Reduce Process Variability Research Articles

Seed Layers for Wide-Band Gap Coevaporated Perovskite Solar Cells: CsCl Regulates Band Gap and Reduces Process Variability

Seed Layers for Wide-Band Gap Coevaporated Perovskite Solar Cells: CsCl Regulates Band Gap and Reduces Process Variability

A-193 Optimizing Emergency Laboratory Operations Through Six-Sigma Strategies for Enhanced Efficiency and Effectiveness: A Pilot Study

Abstract Background Quality assurance is vital in laboratory diagnostics to ensure patient safety and accurate results. Laboratories employ internal and external quality control measures, particularly focusing on the analytical phase. Sigma metrics, representing process variation, play a crucial role in assessing accuracy, with Six Sigma setting high standards for operational excellence. This study aims to enhance laboratory testing procedures by analyzing the analytical phase. In 2022, the Emergency laboratory at KFAFH, Jeddah, achieved optimal sigma performance, marking a significant improvement in analytical testing. http://www.westgard.com/sigma-vp-kfafh.htm. In 2023, The study also demonstrated a 30% productivity increase, driven by sustained efforts and Sigma metrics implementation, leading to cost reductions and improved quality. Real-time patient moving averages enhance quality assurance, extending coverage to the total testing process. The primary objectives include redesigning IQC procedures and optimizing resource utilization. This will be accomplished by reducing the frequency of controls, minimizing unnecessary reruns, optimizing resource allocation, and managing total costs, all while ensuring compliance and averting violations. Methods A retrospective-prospective study was conducted at the Emergency Laboratory of King Fahad Armed Forces Hospital in Jeddah, Saudi Arabia, from May 2021 to October 2023. The study assessed 841,558 tests for 131,785 emergency patients. Forty-seven analytes were evaluated for Internal Quality Control and External Quality Control (CAP), with monthly data collection. Chemistry and Hematology parameters were analyzed using Unity Real-Time software on Alinity-ci and Alinity-hq analyzers. Data was collected in three stages: baseline, pre-Sigma verification, post-Sigma implementation, and a sustainability phase. The methodology involved DMAIC model tools like flowcharts, cause & effect analysis, and sigma metric scales. Six Sigma process tools, including DPM and process capability analysis, were used. Interventions included Six Sigma metrics for QC performance, DPM for TAT process improvement, and patient moving average Real-time for proactive risk management in reducing analytical testing variation. Results Excellent performance (> 6 sigma) was achieved for 70% of chemistry test parameters for three control levels.: This indicates a high level of process capability and reliability in laboratory operations, ensuring accuracy and consistency in test results. The study successfully reduced process variation in Emergency Room (ER) laboratory Turnaround times (TATs), leading to an increase in process capability as measured on the Sigma scale, which improved from 1.35 to 3.1, indicating a more robust and efficient laboratory operation. Reducing the cost and efficiency of internal failure controls by 29% and at the same time saving in labor cost/workload marked a decrease in QC frequency from 7.0% to 0.18%, reaching the target goal of TATs and satisfaction. Conclusions The adoption of Six Sigma methodologies resulted in a notable decrease in QC runs, signaling cost savings. These high-quality methods streamline laboratory operations, improve cost-effectiveness for the healthcare system, and ultimately benefit patients and clinicians. Six Sigma methodologies play a crucial role in selecting appropriate QC designs for each parameter, ensuring efficient control while maximizing cost-effectiveness. Emphasizing the importance of suitable QC strategies and continuous technologist training is vital for mitigating analytical errors and enhancing turnaround time through comprehensive automation of the analytical phase.

Effect of oxidative stress on antigen productivity in B. Pertussis cultures

Whooping cough, also known as pertussis, is an infectious respiratory disease caused by Bordetella pertussis, a type of gram-negative, aerobic, pathogenic bacteria with a coccobacillus shape and an outer capsule. The acellular vaccine for this disease contains a combination of pertussis toxin, fimbriae, filamentous hemagglutinin, and pertactin. The main constraint in the manufacturing of the vaccine relates to pertactin productivity due to its low abundance in the fermentation broth. Being the most abundant nutrient in the media, variability in initial glutamate concentration is hypothesized to be a major cause of process variability. This study proposes the combined use of cytometry and chromatography based separation to study the impact of glutamate-induced oxidative stress on the growth and productivity of pertactin antigen. Surface expression of pertactin was observed using fluorescence microscopy and flow cytometry while extracellular concentration was quantified using affinity chromatography. Oxidative stress levels and secretion of NADPH, a crucial reactant in antioxidant reactions, were monitored in flask and bioreactor experiments. The results established clear correlations between oxidative stress with growth and productivity and differentiate between the reduction in productivity to growth and to synthesis rate of pertactin. These findings are industrially relevant for improving productivity and reducing process variability.

Integration of statistical methods and neural networks for temperature regulation parameter optimization

Temperature control plays a crucial role in various industrial processes, ensuring optimal performance and product quality. The conventional approach to optimizing temperature controller parameters involves manual tuning, which can be time-consuming, labor-intensive, and often lacks precision. This paper introduces an innovative methodology for optimizing the parameters of a temperature controller by integrating statistical methods in the preparation of the experimental plan utilized by neural networks. The integration of statistical techniques in designing the experimental framework enhances the efficiency of data collection, providing a robust foundation for subsequent analysis. The neural network leverages this well-structured dataset to model and optimize the temperature controller parameters, resulting in improved precision and performance. The synergistic integration of statistical methods and neural networks not only streamlines the optimization process but also enhances the reliability of the temperature control system. The effectiveness of the proposed approach is demonstrated through case studies on the Procon level/flow and temperature 38-003 process. The results show significant improvements in temperature control performance, with reduced process variability and faster response times.

Applying the Taguchi Method to Improve Key Parameters of Extrusion Vacuum-Forming Quality.

This research investigates the control of thickness and weight in plastic extrusion vacuum-thermoforming products to identify optimal key parameters for cost reduction and energy savings. The initial step involves identifying crucial influencing factors. In this step, the Delphi technique was employed through a questionnaire administered to a panel of expert scholars to ensure minimal error and maximal reliability in determining key influencing factors. Consensus was sought to establish appropriateness and consistency. Subsequently, the Taguchi method was applied for quality design and planning of the extrusion vacuum-forming process. The experimental design parameters were selected using an L18 (21 × 37) orthogonal array, and the desired quality characteristics were determined. Comparative analysis of quantitative production data from two consecutive experiments was conducted, and based on F-values and contribution analysis, the combination of control factors maximizing the Signal-to-Noise (S/N) ratio was identified. The objective is to seek optimal parameters for improving the quality of the plastic polypropylene (PP cup lid) manufacturing process, reducing process variability, and identifying the most robust production conditions. Through multiple actual production prediction experiments, it was determined that five control factors, "polypropylene new material ratio," "T-die lips adjustment thickness", "mirror wheel temperature stability", "molding vacuum pressure time", and "forming mold area design", contribute to the maximization of the S/N ratio, i.e., minimizing variability. Statistical validation confirms a significant improvement in product quality and weight control. Noteworthily, the quality control model and experimental design parameters established in this study are also applicable to other plastic products and bio-based materials, such as PET, HIPS, and biodegradable PLA lids with added calcium carbonate. The results of the experimental production demonstrate its ability to consistently control product weight within the range of 3.4 ± 0.1 g, approaching the specified tolerance limits. This capability results in approximately 2.6% cost savings in product weight, contributing significantly to achieving a company's KPI goals for environmental conservation, energy efficiency, and operational cost reduction. Therefore, the findings of this study represent a substantial and tangible contribution.

The role of the six sigma method in controlling and improving product quality

The Six Sigma method has been proven effective in industry for reducing process variability, increasing customer satisfaction, and optimizing operational efficiency. This research aims to analyze the role of the Six Sigma method as a systematic approach in controlling and improving product quality. This research uses qualitative methods with descriptive methods. The results of this research reveal that the application of the Six Sigma Method in controlling and improving product quality has a significant positive impact. Controlling process variability, focusing on customer satisfaction, and using statistical methods in data analysis have succeeded in increasing product consistency, building customer loyalty, and providing a strong basis for fact-based decision making. Implementation of the DMAIC concept provides a structured framework for continuous improvement, while cross-functional employee involvement creates a work environment that supports collaboration and innovation.

Next-Gen Production Excellence: A Deep Simulation Perspective on Process Improvement

The pursuit of operational excellence and continuous improvement in the manufacturing sector has led to the convergence of two prominent methodologies, Lean and Six Sigma. This abstract provides an overview of the comprehensive framework discussed in this paper, which blends the principles of Lean and Six Sigma to drive transformative enhancements in manufacturing processes. The proposed framework extends beyond surface-level improvements, delving deep into the intricacies of production systems. It encompasses a holistic approach that addresses process efficiency, waste reduction, quality enhancement, and cost optimization. By synergizing Lean's emphasis on eliminating waste and Six Sigma's focus on process variation reduction, this framework approaches a comprehensive strategy for manufacturers to succeed in operational excellence. Through real-world case studies and practical insights, this paper highlights the tangible benefits of adopting the Lean and Six Sigma deep framework and its potential to revolutionize the manufacturing landscape.

Manufacturing Excellence Using Line Balancing & Optimization Tools: A Simulation-based Deep Framework

The pursuit of operational excellence and continuous improvement in the manufacturing sector has led to the convergence of two prominent methodologies, Lean and Six Sigma. This abstract provides an overview of the comprehensive framework discussed in this paper, which blends the principles of Lean and Six Sigma to drive transformative enhancements in manufacturing processes. The proposed framework extends beyond surface-level improvements, delving deep into the intricacies of production systems. It encompasses a holistic approach that addresses process efficiency, waste reduction, quality enhancement, and cost optimization. By synergizing Lean's emphasis on eliminating waste and Six Sigma's focus on process variation reduction, this framework approaches a comprehensive strategy for manufacturers to succeed in operational excellence. Through real-world case studies and practical insights, this paper highlights the tangible benefits of adopting the Lean and Six Sigma deep framework and its potential to revolutionize the manufacturing landscape

Measuring and Analyzing the Process Capability of Productivity – An Applied Study in the Al-Tahady Factory for the Production of Filters

This study addressed concerns related to increased percentages of damaged and re-worked production, heightened demand for factory products, and lack of awareness of the approved Sigma (σ) level during manufacturing, and associated deviations in the manufacturing process. The primary research problem was to assess the manufacturing process's stability and capability to consistently produce conical filters that meet required specifications. The study followed a sample-based approach, where twenty samples, each containing four observations, were collected continuously over a period of seven days. For each sample, the mean (X ̅) and range (R) were calculated. The mean X-Double bar of 319.32 and the average range R-bar of 0.848 were obtained through data analysis. The main findings revealed that, on average, the manufacturing process was relatively close to the target value (X-Double bar = 319.32). However, the presence of several data points outside the control limits indicated potential variability in the process. The average range (R-bar = 0.848) highlighted certain variations in the manufacturing process, which might contribute to issues like damaged or re-worked production. The study identified the need for further investigation to determine the root causes of these variations, which could include machine malfunctions, material fluctuations, or operator errors. By addressing these concerns and reducing process variability, the factory can enhance product quality, decrease waste, and improve customer satisfaction. In conclusion, continuous process monitoring and improvement initiatives, such as Six Sigma, are essential for achieving greater process capability in conical filter manufacturing. This research contributes valuable insights into process performance and provides a basis for implementing corrective actions to ensure consistent product quality and meet customer demands.

Enhancing Production Capacity at Manufacturing Test Company Using Lean Six Sigma

One of the things that generally happens is a growth in production capacity, and under ideal conditions, a corporation must have enough space to accommodate an increase in output. However, what if this occurs during a period of solid output demand and needs that must be optimized quickly? Of course, this is a significant difficulty that requires a higher level of analysis to achieve an effective and economical answer. Based on the past three years’ statistics, demand from customers will rise to 528,000 in 2023, up from the highest capacity/year of only 409,000. To find the best answer, businesses must examine aspects such as business competitiveness, changing circumstances, and large fluctuations. Improvement and optimization of low-output processes will aid in achieving output. Applying the Lean Six Sigma method in evaluation yields substantial results and leads to the best solution for selecting a productivity-boosting strategy. Strategies for increasing production capacity include eliminating non-value-added operations (lean concept) and integrating processes with other processes to reduce process variability (Six Sigma concept). The results demonstrate that the increase in production capacity exceeds the target (more than 20% from 2022). It is built on the theory of constraint concept approach, which solves the core problem while moving on to the next challenge.

Monitoring, Evaluation, and Improvement Model for Process Precision and Accuracy

Process Capability Indices (PCIs) are devices widely used in the industry to evaluate process quality. The commonly used process capability indices all contain accuracy indices and precision indices. As the accuracy index is closer to zero, the process accuracy is higher. The precision index mainly represents the extent of process variation. As the value is smaller, the process variation is smaller, that is, the precision is higher. In fact, process capability indices are the functions of accuracy indices and precision indices. Obviously, as long as accuracy indices and precision indices are controlled, the process capability indices can be controlled as well. Therefore, this study first derived accuracy and precision control charts to observe not only process accuracy but also process precision. Then, this study adopted in-control data to acquire a 100 (1 − α)% confidence region of an accuracy index and a precision index, with which statistical tests were performed. Subsequently, according to the definition of the six sigma quality level, both indices were examined. Furthermore, based on the testing results, suggestions for process improvement were proposed, including correcting the direction of process deviation and deciding whether to reduce process variation. Finally, this study demonstrated the applicability of the proposed model using an empirical example.

THE EFFECTIVENESS OF LEAN SIX SIGMA IMPLEMENTATION IN HEALTH-CARE FACILITIES

This study was conducted to investigate the effectiveness of implementing Lean Six Sigma within the field of health care facilities. Lean Six Sigma is a powerful tool that can increase the organization performance. In this study A systematic review was performed in accordance with current best practices through close adherence to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses. This study was found that the implementation of Lean Six Sigma enables the hospital to reduce the consumption of plasminogen by 69%, block utilization was increase by 13%-28%, book appointment increases by 24-276%, time saving were increase by 17% to 21%. And for service quality in blood collection was increased by 55% housewide. Lean Six Sigma is an inexpensive methodology to implement, it can bring many benefits to the organization in the health-care facilities. The successful implementation of Lean Six Sigma was able to reduce waste, reduce defect rate, increasing the quality of service, and reduce process variability that led to patient satisfaction and cost effective.
 Keywords: lean six sigma, health-care facility, productivity, efficiency

A-165 Test Harmonisation Is a Key Enabler of Pathology Transformation

Abstract Background In April 2021 a new joint venture was formed to deliver and transform services across South East London. Two large NHS Foundation Trusts (Guy’s and St Thomas’, and King’s College hospitals) partnered with SYNLAB who are specialist pathology providers, bringing extensive global experience and expertise in setting up, transforming and running diagnostic services. The Trusts had been working in partnership for many years but were operating in numerous areas as two separate entities. There was duplication of services and tests, with different analytical platforms and very different ways of working, which led to variations in resulting and reporting. Our aim was to establish a single consolidated service, with a harmonised test catalogue, facilitating the provision of consistent results regardless of the origin of sample, and to have a single governance framework to drive improved accountability and optimisation of resources. Our view was that test harmonisation was essential to enable the deployment of a common core Laboratory Information Management System (LIMS) from the 24 legacy systems, and that the harmonisation process should be clinically led. Method We established forums (through a series of workshops) for comprehensive engagement and dialogue to allow key clinical leads from all services to meet and agree the path forward. After extensive collaborative engagement, a new model was created with a single “Strategic Clinical Lead” with pan-site responsibility appointed for each service from the existing senior clinical staff. The Strategic Clinical Lead works with their colleagues to drive consensus, seeking wide stakeholder engagement from end service users as required. Results From an initial repertoire of circa 7100 tests, test harmonisation has resulted in a 25% reduction, and in some services such as Virology, we identified 31% duplication in tests. The establishment of the new clinical model has resulted in improved efficiency and better use of resources including staffing as well as a reduction of variation of services. Importantly, test harmonisation is essential to move samples seamlessly in a hub and spoke model. The implementation of a single common core LIMS this year will significantly improve business continuity by facilitating seamless transfer of work between sites. Conclusion Extensive clinical engagement is key to transforming pathology services. The benefits of consolidation include an overall reduction in process variation leading to improved standardisation and therefore enhanced patient safety as a result of consistency in result reporting. We established a framework and new clinical model which has resulted in improved links between staff across all sites who are working collaboratively on the new target operating model (hub and spokes) which will be live in April 2024.

Lean Six-Sigma Approach for Sub-Contract Licensing and its Process Improvement across the Manufacturing Supply Chain using GUT Priority Matrix

This study aims to examine how Lean Six Sigma as a quality management tool could enhance productivity in a manufacturing organization, ZITRON. In addition, this study also aims to show how DMAIC (defined, measured, analyzed, improved and controlled) is operationalized for overall process improvement in ZITRON manufacturing. Lean management takes care of waste across all processes and focuses on speed and time within a firm while Six Sigma is a strategy that focuses on eliminating defects, reducing costs and also reduces process variability. As such, this study utilizes the Lean Six-Sigma approach as a tool to ensure more efficiency is achieved in terms of source, make, delivery and process efficiency. Methodologically, there are five phases in the Lean Six Sigma strategies which are defined, measured, analyze, improve and controlled (DMAIC). The findings show that Lean Six Sigma can help to eliminate waste, and cost-reduction, and help eliminate variations in processes within the manufacturing. However, SIX SIGMA tools have been used to solve the problem which is a delay in processes in manufacturing. Based on this research paper, focuses on the manufacturing problem which is a delay in processes. By using Six Sigma, the problem that existed in manufacturing has been solved through process improvement. This work is originally following a self-method in which the data is collected by using observation techniques to identify the existing problem and solution-seeking within the manufacturing.

Six Sigma application in an Irish meat processing plant to improve process yields

PurposeThis study aims to utilise Six Sigma in an Irish-based red meat processor to reduce process variability and improve yields.Design/methodology/approachThis is a case study within an Irish meat processor where the structured Define, Measure, Analyse, Improve and Control (DMAIC) methodology was utilised along with statistical analysis to highlight areas of the meat boning process to improve.FindingsThe project led to using Six Sigma to identify and measure areas of process variation. This resulted in eliminating over-trimming of meat cuts, improving process capabilities, increasing revenue and reducing meat wastage. In addition, key performance indicators and control charts, meat-cutting templates and smart cutting lasers were implemented.Research limitations/implicationsThe study is one of Irish meat processors' first Six Sigma applications. The wider food and meat processing industries can leverage the learnings to understand, measure and minimise variation to enhance revenue.Practical implicationsOrganisations can use this study to understand the benefits of adopting Six Sigma, particularly in the food industry and how measuring process variation can affect quality.Originality/valueThis is the first practical case study on Six sigma deployment in an Irish meat processor, and the study can be used to benchmark how Six Sigma tools can aid in understanding variation, thus benefiting key performance metrics.

Close the GAPS: A Standardized Perioperative Protocol Reduces Breast Reconstruction Implant Infections.

Implant-based breast reconstruction (IBBR) is a complex process with significant practice variability. Infections after IBBR are associated with higher rates of readmission, reoperation, and reconstructive failure. To reduce process variability and postoperative infections, the authors implemented an evidence-based, standardized protocol for IBBR. The protocol was applied to all patients undergoing IBBR at a single institution from December of 2019 to February of 2021. Intraoperative protocol adherence was recorded, and infection events were considered minor (managed with outpatient antibiotics) or major (managed with readmission or reoperation). A historic control group was retrospectively analyzed for comparison. Sixty-nine patients (120 breasts) in the protocol group were compared with 159 patients (269 breasts) in the retrospective group. No differences were found in demographic characteristics, comorbidities, or type of reconstruction (expander versus implant). Intraoperative protocol adherence was 80.5% (SD, 13.9%). Overall infection rate was significantly lower in the protocol group versus controls (8.7% versus 17.0%; P < 0.05). When dichotomized, protocol patients had a lower rate of both minor (2.9% versus 5.7%; P = 0.99) and major (5.8% versus 11.3%; P = 0.09) infections, although this was not statistically significant. Rate of reconstructive failure secondary to infection was significantly lower in the protocol group (4.4% versus 8.8%; P < 0.05). Among protocol patients, those without infection had higher protocol adherence (81.5% versus 72.2%; P < 0.06), which neared statistical significance. A standardized perioperative protocol for IBBR reduces process variability and significantly decreases rate of overall infections and reconstructive failure secondary to infection. Therapeutic, III.

Improvement of production process variations of bolster spring of a train bogie manufacturing industry: a six-sigma approach

The need for improved productivity without sacrificing quality, which is in line the prime target of many manufacturing industries. The aim of this study is to investigate the causes of production variation: a case study of the rail manufacturing industry, South Africa. In this study, the six-sigma Define, Measure, Analyse, Improve and Control (DMAIC) phases were applied to enhance the process capability (long term) in the production of bolster compression springs in the main line of bogie secondary suspension system. In every phase of DMAIC method, a combination of both qualitative and quantitative techniques was utilized. First, process capability index Cpk of the current process was computed which was found less than 1. The results obtained indicated that the process capability index values were found to be 1 after the improvement phase. Hence, significant improvement was achieved in the area of reduction in process variation and product quality after taking corrective actions. From outcomes of the study, it can be concluded that process performance of a train manufacturing plant can be improved significantly by implementing six-sigma DMAIC methodology. The novelty of this study lies in the fact that the implementation of the six-sigma DMAIC phases to enhance the process capability (long term) and minimise variations in the production of bolster compression springs has not be sufficiently highlighted by the existing literature.

Tinjauan Literatur Sistematis Penerapan DMAIC dan DMADV di Industri Manufaktur

Manufacturing company activities consist of several activities, one of which is management. The Process Management Framework includes activities to develop, improve and manage business processes in manufacturing companies. Some companies use some of the best methodologies to improve or develop existing processes and develop new ones. This literature aims to analyze the application of the Define, Measure, Analyze, Improve, Control (DMAIC) and Define, Measure, Analyze, Design, Validate (DMADV) methods in the Manufacturing Industry. The method used is Systematic Literature Review. These results indicate that the DMAIC and DMADV methods are methods that drive the pace of industrial economic growth, because they reduce process variability and improve product quality and prevent errors from occurring. However, this process requires trained and experienced personnel to meet the demand for product defect reduction. This method is the choice of industries that want to improve product quality and avoid mistakes, even though it takes a long time to implement, but almost all problems can be solved and improve processes in the manufacturing industry.

Corporation-wide variation reduction

We illustrate the Statistical Engineering (SE) approach to the solution of large unstructured problems through some years-long experiences with improving productivity and quality using only a relatively small technical staff to elicit a major manufacturing culture change. The invention of a program for Process Variation Reduction (PVR) paralleling the SE steps of opportunity identification, problem definition, operating within the organizational context, strategy development, identification of tactics and creating sustainable solutions, enabled staff at all levels to drive down costs while improving customer satisfaction. Examples here involve advances in the food industry, but the variation reduction planning and strategies are applicable to most organizations.

Strategi persaingan provider telekomunikasi berdasarkan kriteria kepuasan pelanggan

This study aims to analyze the level of customer satisfaction using the six sigma method which is an approach to reduce process variability through statistical tools. Industry 4.0 brings its own challenges for telecommunication providers. In the Industrial Era 4.0, which is marked by the integration between machine systems and smart internet, making dependence on telecommunication providers increases. Industrial companies currently compete in offering products in the form of goods or services in order to attract consumers' interest, as well as the telecommunications industry such as Indosat and Telkomsel. In recent years, providers from Indosat and Telkomsel have been in the top two rankings in terms of number of subscribers and profit generated. The highest DPMO value obtained from the Assurance dimension with a satisfaction target of 6. The results of the Assurance dimension DPMO for Indosat providers are 243.333.33 and Telkomsel providers are 224.166.67 and are at level 2 sigma. These results are still far from the expected target of 3.4 DPMO and 6-sigma