Predictive Six Sigma for Turkish manufacturers: utilization of machine learning tools in DMAIC

PurposeThis study aims to emphasize utilization of Predictive Six Sigma to achieve process improvements based on machine learning (ML) techniques embedded in define, measure, analyze, improve, control (DMAIC). With this aim, this study presents selection and utilization of ML techniques, including multiple linear regression (MLR), artificial neural network (ANN), random forests (RF), gradient boosting machines (GBM) and k-nearest neighbors (k-NN) in the analyze and improve phases of Six Sigma DMAIC.Design/methodology/approachA data set containing 320 observations with nine input and one output variables is used. To achieve the objective which was to decrease the number of fabric defects, five ML techniques were compared in terms of prediction performance and best tools were selected. Next, most important causes of defects were determined via these tools. Finally, parameter optimization was conducted for minimum number of defects.FindingsAmong five ML tools, ANN, GBM and RF are found to be the best predictors. Out of nine potential causes, "machine speed” and "fabric width” are determined as the most important variables by using these tools. Then, optimum values for "machine speed” and "fabric width” for fabric defect minimization are determined both via regression response optimizer and ANN surface optimization. Ultimately, average defect number was decreased from 13/roll to 3/roll, which is a considerable decrease attained through utilization of ML techniques in Six Sigma.Originality/valueAddressing an important gap in Six Sigma literature, in this study, certain ML techniques (i.e. MLR, ANN, RF, GBM and k-NN) are compared and the ones possessing best performances are used in the analyze and improve phases of Six Sigma DMAIC.

A case study for surgical mask production during the COVID-19 pandemic: continuous improvement with Kaizen and 5S applications

PurposeWith the emergence of the COVID-19 pandemic, the production shortage of personal protective equipment (PPE), such as surgical masks, has become increasingly significant. It is vital to quickly provide high-quality, hygienic PPE during pandemic periods. This comprehensive case study aims to confirm that Kaizen and 5S applications reduce wastage rates and stoppages, which as a result, created a more efficient and sustainable workplace in a small–mediumenterprise (SME) producing PPE in Turkey.Design/methodology/approachThe method for this case is discussed with the help of a flowchart using the DMAIC cycle: D-define, M-measure, A-analyse, I-improve and C-control.FindingsThe total stoppages due to fishing line, gripper, piston and yarn welding have decreased by approximately 42.4%. As a result of eliminating wasted time and reduced changeovers, a total of 5,502 min have been saved per month. This increased production of approximately 10.55% per month, led to an addition of 506,184 units.Originality/valueThe use of lean manufacturing (LM), Six Sigma, Lean Six Sigma and continuous improvement methodologies are not common in textile SMEs. Based on the current literature reviewed, to the best of the authors' knowledge, this is the first comprehensive case study that combines statistical tools, such as hypothesis tests and LM practices, in the production process for a PPE company operating as a textile SME.

SHARPENING UP WITH CERTIFICATION

Table 1 shows, for example, that respondents with a job title of auditor who hold the ASQ quality auditor certification earn a beefy 16.1% more, on average, than those without that certification. Table 1 shows the power of a close match between one's job title, with its attendant duties, and an ASQ certification. U.S. and Canadian respondents with a job title of manager who are ASQ-certified Six Sigma Black Belts earn a beefy 16.1% more, on average, than those without that certification.

BELT COLOR COUNTS

Table 2 breaks down salaries by Six Sigma Belt and job title for full-time professionals in the United States and Canada viewed together. The table shows that even when viewed by individual job title, pay strongly tends to increase with higher levels of Six Sigma credentials. BRUSH UP ON SIX SIGMA ASQ offers a range of Six Belt, Black Belt and learning options.

Application of six sigma and the system thinking approach in COVID-19 operation management: a case study of the victorian aged care response centre (VACRC) in Australia

COVID-19 has posed many unique and critical challenges in various contexts and circumstances. This often led the stakeholders and decision-makers to depart from traditional thinking and the business-as-usual processes and to come up with innovative approaches to tackle various mission-critical situations within a short time frame. In this paper, a real-life case study of COVID-19 operation management following a multi-disciplinary, multi-stakeholder novel integrated approach in aged care facilities in Victoria, Australia, is presented which yielded significant and positive outcomes. The purpose of the intervention was to develop an integrated system performance approach through the application of various quality management tools and techniques to achieve organizational excellence at the aged care centers. The case involved the use of mathematical models along with statistical tools and techniques to address the specific problem scenario. A system-wide management plan was proposed, involving various agencies across several residential aged care facilities during the pandemic. A three-step methodological framework was developed, where Six Sigma, a system thinking approach, and a holistic metric were proposed to manage the value chain of the pandemic management system. The experimental result analyses showed significant improvement in the management process, suggesting the validity and potential of this holistic approach to stabilize the situation and subsequently set the conditions for operations excellence within the sectors. The model offers new insight into the existing body of knowledge and offers an efficient approach to achieving operational excellence in any organization or business regardless of its type, shape and complexity, which can help practitioners in managing complex, mission-critical situations like a pandemic.

Implementation of DMAIC Cycle to Study the Impact of COVID-19 on Emergency Department-LOS

Lean Six Sigma (LSS) is a methodological approach that originated in industry and has, over time, become increasingly popular in healthcare. Its tool-to, the DMAIC cycle, consisting of 5 main steps, offers methodological rigor that helps improve processes by comparing results quantitatively. In this study, the LSS and in particular the DMAIC cycle was used to investigate the impact of COVID-19 on patients' length of stay in the Emergency Department (ED-LOS) of the Evangelical Hospital "Betania” of Naples (Italy). The study revealed a general increase in ED-LOS due mainly to the new steps that the hospital added to the standard flow, such as those for performing screening swabs, and the reduction of treatment stations, with the exception of patients discharged home for whom there was a statistically significant reduction. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Enhancing sustainable supply chain performance by adopting sustainable lean six sigma-Industry 4.0 practices

PurposeThe Sustainable Lean Six Sigma (SLSS) adoption approach, advancements in Internet technologies and the use of Industry4.0 technologies has resulted in faster customer need fulfilment. The Industry4.0 technologies have resulted in a new paradigm where strategic and operational decisions are in favour of profitability and long-term viability. The purpose of this study is to identify Industry4.0-SLSS practices and sustainable supply chain performance metrics, as well as to develop a framework for decision-makers and managers to make supply chains more sustainable.Design/methodology/approachThe 33 Industry4.0-SLSS practices and 24 performance metrics associated with the sustainable supply chain are shortlisted based on extensive literature review and expert opinion. The Pythagorean Fuzzy Analytical Hierarchy Process (PF-AHP) approach is used to evaluate the weights of Industry4.0-SLSS practices after collecting expert panel opinions. The Weighted Aggregated Sum Product Assessment (WASPAS) methodology used these weights to rank performance metrics.FindingsAccording to the results of PF-AHP, "Product development competencies (PDC)" are first in the class of major criteria, followed by "Advanced technological competencies (ATC)" second, "Organisational management competencies (OMC)" third, "Personnel and sustainable competencies (PSC)" fourth and "Soft Computing competencies (SCC)" fifth. The performance metric "Frequency of NPD" was ranked first by the WASPAS method.Research limitations/implicationsThe proposed paradigm helps practitioners to comprehend Industry4.0 technology and SLSS practices well. The identified practices have the potential to boost the sustainability and supply chain's performance. Organizational effectiveness will benefit from practices that promote a sustainable supply chain and the use of developing technology. Managers can evaluate performance using performance metrics that have been prioritized.Originality/valueThe present study is one of the unique attempts to establish a framework for enhancing the performance of the sustainable supply chain. The idea of establishing Industry4.0-SLSS practices and performance measures is the authors' original contribution.

An integrated Industry 4.0-Sustainable Lean Six Sigma framework to improve supply chain performance: a decision support study from COVID-19 lessons

Purpose: The COVID-19 pandemic era has severely hampered the economy over the globe. However, the manufacturing organizations across all the countries have struggled heavily, as they were among the least who worked on online mode. The organizations are adopting various innovative quality methodologies to improve their performance. In this regard, they are adopting the Sustainable Lean Six Sigma (SLSS) concept and Industry 4.0 technologies to develop products at a faster rate. The use of Industry 4.0 technologies may reduce material movement and supply chain disruptions with the help of smart intelligent systems. There is a strong synergy between SLSS and Industry 4.0 technologies, resulting in an integrated approach for adoption. This study aims to develop a framework that practitioners can use to adopt Industry 4.0-SLSS practices effectively. Design/methodology/approach: This study portrays 31 Industry 4.0-SLSS practices and 22 performance metrics identified through a literature review to improve the manufacturing supply chain performance. To compute the weights of these practices, the Robust Best–Worst Method (RBWM) is used. The Pythagorean fuzzy combined compromise solution (PF-CoCoSo) method is used to rank performance metrics. Findings: According to the RBWM results, "Process Development Practices (PDP)” are first among the major criteria, followed by "Organizational Management Practices (OMP)” at second, "Technology Adoption Practices (TAP)” at third, "Strategy Management Practices (SMP)” at fourth and "Executive Management Practices (EMP)” at fifth, whereas the PF-CoCoSo method resulted in the performance metric "On time product delivery” ranking first. Research limitations/implications: The identified practices have the potential to significantly improve the performance of the manufacturing supply chain. Practices that encourage a sustainable manufacturing supply chain and the usage of emerging technology will benefit organizational effectiveness. Managers can assess performance using prioritized performance metrics. Originality/value: During the COVID-19 pandemic era, this is one of the unique attempts to provide a framework to improve the manufacturing supply chain performance. This study integrates and identifies Industry 4.0-SLSS practices and performance metrics for enhancing overall performance. © 2022, Emerald Group Publishing Limited.

Quality improvement initiative reduces overcrowding on labour and delivery unit in a university hospital in Haiti.

BACKGROUND: Following the first COVID-19 peak in 2020, came the seasonal childbirth peak at Hôpital Universitaire de Mirebalais (HUM). This peak is associated with overcrowding on the labour and delivery (L&D) ward. Lack of sufficient bed-space for sick neonates in the neonatal ICU at HUM, has led to overcrowding and lengthy stays of sick newborns on L&D. These conditions contribute to the subsequent lack of bed-space for newly postpartum mothers and potentially decreases quality of care for both new mothers and neonates. METHODS: A Maternity Task Force was created by hospital leadership to address these urgent needs. The team's objective was to eliminate mothers and newborns laying on the floor in L&D. The Six-Sigma/DMAIC quality improvement methodology was used as the problem was urgent, demanded rapid results and centred around the process of patient flow in the institution. Process flow chart and Ishikawa diagrams were used to identify the root causes of the issues. RESULTS: An average of 22% of postpartum women did not have a bed preintervention and 0% of postpartum women were laying on the floor post intervention. An average of 33% of newborns received paediatric care on the maternity ward pre-intervention compared with an average of 17% postintervention. The team did not achieve its objective for this second indicator, which was to have less than 10% of sick newborns on the maternity ward receiving paediatric care. CONCLUSION: HUM hospital leadership took the vital decision to form the Maternity Task Force to make changes, which consequently led to a sustainable positive and lasting impact on the lives of new mothers and their babies at the institution. The objective of 0 postpartum mothers and newborns on the ground was achieved and fewer newborns receive intensive paediatric care on the maternity ward as a result of our interventions.

Innovation and Control of Health Technology Management Procedures Applying Six Sigma Methodology

The National Institute of Respiratory Diseases (INER. Its acronym in Spanish) is a public healthcare institution that provides medical attention, teaching and scientific research centered on diseases of the respiratory system. In 2019, the hospital held 228 beds and over six thousand medical devices for diagnosis, treatment, and rehabilitation of patients. In 2020, the COVID-19 pandemic pushed the Hospital to convert 215 conventional care beds to intensive care beds, assigned exclusively to critical COVID-19 patients. This resulted in a higher demand of Health Technology Management (HTM) resources which were reflected by a 60% increase in the Hospital’s medical devices. Therefore, the technical staff of the Department of Biomedical Engineering grew by 300%. Thus, the objective of this work was to innovate HTM procedures through the application of Six Sigma Methodology and two Lean tools: 5S and Kanban, to control the activities carried out at the Hospital. Procedures for dealing with work orders and inventory control of supplies (accessories, consumables, and spare parts) that medical equipment require for its operation were analyzed. Additionally, three activities related to HTM were analyzed: tool control, work area control and information flow among all 5 personnel work shifts. In total, seven innovation strategies were proposed and implemented in the Department of Biomedical Engineering at the INER. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

A Transdisciplinary Framework for Engineering Education, Developing Tactical Engineering Decision Making Skills

The use of digital simulators is a well-known practice in managerial courses as tools for strategic decision-making. On the other side, engineering students are used to practicing theoretical knowledge in laboratories or real factories due to the tactical nature of the decisions involved. During the COVID pandemic, universities were forced to limit or cancel access to physical facilities. Engineering professors were challenged to keep educational schedules using digital tools. The contribution of this work is a transdisciplinary framework on how to design engineering practices through digital simulation models to keep or improve prepandemic learning levels. The societal challenge involves a change in paradigm for professors, students, and practitioners. The proposed framework was used to design, implement, and feedback a senior student Six Sigma project course, using a tailormade web-based simulator. Two iterations of the framework are currently deployed: one-way information flow, and two-way interaction. The information obtained so far was the base for the third iteration of the framework which involves three-dimensional virtual reality interaction. Case-based learning and management simulators have been successful at bridging theory and practice for management students. The work in this paper builds on these management practices to achieve equivalent learning levels for engineering students. © 2022 The authors and IOS Press.

A comparative evaluation of six sigma metrics and quality goal index ratio 3 months prior to first lockdown due to COVID-19 pandemic and 3 months during lockdown in a NABL accredited central laboratory

Introduction and Aim: Sigma represents Standard Deviation (SD) which indicates the degree of variation in a process, where the higher sigma value implies that less likely the laboratory reports false test results. Using a newer parameter called Quality Goal Index (QGI) we can find the reason behind the lower sigma value. Our study aimed to compare the six-sigma metric and QGI ratio 3 months prior to first lockdown due to COVID-19 pandemic and 3 months during the first lockdown. Methodology: A retrospective study was used to compare the six-sigma metric and QGI ratio 3 months prior to first lockdown due to COVID-19 pandemic and 3 months during the first lockdown for the selected ten analytes from 1st of January 2020 to 30th of June 2020 from the clinical biochemistry section of Yenepoya Medical College Hospital, Deralakatte, Mangalore. Result(s): The sigma metrics from January to March (level 1) indicated that urea, TSH, beta-HCG fell short of meeting Six Sigma quality performance and from April to June, glucose, creatinine, urea and ALT had metrics less than 3 at both the Internal Quality Control levels. QGI ratio indicated that from January to March, the problem was imprecision for urea, TSH and beta-HCG (QGI < 0.8). From April to June, urea and creatinine showed imprecision, glucose and ALT showed inaccuracy, urea and ALT showed both imprecision and inaccuracy. Conclusion(s): This study highlights the necessity for stringent Internal Quality Control and External Quality Assurance monitoring even during the lockdown period of the pandemic. By implementing six sigma and finding QGI ratio, quality of laboratory services can be improved immensely. Copyright © 2022, Indian Association of Biomedical Scientists. All rights reserved.

Healthcare Process Improvement Initiative Catalyzes Unintended Positive Consequences: An Evaluation Case Study

An After-Action Review of Mobile Charge Capture (MCC) (an electronic health record management tool that addresses billing, scheduling, and coding practices) software, post implementation, was the foundation of this "evaluation case" study that focused on process improvement (PI) at a Midwest United States specialty medical practice ("the practice"). Outcomes, influenced by the SARS-CoV-2 pandemic (COVID-19), confirmed substantial operational improvement from MCC activation and generated unintended positive consequences. The MCC Improved Process facilitated billing and scheduling continuity that cultivated a readiness for the nimble pivot to virtual healthcare delivery. Culture emerged as a key factor in organizational change;with additional PI opportunities identified for further study and consideration.

Can Quality Improvement Methodologies Derived from Manufacturing Industry Improve Care in Cardiac Surgery? A Systematic Review.

OBJECTIVES: Healthcare is required to be effectively organised to ensure that growing, aging and medically more complex populations have timely access to high-quality, affordable care. Cardiac surgery is no exception to this, especially due to the competition for and demand on hospital resources, such as operating rooms and intensive care capacity. This is challenged more since the COVID-19 pandemic led to postponed care and prolonged waiting lists. In other sectors, Quality Improvement Methodologies (QIM) derived from the manufacturing industry have proven effective in enabling more efficient utilisation of existing capacity and resources and in improving the quality of care. We performed a systematic review to evaluate the ability of such QIM to improve care in cardiac surgery. METHODS: A literature search was performed in PubMed, Embase, Clarivate Analytics/Web of Science Core Collection and Wiley/the Cochrane Library according to the Preferred Reporting Items for Systematic Reviews and Meta-Analysis methodology. RESULTS: Ten articles were identified. The following QIM were used: Lean, Toyota Production System, Six Sigma, Lean Six Sigma, Root Cause Analysis, Kaizen and Plan-Do-Study-Act. All reported one or more relevant improvements in patient-related (e.g., infection rates, ventilation time, mortality, adverse events, glycaemic control) and process-related outcomes (e.g., shorter waiting times, shorter transfer time and productivity). Elements to enhance the success included: multidisciplinary team engagement, a patient-oriented, data-driven approach, a sense of urgency and a focus on sustainability. CONCLUSIONS: In all ten papers describing the application of QIM initiatives to cardiac surgery, positive results, of varying magnitude, were reported. While the consistency of the available data is encouraging, the limited quantity and heterogenous quality of the evidence base highlights that more rigorous evaluation, including how best to employ manufacturing industry-derived QIM in cardiac surgery is warranted.

Design for Six Sigma: Material Design for 3D Printing of the New Product

The starting point of the research was the demand of customers for 3D printing of face shield frames. In this context, but also due to the possibilities of relatively cheap 3D printing of various products, a large amount of waste has started to be generated, which needs to be disposed of. The goal of the research was to contribute to the development of new 3D printed products while balancing their level of quality, and cost and minimizing their environmental footprint. For this purpose, based on customer requirements, the research team printed samples of PLA (polylactic acid) material and recycled PLA using Fused Filament Fabrication (FFM) technology, thus significantly helping especially during the Covid-19 pandemic. The research used a design method known as Design for Six Sigma (DFSS) and its framework of successive steps of defining, measuring, analyzing, designing, and validating DMADV (Define, Measure, Analyze, Design, and Validate). The research concerns the development of Quality Function Deployment (QFD) functions that respect the requirement of a minimum environmental footprint of 3D printing. The result is an original QFD methodology, taking into account the choice of material in terms of minimizing the impact on the environment. © 2022 IEEE.

Understanding System Complexity in the Non-Destructive Testing of Advanced Composite Products

Non-destructive testing (NDT) is a quality control measure designed to ensure the safety of products according to established variability thresholds. With the development of advanced technologies and a lack of formalised knowledge of the state-of-the-art, the National Composites Centre, Bristol, has identified that the increasing complexity of composite products will lead to some severe inspection challenges. To address the apparent knowledge gap and understand system complexity, a formulaic approach to introduce intelligence and improve the robustness of NDT operations is presented. The systemic development of a high-fidelity knowledge base (KB) involves the establishment of a capability matrix that maps material, component, and defect configuration to the capabilities and limitations of selected detection methods. Population and validation are demonstrated through the experimental testing of reference standards and evaluated against an assessment criteria. System complexity in ultrasonic testing operations focusses on capturing the inherent risks in inspection and the designation of evidence-based path plans for automation platforms. Anticipated deployment of the validated applicability data within the KB will allow for road-mapping of the inspection technique development and will provide opportunities for knowledge-based decision making. Moreover, the KB highlights the need for Design for Inspection, providing measurable data that the methodology should not be ignored.

New Ideas and Methods of Coping Mechanism for Infectious Diseases Based on Big Data: A Critical Literature Review

Based on big data analysis, we discuss how to formulate an optimal coping mechanism for infectious diseases, especially major and emerging infectious diseases. First, by combining big data analysis and statistical analysis model and deducing whether the emerging disease is contagious, the strength of the contagion effect and the possible consequences, this study will determine whether the corresponding coping strategies should be implemented for infectious diseases, especially major and emerging infectious diseases. Secondly, according to the inspection results and actual situation, the optimal coping strategy is formulated to minimize the loss of life and property security of the country and the society by using the optimization principle and the objective management in management science. Finally, the statistical analysis method and the six sigma principle are combined to develop a feedback mechanism to evaluate whether the formulated coping strategies can achieve the expected results in practice. Our research has improved the research framework of infectious diseases in theory and provided scientific reference and experience for the major and emerging infectious diseases in practice for the future. © 2022 IEEE.

Development of Lean & Six Sigma Integration Model through Axiomatic Design Theory

After Pandemic covid19 Era, Uncertain Rapid changes in product demand, product design and introduction of new products and increasing global Competition require manufacturing systems to be highly flexible, adaptable and responsive. These characteristics of a manufacturing system must be addressed at the design stage. This paper presents a method for design of manufacturing systems by combining Lean Manufacturing and Six Sigma Methodology by the means of Axiomatic Design approach. To achieve the desired goals of a manufacturing enterprise, manufacturing systems must be designed to satisfy a specific set of functional requirements (FRs) and constraints (C).Axiomatic design theory can help you achieve this type of design. This developed Integration model, albeit hypothetical, may be an ideal and practical model for many manufacturing firms competing in consumer-oriented industries with worldwide during this Pandemic Era. © 2021 ACM.