle and the subsequent business intelligence reporting lifecycle. It is the first study to look at process optimisation across the agricultural industry's entire data and business intelligence lifecycle. [ABSTRACT FROM AUTHOR]

The company has problems in production, one of which is that there is defective packaging on the product. The product that became the object of research was Packaging of Product MSR, which was most often returned from distributors because it did not comply with the specified quality with an average percentage of 1.08%. This study aims to identify the factors that cause defects in MSR products and provide suggestions for improvements to minimize defects and improve the quality of the production process. Quality improvement is carried out using the Six Sigma method with the stages of Define, Measure, Analyze, Improve, and Control and the Fuzzy Analytical Hierarchy Process. the DPMO value obtained was 8211.43 with a sigma level of 3.899. In the Failure Mode Effect Analysis calculation, the four highest causes were found namely packaging that did not meet the requirements, the sealer machine was too hot, rice buildup during storage and the printing machine was damaged. The best alternative is determined using the Fuzzy Analytical Hierarchy Process with the criteria of cost, materials, maintenance and storage. The selected improvement proposals are the creation of a form for routine inspection, cleaning of packaging machines and creating an alarm for every 730 products, and manufacturing of shelves for product storage.

This paper aims to present improvement actions to reduce the failure risk of a new vehicle cabin air transporter developed in an automotive plant in the Sul Fluminense region by applying the Failure Modes and Effect Analysis (FMEA) tool. This analysis method enables the equipment to reduce some existing problems. Thus, it was possible to increase the demand for vehicles, expected by the end of the year 2018. For this purpose, a team was formed with employees from the main areas involved who raised the main failure modes of the equipment through the brainstorming technique. The FMEA worksheet was filled using the severity, occurrence, and detection classification tables to calculate the Risk Priority Number (RPR). Then, it was suggested to apply the technique with the necessary improvement actions for the failure modes, broken down in a 5W1H worksheet. In a conclusion, the importance of applying the FMEA before acquiring new equipment was demonstrated, avoiding waste, and guaranteeing the company's efficiency.

In this paper, we analyze the literature concerning the implementation of digital twins (DTs) for zero-defect manufacturing (ZDM) following a systematic method and, guided by a preliminary finding that a structured and standardised approach to the development of the DT applications is lacking, we provide a standardised design methodology to guide researchers and practitioners in their efforts to develop DTs regardless of the domain. After examination and interpretation of the literature, we also present the results of our state-of-the-art analysis, discuss the current state and limitations of research and practice, and provide useful insights on this important and complex topic. The design methodology proposed in our study will benefit both practitioners and academicians by covering the essential elements to be considered when developing DTs for ZDM for any applications in this domain. The study also contributes to knowledge by presenting a structured overview of the specific research area with a comprehensive, systematic, and critical analysis of the literature and by providing answers to some fundamental questions in the context of DTs for ZDM. Finally, we provide suggestions for further developments in research and practice. [ABSTRACT FROM AUTHOR]

Purpose: Many organizations currently transition towards digitalized process design, execution, control, assurance and improvement, and the purpose of this research is to empirically demonstrate how data-based operational excellence techniques are useful in digitalized environments by means of the optimization of a robotic process automation deployment. Design/methodology/approach: An interpretive mixed-method case study approach comprising both secondary Lean Six Sigma (LSS) project data together with participant-as-observer archival observations is applied. A case report, comprising per DMAIC phase (1) the objectives, (2) the main deliverables, (3) the results and (4) the key actions leading to achieving the presented results is presented. Findings: Key findings comprise (1) the importance of understanding how to acquire and prepare large system generated data and (2) the need for better large system-generated database validation mechanisms. Finally (3) the importance of process contextual understanding of the LSS project lead is emphasized, together with (4) the need for LSS foundational curriculum developments in order to be effective in digitalized environments. Originality/value: This study provides a rich prescriptive demonstration of LSS methodology implementation for RPA deployment improvement, and is one of the few empirical demonstrations of LSS based problem solving methodology in industry 4.0 contexts. [ABSTRACT FROM AUTHOR]

Globally, the textile industry plays a significant role in production but faces many challenges that affect its operation and sustainability. Product quality remains an essential concern despite technological advancements and quality control measures. Raw materials, production methods, and skills differ, resulting in customer dissatisfaction and damage to reputation. A quality management strategy is necessary to overcome these problems and promote quality improvement. This includes the implementation of the Lean Six Sigma approach. This article focuses on improving textile quality by developing the Six Sigma DMAIC method. An analysis of three months' data from a textile company shows that an increase in the Outgoing Quality Level (OQL) from 3% to 6% resulted in significant economic losses, amounting to PKR-240,000 due to rework, residual costs, customer returns, and lost sales time. By utilizing Six Sigma tools, the company reduced OQL from 6% to 4%, thus lowering product losses and increasing operational efficiency. As a result of these findings, the textile industry can improve quality and profitability through a Lean Six Sigma approach.

Quality control is intended to ensure that the products are in accordance with the predefined standards. PT. XYZ is a garment company that manufactures products with global target market. Hence, product quality assurance becomes an important issue for PT. XYZ. This research focuses on the Just Brand-MCJA216142 jacket product at the sewing work station on line-4 of PT. XYZ. Preliminary observations show that the number of reworked-products was experiencing an increasing trend. This study aims to determine whether the company has carried out quality control properly. Specifically, the research objectives are to identify the type and level of product defects, identify the factors causing product defects, and provide proper improvement suggestions to reduce the occurrence of product defects. This study applied DMAIC (Define–Measure–Analyze–Improve–Control) Six Sigma concept. The results showed that the quality of the Just Brand-MCJA216142 jacket product has exceeded the Indonesian industry average and is classified as the USA industry average. However, quality improvement is still needed since the products are targeted for the export market. Based on Pareto diagram at the Analyze stage, it was found that the most dominant defects occurred in the Just Brand-MCJA216142 jacket production process were broken threads and puckering. The frequency of occurrence for these two types of defect reaches 23% of the total 16 types of defects. The defects were caused by human, machine, material, method, and environmental factors. Recommendations for improvement at the Improve stage are based on root cause analysis of each causative factor that is identified using a fishbone diagram. This research results strengthen the previous related researches regarding the effectivity of DMAIC Six Sigma for analyzing quality control of products.

Background: The application of the Six Sigma (σ) metric in biochemical laboratories is a powerful tool for reducing the occurrence of errors and prioritizing important improvements in laboratory quality control. The National Accreditation Board for Testing and Calibration Laboratories (NABL) is an accreditation body with an accreditation system established in accordance with ISO/IEC 17011, providing specialty- or scope-based certification based on the conformance of quality indices to medical laboratories. In this context, a study has been designed that considers the quality guidelines set by NABL as well as the sigma metric rule in the assessment of analytical performance. Aims and Objectives: The aims of this study were to identify the gaps and need for strategy modification for quality improvement by assessing the performance of two NABL-accredited medical testing laboratories on a Sigma metric scale. Materials and Methods: A retrospective analytical study was conducted over 6 months (January–June 2021). Internal quality control (IQC) and EQAS data were obtained from third-party QC providers (Bio-Rad, India) and analyzed by calculating sigma (σ) values based on the coefficient of variation, bias, and total error allowable in two NABL-accredited medical testing laboratories. To identify potential problems for analytes with poor sigma values, a quality goal index (QGI) analysis was performed. Results: By analyzing the sigma values obtained by both NABL-accredited laboratories, we can see that laboratory 2 performed better than laboratory 1. After calculating the QGI, there was a problem of inaccuracy and imprecision in laboratory 1, and laboratory 2 had QGI values that indicated only imprecision. Conclusion: Diagnostic laboratories should incorporate Six Sigma metrics to identify gaps in their performance to ensure better quality control and patient safety.

Printing as a process itself generates many environmental concerns. The paper addresses ink management in terms of environmental issues in the label printing industry, focusing on its environmental implications. The goal is to demonstrate how a proper ink management system impacts overall printing process efficiency and environmental sustainability for printing companies. The paper introduces an empirical approach to managing components for label and packaging production, utilizing automatic ink dispensing systems. The results demonstrate that the proper management of ink dispensing to minimize waste in packaging printing is crucial for optimizing operating print costs, potentially reducing the amount of ink needed to prepare colors by 52% and achieving energy savings of 37%. This approach fulfills the goal of sustainability by addressing environmental, economic, and social concerns. By optimizing ink usage and energy consumption, companies can significantly reduce operating costs and enhance economic performance. Simultaneously, these practices improve product quality, meet consumer demands for sustainable packaging, and create better working conditions for employees. Future directions and practical implications for supporting operational excellence in production are also discussed. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this study is to present the systematic literature review (SLR) on Lean Six Sigma (LSS) by exploring the state of the art on growth of literature on LSS within the manufacturing sector, critical factors to implement LSS, the role of LSS in the manufacturing sector from an implementation and sustainability viewpoint and Industry 4.0 viewpoints while highlighting the research gaps. Design/methodology/approach: An SLR of 2,876 published articles extracted from Scopus, WoS, Emerald Insight, IEEE Xplore, Taylor & Francis, Springer and Inderscience databases was carried out following the protocol of systematic review. In total, 154 articles published in different journals over the past 10 years were selected for quantitative and qualitative analysis which revealed a number of research gaps. Findings: The findings of the SLR revealed the growth of literature on LSS within the manufacturing sector. The review also highlighted the most cited critical success factors, critical failure factors, performance indicators and associated tools and techniques applied during LSS implementation. The review also focused on studies related to LSS and sustainability viewpoint and LSS and Industry 4.0 viewpoints. Practical implications: The findings of this SLR can help senior managers, practitioners and researchers to understand the current developments and future requirements to adopt LSS in manufacturing sectors from sustainability and Industry 4.0 viewpoints. Originality/value: Academic publications in the context of the role of LSS in various research streams are sparse, and to the best of the authors' knowledge, this paper is one of the first SLRs which explore current developments and future requirements to implement LSS from sustainability and Industry 4.0 perspective. [ABSTRACT FROM AUTHOR]

Purpose: This research aims to conduct a literature review of Lean Six Sigma (LSS) in the Indian context and related research publications and apply bibliometric analysis and the author's visualization to map research trends in this area. Design/methodology/approach: This article conducts a bibliometric analysis of LSS-related research in the Indian context using data gathered from Scopus and Web of Science databases from 2011 to 2022. The review provides information on LSS-related research in the Indian context and evaluates performance based on primary sources, authors, keywords, countries, affiliations, and documents. The analysis employs the Biblioshiny app and Bibliometrix R-tool for data analysis and scientific mapping. Findings: The results of the bibliometric analysis indicate that the LSS culture has widely spread in India. The International Journal of Lean Six Sigma and Production Planning and Control were found to be the most productive sources for publishing LSS-related research articles. Antony J. was identified as the most active author in this field, contributing the most over the years. Among all organizations, NITs have conducted the most comprehensive research on LSS, indicating their significant investment of resources and efforts in studying this methodology and its applications in India. Additionally, the study examined the intellectual, social, and conceptual structures to identify implicit gaps and future research opportunities. Practical implications: The findings of this study can inform academicians, researchers, practitioners, and policymakers about the state-of-the-art and the specifics of the most prolific studies. This study will facilitate their exploration of emerging research areas in LSS. Originality/value: To the best of the authors knowledge, this is the first bibliometric analysis of LSS in the Indian context, providing an overview of relevant publications published between 2011 and 2022. This study analyzed 194 articles on LSS in India, which can help researchers and academics identify emerging research areas, suitable collaborators, and relevant journals for future publications. [ABSTRACT FROM AUTHOR]

Purpose: This study aims to present the results of a comparative study on the most key reasons for the failure of sustaining activities of operational improvement (OI) methodologies from the different types of manufacturing enterprises that located in Vietnam. Design/methodology/approach: This study presents survey results from 30 local manufacturing enterprises and 21 foreign direct investment (FDI) enterprises located in Vietnam – a developing country. The authors utilize a combination method to collect data, including online and direct survey. The targeted interviewees selected to answer the questionnaire are manufacturing managers and top managers working in productivity, quality, engineering and other departments in respective firms. The developed questionnaire is verified by five experts to ensure the validity and soundness. Findings: The study uncover that 21 local enterprises (70%) have not issued standard forms for OI deployment and supervision, while 17 foreign enterprises (81%) have adopted a systematic management and clearly indicators for evaluation of OI outputs. In addition, the top three reasons for OI failure are differences between local and foreign enterprises. In term of OI methodologies, most of participated enterprises implemented Lean tools and principles while only 7.8% of the participated enterprises applied Six Sigma. Three vital findings are uncovered, including, first, 30% of local enterprises standardize and supervise forms of OI. Research limitations/implications: There is a limitation in sample size, with the number of participants of 51 enterprises. Among the participants, 27% of local enterprises are small and medium-sized enterprises (SMEs) while 60% of FDI enterprises from large-sized group. This was limited to making a generated conclusion in the comparison of failure factors between two types of enterprises. This partly affects the radical comparison of failure factors between local and FDI enterprises. Originality/value: To the best of the authors' view, this is the first empirical study that compares the reasons for the failure for sustaining OI between local and foreign enterprises in a developing country. The result from this study will make contributions for further research in considering OI failure factors and then enhance effectiveness of OI methodologies in manufacturing companies. [ABSTRACT FROM AUTHOR]

Purpose: New product development (NPD) is necessary for business sustenance and customer satisfaction. Six Sigma and Design for Lean Six Sigma (DLSS) efficiently employ the repetitive stages for NPD, leading to quality performance and profitability. This study aims to map the quality performance through NPD attributes through the Lean methodology. Design/methodology/approach: The data on NPD were collected from 267 respondents from manufacturing companies to map the relationship between Six Sigma and DLSS for NPD. Confirmatory factor analysis was employed to confirm model fit, while structural equation modeling was employed to analyze the empirical data for framework testing. The study included nine variables and fourteen hypotheses identified from the literature. Findings: The statistical results of this study show that NPD attributes such as innovation, marketing, organization, customer, product and technology positively influence the Lean Six Sigma structured improvement process (LSSSIP) and DLSS. Moreover, integrating these attributes in Lean planning enhance quality performance. This empirical investigation's findings indicate that ten of the 14 hypotheses were supported, giving the study a strong foundation. Research limitations/implications: The data collection was limited to northern India; therefore, the results may not be generalizable to other areas of the world. Practical implications: NPD involves handling technical issues and factors such as cost, operational bottlenecks, economic changes, competitors' strategy and company policy. This study helps understand the various NPD parameters and their relationship to Lean, which enables an effective NPD implementation strategy. Originality/value: The current philosophy of NPD calls for a concurrent engineering approach; therefore, the entire organization must be part of this process. This study uses the holistic framework by optimizing NPD with Lean Six Sigma (LSS) principles. The study is unique in that, to date, research does not integrate NPD attributes with the objectives of LSS to develop an efficient NPD implementation strategy. [ABSTRACT FROM AUTHOR]

In a globalizing world, the borders on the map have started to disappear economically and customers in the world economy expect high levels of quality from their suppliers. Only companies that can respond to customer expectations in the most accurate way will be able to survive in the future. One of the main goals of today's companies is to offer the best quality product at the most affordable price in line with the needs of customers. At this point, the Six Sigma approach is one of the most effective methods that companies can try. In this study, by applying the Six Sigma methodology, the root causes of PE (Polyethylene) film breaks in plastic bags were investigated by taking plastic bag production machines, processes and control steps as an example, necessary measurements were made by considering the production lines in a sample company and improvements were made in the production process by analyzing with Minitab program. At the end of the study, 24.012 TL/year savings were achieved by reducing downtime due to film breaks in the PE Film line, and 13.938 TL/year savings were achieved financially by reducing the amount of waste. In this way, the plastic bag industry benefited. In six sigma projects, it has been determined once again that the only element that is as important as making the change is that all the personnel of the company should keep up with and support this change. [ABSTRACT FROM AUTHOR]

Outsourcing manufacturing to a contract manufacturer is a growing practice that has gained global prominence since the turn of the century. This business practice is common in pharmaceutical and electronics industries, and has now also become common practice in consumer product supply chains. This practice has many challenges; in most cases it does not achieve its intended objectives, with a number of contract manufacturing companies (CMCs) in South Africa going out of business. Research and the literature on contract manufacturing is predominantly focused on the aspects of contract manufacturing and its role and impact in the supply chain. However, there are very few, if any, scholarly articles on the factors that make CMCs in the consumer products supply chain in South Africa succeed and become sustainable businesses and strategic partners to their brand owner customers. This study aimed to fill this gap by identifying, through empirical research of experts in contract manufacturing, the factors that are critical for contract manufacturers to succeed and to be sustainable businesses that can contribute to the success of their clients. Several critical success factors were identified using a Delphi survey of experts who had experience working with CMCs. [ABSTRACT FROM AUTHOR]

The rapid progression of emerging technologies like the Internet of Things (IoT) and Big Data analytics for manufacturing has driven innovation across various industries worldwide. Production data are utilized to construct a model for quality evaluation and analysis applicable to components processed by machine tools, ensuring process quality for critical components and final product quality for the machine tools. Machine tool parts often encompass several quality characteristics concurrently, categorized into three types: smaller-the-better, larger-the-better, and nominal-the-better. In this paper, an evaluation index for the nominal-the-better quality characteristic was segmented into two single-sided Six Sigma quality indexes. Furthermore, the process quality of the entire component product was assessed by n single-sided Six Sigma quality indexes. According to numerous studies, machine tool manufacturers conventionally base their decisions on small sample sizes (n), considering timeliness and costs. However, this often leads to inconsistent evaluation results due to significant sampling errors. Therefore, this paper established fuzzy testing rules using the confidence intervals of the q single-sided Six Sigma quality indices, serving as the fuzzy quality evaluation model for components of machine tools. [ABSTRACT FROM AUTHOR]

The scheduling of prefabricated component production is crucial for production efficiency and timely delivery in off-site construction (OSC). However, the prevalence of mixed-flow production in prefabrication renders conventional scheduling methods inadequate, leading to cost overruns and waiting issues. This study introduces a lean-based mixed-flow production scheduling approach to address the scheduling challenges in mixed-flow prefabrication. Guided by lean principles, this research analyzes the mixed-flow production process and process wastes, resolving issues of group production, pull production, and overall scheduling enhancement through multiobjective optimization modeling. A nondominated sorting genetic algorithm II (NSGA-II) based solution algorithm was established to minimize completion times, tardiness/earliness penalty, and type changes, thereby eliminating waste and ensuring continuous production. The effectiveness of the proposed approach was validated through a real case study, showing an average 1.84% reduction in makespan, 24.19% cost savings, and 36.03% reduction in waiting times compared to conventional nonlean optimization methods, while demonstrating good stability and reliability. This study innovates in engineering scheduling management and contributes to the development of lean construction theory, providing concrete support for component suppliers' successful delivery and promoting further development in OSC. [ABSTRACT FROM AUTHOR]

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Copyright of Automobile Technology & Material is the property of Automobile Technology & Material Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This article examines the various aspects of innovation, such as culture, advanced analytics, and creativity. A survey conducted by Firefly Consulting found that while many executives believe their organizations are proficient in developing innovative products and services, they also recognize the need for improvement. The article emphasizes the importance of cultivating an innovative culture that promotes curiosity, creativity, and the freedom to take risks. It also discusses the role of advanced analytics in driving innovation, while acknowledging the challenges of data availability and reliability. Furthermore, the article suggests that allocating time for exploration and contemplation can enhance creativity. It addresses the difficulties faced by employees in large organizations who spend a significant amount of time on emails, meetings, and tasks unrelated to their job responsibilities. The article underscores the significance of creating space for creative thinking and exploring non-traditional approaches to achieve business objectives. It also mentions the potential of artificial intelligence (AI) in generating new ideas and outlines the benefits of embracing AI chatbots for brainstorming. Overall, the article underscores the importance of fostering an innovative culture and leveraging data and unconventional thinking to gain a competitive edge. [Extracted from the article]

This article explores a more streamlined approach to Failure Mode and Effects Analysis (FMEA) to enhance efficiency for smaller issues. FMEA is a method used to identify and prioritize potential failures in a system, process, or design. The traditional FMEA practice has limitations, such as a one-size-fits-all format that can lead to inefficiency and a lack of assessment of return on investment. One challenge in conducting FMEA is the lack of data to support meaningful risk scoring. The article suggests that in situations where in-depth assessment is not feasible, alternative approaches or customizations can be considered to reduce the burden and cost. The article emphasizes the importance of considering return on investment when making business decisions, even if the risk priority number is low. It proposes using simpler risk analysis practices when data is limited or resources are constrained, such as a three-level scale instead of the traditional 10-level scale for risk assessment. The article acknowledges potential resistance to this approach but argues that it can be more efficient and effective. The concept of tiered triage, where initial risk assessment is conducted using a simpler technique and then transitioning to traditional practices for post-action assessment, is also discussed. [Extracted from the article]

Purpose: The exact process of construction projects performance assessment and benchmarking still remains subjective relying on qualitative techniques, which does not allow stakeholders to address the issues and the drawbacks of their respective projects as effectively as possible for performance improvement purposes. Hence, this research aims to establish a unified project performance score (PPS) for assessing and comparing projects performance. Design/methodology/approach: Data were collected from Construction Industry Institute (CII) members and through University of Wisconsin active research projects. Exploratory data analysis was done to investigate the calculated performance metrics and the collected data characteristics. Data were converted into six performance metrics which were used as the independent variables in creating the PPS model. Logistic regression model was developed to generate the unified PPS equation in order to explain the variables that significantly affect construction projects successful post-completion performance. The PPS model was then applied on the collected dataset to benchmark projects in terms of project delivery systems, compensation types and project types in order to showcase the PPS capabilities and possible applications. Findings: The model revealed that construction cost and schedule growth are the most important metrics in assessing projects performance, while RFIs' processing time and change orders per million dollars were the features with the least effect on the PPS value. The authors found that integrated project delivery (IPD) and target value (TV) projects outperformed all other project delivery and compensation types. While, industrial projects showed the worst performance, as compared to commercial or institutional projects. Originality/value: The PPS model can be used to assess the performance of any pool of executed projects, and introducing a novel addition to the field of construction business analytics which is a supplementary tool to successful decision making and performance improvement. Additionally, the bidding selection system can be revolutionized from a cost-based to a performance based one using the PPS model to improve the outcomes of the buyout process. [ABSTRACT FROM AUTHOR]