Search results

First is to develop the time truncated median control chart for the Rayleigh distribution (RD) and generalized RD (GRD), respectively. Second is to evaluate the performance of the proposed attribute control chart which depends on the average run length (ARL) and third is to include real life examples for application purpose of the proposed attribute control chart.

(1) Select a random sample of size n from each subgroup from the production process and put them on a test for specified time t, where t = ? × µe. Then, count the numbers of failed items in each subgroup up to time t. (2) Step 2: Using np chart, define D = np, the number of failures, which also a random variable follows the Binomial distribution. It is better to use D = np chart rather than p chart because the authors are using number of failure rather than proportion of failure p. When the process is in control, then the parameters of the binomial distribution are n and p0, respectively. (3) Step 3: The process is said to be in control if LCL = D = UCL; otherwise, the process is said to be out of control. Hence, LCL and UCL for the proposed control chart.

From the findings, it is concluded that the GRD has smaller ARL values than the RD for specified values of parameters, which indicate that GRD performing well for out of control signal as compared to the RD.

This developed control chart is applicable when real life situation coincide with RD and GRD.

Researcher can directly use presented study and save consumers from accepting bad lot and also encourage producers to make good quality products so that society can take benefit from their products.

This article dealt with time truncated attribute median control chart for non-normal distributions, namely, the RD and GRD, respectively. The structure of the proposed control chart is developed based on median lifetime of the RD and GRD, respectively.

The purpose of this paper is to develop a double-objective economic statistical design (ESD) of (X ‾) control chart under Weibull failure properties with the Linex asymmetric loss function. The authors have expressed the probability of type II error (β) as the statistical objective and the expected cost as the economic objective.

The design used in this study is based on a double-objective economic statistical design of (X ‾) control chart with Weibull shock model via applying Banerjee and Rahim's model for non-uniform and uniform schemes with Linex asymmetric loss function. The results in the least average cost and β in uniform and non-uniform schemes by Linex loss function, compared with the same schemes without loss function.

Numerical results indicate that it is not possible to reduce the second type of error and costs at the same time, which means that by reducing the second type of error, the cost increases, and by reducing the cost, the second type of error increases, both of which are very important. Obtained based on the needs of the industry and which one has more priority has the right to choose. These designs define a Pareto optimal front of solutions that increase the flexibility and adaptability of the X ‾ control chart in practice. When the authors use non-uniform schemes instead of uniform schemes, the average cost per unit time decreases by an average and when the authors apply loss function, the average cost per unit time increases by an average. Also, this quantity for double-objective schemes with loss function compared to without loss function schemes in cases uniform and non-uniform increases. The reason for this result is that the model underestimated the costs before using the loss function.

This research adds to the body of knowledge related to flexibility in process quality control. This article may be of interest to quality systems experts in factories where the choice between cost reduction and statistical factor reduction can affect the production process.

The cost functions for double-objective uniform and non-uniform sampling schemes with the Weibull shock model based on the Linex loss function are presented for the first time.

This study investigates the repetitive mixed sampling (MRS) plan based on the Cpk index that was proposed by Aslam et al. (2013a). They were the first to study the MRS plan, but they did not pay attention to the fact that submitting to the variable inspection a sample that was first submitted to the attribute inspection, truncates the X observations. In addition, they did not work with an accurate expression to calculate the probabilities of the Cpk statistic.

The authors presented the results based on their original sampling plan through Monte Carlo simulation and defined the theoretical results of their plan when the sample submitted to the variable inspection is no longer the same one submitted to the attribute inspection.

The β risks of the optimum sampling plans presented by Aslam et al. (2013a) are pretty high, exceeding 46%, on average – this same problem was also observed in Saminathan and Mahalingam (2018), Balamurali (2020) and Balamurali et al. (2020), where the β risks of their proposed sampling plans are yet higher.

In terms of originality, the authors can declare the following. It is not a big deal to propose new sampling plans, if one does not know how to obtain their properties. The miscalculations of the sampling plans risks are dangerous; imagine the situation where the acceptance of bad lots exceeds 50% just because the sampling plan was incorrectly designed. Yes, it is a big deal to warn that this type of problem is arising in a growing number of papers. The authors of this study are the pioneers to discover that many studies focusing on the sampling plans need to be urgently revised.

With the growing need for employees to be innovative, public-sector organizations are investing in employee training. This study aims to examine the effects of a combined Lean Six Sigma and innovation training, using action learning, on public-sector employees’ creative role identity and innovative work behavior.

The authors studied a public service agency in Singapore in which a five-day Lean Innovation Training was implemented, using a combination of Lean Six Sigma and Creative Problem-Solving tools, with a simulation on day one and subsequent team-based project coaching, spread over six months. The authors administered pre- and postintervention surveys among all the employees, and initiated group interviews and observations before, during and after the intervention.

Creative role identity and innovative work behavior had significantly improved six months after the intervention, enabled through senior management’s transformational leadership. The training induced managers to role-model innovative work behaviors while cocreating, with their employees, a renewal of their agency’s core processes. The three completed improvement projects contributed to an innovative work culture and reduced service turnaround time.

Starting with a role-playing simulation on the first day, during which leaders and followers swapped roles, the action-learning type training taught all the organizational members to use various Lean Six Sigma and Creative Problem-Solving tools. This nimble Lean Innovation Training, and subsequent team-based project coaching, exemplifies how advancing the staff’s creative role identity can have a positive impact.

Sizing system is a fundamental topic in garment fitting. The purpose of this study was to assess the fit of existing police uniforms (shirt, jacket, overcoat and trousers) and develop a sizing system for upper and lower body uniforms of Amhara policemen in Ethiopia.

In total, 35 body dimensions of 889 policemen were taken through a manual anthropometric survey following the procedures in ISO 8559:1989 after each subject was interviewed on issues related to garment fit. The anthropometric data were pre-processed, key body dimensions were identified by principal components analysis and body types were clustered by the agglomerative hierarchical clustering algorithm and verified by the XGBoost classifier in a Python programming environment. The developed size charts were validated statistically using aggregate loss and accommodation rate.

About 44% of the subjects encountered fit problems every time they own new readymade uniforms. Lengths and side seams of shirts, and lengths and waist girths of trousers are the most frequently altered garment sites. Analysis of the anthropometric measurements resulted in 13 and 15 sizes for the upper and lower bodies, respectively. Moreover, the comparison of the developed upper garment size chart with the existing size chart for a shirt showed a considerable difference. This indicates that inappropriate size charts create fit problems.

The study considers the analysis of fit problems and sizing system development in a less researched country. Moreover, the proposed data mining procedure and its application for size chart development is unique and workable.

This study intends to improve the quality of venous thromboembolism (VTE) prophylaxis practices including proper VTE risk assessment and the appropriate prophylaxis measures for surgical urology patients.

The authors applied the Six-Sigma define, measure, analyze, improve and control (DMAIC) improvement methodology in a pre–post interventional study that involved all adult patients above 18 years old indicated and scheduled for urology surgical interventions including endoscopic urological surgeries in a urology specialized 60-bed hospital. The pre-intervention sample included all patients meeting the inclusion criteria over a period of six months. Post-intervention sample included all patients meeting the inclusion criteria over a period of six months. The improvement areas included both the VTE risk assessment as well as the VTE prophylaxis prescription.

DMAIC methodology has achieved a substantial sustained improvement in surgical urology VTE prophylaxis practices with an average of 70% on both levels; VTE risk assessment practices and VTE prophylaxis prescribing practices were statistically significant. The post-intervention results also showed a statistically controlled process with no special cause variations. Based on the study results, the Six-Sigma DMAIC methodology can be considered of high value when applied in healthcare clinical practice improvement projects.

The project study includes some pitfalls that can be addressed as follows: 1. The lack of VTE rate incidence tracking. This limitation can be partly refuted when the authors conduct a literature review and explore that the VTE prophylaxis effectiveness had been proven with sufficient evidence to an extent that pushed several scientific societies to develop their own guidelines to support VTE prophylaxis. (Algattas et al., 2018). 2. Another limitation of this study can be that it handled only surgical patients and more specifically surgical urology patients. Of course, VTE prophylaxis is a crucial life-threatening problem not only for the surgical admitted patients but also for all the medical admitted patients either in hospital wards or ICUs. However, the prediction that surgical patients especially surgical urology patients are more prone to VTE development risk as they have -in several cases-two or three main additive risk factors which are age, procedure duration and malignancy in elderly men. (Tikkinen et al., 2014). So, the authors consider the study project to be a prototype that hopefully can be utilized for future study projects that will manage both other surgical specialty patients and medical patients on the national level and can track accurately and effectively report the VTE incidence rates.

Several recommendations can be extracted from the research project that is summarized in the following points: Paying focused attention to continuous healthcare quality improvement initiatives and projects as a main approach for healthcare improvement especially for the public health-related problems. This might be achieved through periodic region-specific or specialty-specific focus groups from which public health problems could be addressed and prioritized to be considered as a part of country healthcare campaigns regarding cost-utility and feasibility studies. The adoption of a system thinking approach in dealing with the improvement strategies; all efforts and resources are to be employed to achieve a common objective. This includes the generation of a national-wide electronic health information system that can aid in healthcare resource allocation and direct the healthcare efforts towards the most important, high-priority public health problems. Electronic national-wide health record is really an effort, and resources consuming activity, but actually, it's worth exerting efforts, and its valuable outcomes may be seen several years later. 3. Development of unified national specialized VTE prophylaxis pathways to standardize the patient-specific VTE prophylaxis plans. Standardization of healthcare pathways enables healthcare professionals to follow an evidence-based practice which will be reflected on the improvement of healthcare quality level, cost-effectiveness enhancement, and timely patient care on all levels especially in high critical areas like ER and ICU. 4. Incorporation of VTE prophylaxis costs in the universal health insurance diagnosis-related group (DRG) insurance packages and service pricing. Universal health insurance is a nationwide strategy that is aiming to cover all Egypt residents by the year 2030. Universal health insurance is being following the DRG reimbursement policy that is thought to control all the healthcare-associated costs so, the VTE prophylaxis costs shall be added as the main cost item to encourage all healthcare facilities to follow an evidence-based VTE prophylaxis pathway taking into consideration the high-risk patient categories who will definitely represent a high-cost burden on the long run if they suffer a VTE event.

DMAIC improvement methodology applications in healthcare are still relatively limited, especially on the clinical level. The study can be considered one of a kind in Egypt dealing with a comprehensive DMAIC methodology application on the clinical level.

This article aims to focus on implementing Lean Six Sigma (LSS) in steel manufacturing to enhance productivity and quality in the galvanizing process line. In recent trends, manufacturing organizations have expressed strong interest in the LSS since they attempt to enhance its overall operations without imposing significant financial burdens.

This article used lean tools and Six Sigma's DMAIC (Define, Measure, Analyze, Improve and Control) with Yin's case study approach. This study tried to implement the LSS for the steel galvanizing process in order to reduce the number of defects using various LSS tools, including 5S, Value stream map (VSM), Pareto chart, cause and effect diagram, Design of experiments (DoE).

Results revealed a significant reduction in nonvalue-added time in the process, which led to improved productivity and Process cycle efficiency (PCE) attributed to applying lean-Kaizen techniques. By deploying the LSS, the overall PCE improved from 22% to 62%, and lead time was reduced from 1,347 min to 501 min. DoE results showed that the optimum process parameter levels decreased defects per unit steel sheet.

This research demonstrated how successful LSS implementation eliminates waste, improves process performance and accomplishes operational distinction in steel manufacturing.

Since low-cost/high-effect improvement initiatives have not been adequately presented, further research studies on adopting LSS in manufacturing sectors are needed. The cost-effective method of process improvement can be considered as an innovation.

The application of lean and quality improvement methods is very common in process improvement projects at organisational levels. The purpose of this research is to assess the adoption of Lean Six Sigma™ approaches for addressing a complex process-related issue in the coal industry.

The sticky coal problem was investigated from the perspective of process-related issues. Issues were addressed using a blended Lean value stream of supply chain interfaces and waste minimisation through the Six Sigma™ DMAIC problem-solving approach, taking into consideration cross-organisational processes.

It was found that the tendency to “solve the problem” at the receiving location without communication to the upstream was, and is still, a common practice that led to the main problem of downstream issues. The application of DMAIC Six Sigma™ helped to address the broader problem. The overall operations were improved significantly, showing the reduction of sticky coal/wagon hang-up in the downstream coal handling terminal.

The Lean Six Sigma approaches were adopted using DMAIC across cross-organisational supply chain processes. However, blending Lean and Six Sigma methods needs to be empirically tested across other sectors.

The proposed methodology, using a framework of Lean Six Sigma approaches, could be used to guide practitioners in addressing similar complex and recurring issues in the manufacturing sector.

This research introduces a novel approach to process analysis, selection and contextualised improvement using a combination of Lean Six Sigma™ tools, techniques and methodologies sustained within a supply chain with certified ISO 9001 quality management systems.

This research paper aims to analyze the scientific and grey literature on Quality 4.0 and zero-defect manufacturing (ZDM) frameworks to develop an integrated quality 4.0 framework (IQ4.0F) for quality improvement (QI) based on Six Sigma and machine learning (ML) techniques towards ZDM. The IQ4.0F aims to contribute to the advancement of defect prediction approaches in diverse manufacturing processes. Furthermore, the work enables a comprehensive analysis of process variables influencing product quality with emphasis on the use of supervised and unsupervised ML techniques in Six Sigma’s DMAIC (Define, Measure, Analyze, Improve and Control) cycle stage of “Analyze.”

The research methodology employed a systematic literature review (SLR) based on PRISMA guidelines to develop the integrated framework, followed by a real industrial case study set in the automotive industry to fulfill the objectives of verifying and validating the proposed IQ4.0F with primary data.

This research work demonstrates the value of a “stepwise framework” to facilitate a shift from conventional quality management systems (QMSs) to QMSs 4.0. It uses the IDEF0 modeling methodology and Six Sigma’s DMAIC cycle to structure the steps to be followed to adopt the Quality 4.0 paradigm for QI. It also proves the worth of integrating Six Sigma and ML techniques into the “Analyze” stage of the DMAIC cycle for improving defect prediction in manufacturing processes and supporting problem-solving activities for quality managers.

This research paper introduces a first-of-its-kind Quality 4.0 framework – the IQ4.0F. Each step of the IQ4.0F was verified and validated in an original industrial case study set in the automotive industry. It is the first Quality 4.0 framework, according to the SLR conducted, to utilize the principal component analysis technique as a substitute for “Screening Design” in the Design of Experiments phase and K-means clustering technique for multivariable analysis, identifying process parameters that significantly impact product quality. The proposed IQ4.0F not only empowers decision-makers with the knowledge to launch a Quality 4.0 initiative but also provides quality managers with a systematic problem-solving methodology for quality improvement.

This study aims to utilise Six Sigma in an Irish-based red meat processor to reduce process variability and improve yields.

This 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.

The 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.

The 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.

Organisations 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.

This 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.