Six-Sigma Project Research Articles

Application of Principle Component Analysis and logistic regression to support Six Sigma implementation in maintenance

Improving the efficiency of maintenance processes is one of the goals of companies. Improvement activities in this area require not only an appropriate maintenance strategy but also the use of a new approach to increase the efficiency of the process. This article focuses on using Six Sigma (SS) to improve maintenance processes. As an introduction, the generations of SS development are identified, and traditional and advanced analytical tools that can be useful in SS projects are reviewed. As part of the research, an example of the implementation of the SS project in the maintenance process using the DMAIC and selected advanced analytical methods, such as PCA and logistic regression, was presented. The PCA results showed that it was enough to have seven main components to keep about 84% of the information on variability. In developed logistic regression explained the impact of the individual factors affecting the availability of the machines. The identified factors and their interactions made it possible to define maintenance activities requiring improvements.

Six Sigma Project Prioritization and Selection Using AHP–CODAS Integration: A Case Study in Healthcare Industry

Given the complex nature of Six Sigma project (SSP) prioritization and selection processes, multicriteria decision-making (MCDM) methods may help organizations identify the most effective projects. Considering potential limitations of subjective methods and advantages of MCDM methods, this article proposes a model that integrates analytical hierarchy process (AHP) and combinative distance-based assessment (CODAS) in SSP prioritization and selection process. In the proposed approach, AHP is employed to assign criteria weights, and CODAS is performed to determine priorities of SSPs. CODAS was advanced in term of its threshold function. Differences between Euclidean distances of two alternatives were compared, based on the standard deviation of Euclidean distances of all alternatives to overcome the subjectivity. This is the first study that combines AHP and CODAS methods for SSP selection, and CODAS is used with objective threshold value computation, and developed for the healthcare industry. In this article, ten SSPs were evaluated for four key criteria groups as financial, operational, patient centric, and organizational main criteria groups. In total, 18 subcriteria were considered under these four main criteria groups. This article provides a support for executives who make implementation plans for the potential SSPs.

The performance improvement analysis using Six Sigma DMAIC methodology: A case study on Indian manufacturing company

The six-sigma methodology has been adopted by the industry as a business management tool to improve operational capabilities and reduce defects in any process. This study aims to present a case study on the implementation of the Six-Sigma DMAIC methodology with the purpose to reduce the rejection rate of rubber weather strips manufactured by XYZ Ltd. (name changed) situated in Gurugram, India. Weather strips are used in all four doors of cars for noise reduction, waterproofing, dust proofing, soundproofing, windproofing, and for improving air conditioning cooling, and heating effects. The overall rejection rate of both front and rear door rubber weather strips was 5.5% which was causing a huge loss to the company. The average rejection rate of rubber weather strips per day reduced from 5.5% to 3.08%. After implementing the Six-Sigma project findings the rejection was reduced from 153 pieces to 68 pieces helped the industry in saving the cost of a compound by Rs. 15,249 per month. The sigma level improved from 3.9 to 4.45 within three months with the implementation of one Six-Sigma project solution. The company was highly concerned about reducing the high rejection rate of rubber weather strips and decided to deploy Six Sigma DMAIC as a quality improvement tool. The industry was keen to reduce this high rejection rate to 2% and this target was materialized with the application of the Six-Sigma DMAIC methodology. The novelty of this study is to analyze performance improvement considering the Six Sigma DMAIC methodology to reduce the rejection rate of rubber weather strip manufacturing companies.

Using Process Capability Indices to Develop the Execution Models of DMAIC Process

The method of six-sigma and the index of process capability are both commonly used tools in the industrial community. Process engineers can follow five improvement steps of the six-sigma method, including “define”, “measure”, “analyze”, “improve”, and “control” (DMAIC), aiming to improve and enhance the process quality. However, none of these five improvement steps have a clear corresponding approach. This paper considered process capability indices not only a process quality evaluation tool widely used in the industrial community but also a process quality evaluation and analysis tool adopted by internal engineers. Therefore, this paper applied the method integrating process capability indices and statistical testing to develop execution models for the five improvement steps, DMAIC, of the six-sigma method. First, this paper, based on the concept of yield, not only deduced the relationship between the required value of the process capability index for the product and the process capability index value of the individual quality characteristic but also discussed the definition of the quality level of six-sigma as well as its relationship with the process capability index. Next, according to these results, five improvement execution models of the six-sigma method were developed and served as a reference for the process engineers in the industry to promote the performance of the six-sigma project. The proposed method in this paper applied various normal processes and combined the six-sigma method and process capability indices, both of which are tools commonly used in the industrial community. It also has taken into account the advantages of theoretical contribution and industrial acceptance.

Implementation of lean Six-Sigma project in enhancing health care service quality during COVID-19 pandemic.

The recent outbreak of coronavirus (COVID-19) pandemic has exposed the weakness of the existing healthcare facilities in developing countries, and Pakistan has no exception. The increasing amount of patients has made this condition more vulnerable to failure. It became difficult for health care management to handle the surge of patients. This case study is based on the XYZ hospital system of Pakistan. The hospital initiates passive immunization as a savior in the absence of a vaccine. The process initiates numerous challenges as the same facility was using for passive immunization and routine operations of the hospital. DMAIC lean sig-sigma problem-solving methodology has been adopted to Define, Measure, Analyze, Implement and Control the improvement process for smooth special and routine activities. The staff and patients were interviewed, their issues were listed, and a comprehensive solution was suggested to deal with operational uncertainties. The results identified various factors through VOC and SIPOC processes, prioritized using fishbone diagram, analyzed through Kano model, and finally proposed process improvement by incorporating Kaizen process improvement methodology. Other industries could use this set of tools to evaluate and optimize routine problems, which ultimately enhances the quality and reduces cost.

A cognitive map integrated intuitionistic fuzzy decision-making procedure for provider selection in project management

Organizations make use of project management methodologies, which provide an effective manner to achieve managerial goals, maintain the strength of the companies in increasing competition. Efficiency in planning, budgeting, and scheduling are provided so that high quality outputs are obtained through these processes. Agile project management methodology, which has been emerged from unpredictability of customer requirements and changeable business environment, is apt to cope with the failures of traditional project management tools. Besides, lean six-sigma project management methodology has become a combination of lean and six-sigma, which were opponent methodologies previously. This paper aims to determine the most suitable outsourcing provider alternative by presenting a novel cognitive maps-based intuitionistic fuzzy decision making procedure. Interrelationships among evaluation criteria are weighted employing intuitionistic fuzzy cognitive map technique because of the causal links among evaluation criteria, vagueness, fuzziness, and hesitation in data. Moreover, the most appropriate provider alternative for both agile and lean six-sigma project management methodologies is identified by utilizing intuitionistic fuzzy TOPSIS method, which aims for minimizing the closeness to the ideal solution while maximizing the distance from the anti-ideal solution in hesitative environment. The case study is carried out in a bank that performs in Turkish banking sector.

Six Sigma Methodology and an Application in the Textile Sector

In today's world of increasing international trade, companies are suggested to learn, implement and maintain new production techniques in place of traditional production systems in order to sustain their assets. In this approach, we have worked on “Six Sigma”, one of the strongest components of these so-called systems. Within the scope of this study, Six Sigma and its application areas are investigated, and as a result of the obtained findings, six sigma applications have been made in the textile industry where the quality is desired to be improved. In the study, conducted by following the six sigma's DMAIC (Define, Measure, Analyse, Improve, Control) steps, using the detection tools such as SIPOC (Suppliers, Inputs, Process, Outputs, Customers) diagrams, measurement systems analysis, root cause analysis, determination of poor quality sources and improvement on the basic problem have been made. The variation of the strips from the card and cer machines in the plant created great quality problems. Before the six sigma project, the average coefficient of variation value of the card machines was 2.02. With this project, the average coefficient of variation value of the card machines is aimed to be reduced to 0.5-1.5 range. After the six sigma project, the average voefficient of variation value of the card machines was reduced to 1.03.

Role of Human Knowledge and Communication on Operational Benefits Gained from Six Sigma

Six Sigma (SS) is a production philosophy focused on human experiences and knowledge, aimed to minimize defects of products and services. The appropriate implementation of SS requires an education process, reliable data analysis, efficient didactic material, statistical techniques and human knowledge to improve communication and operational benefits. In this article, we present a structural equation model integrating those aspects as latent variables and relating them with ten hypotheses. Data for hypothesis validation were gathered among 301 manufacturing companies, and assessed using partial least squares (PLS) to estimate direct, indirect, and total effects. As results, we found that access to reliable information, trusted analysis and knowledgeable management are crucial for SS implementation at the problem definition stage. Likewise, to execute and control SS projects, it is important to be trained in statistical techniques through clear didactic materials.

Prioritization of Six-Sigma project selection

PurposeWith increasing choice from a range of programs, improvement project selection within broader supply chain context and resource constraints has become a major research challenge. The purpose of this paper is to investigate the different criteria for selecting Six-Sigma (SS) projects based on previous studies. The study is supported by two grounded theories: resource-based view and institutional norms. The criteria include: first, business drivers for improvement and the common performance metrics deployed; second, the organization’s stakeholders needs; and third, process owner’s needs.Design/methodology/approachTo determine the relative importance of influencing factors, opinions were collected from 30 experienced practitioners including SS champions/master black-belts, company directors, consultants, and process owners through a series of interviews in small, medium, and large organizations including multi-national organizations. The evaluation of criteria is based on analytical hierarchy process.FindingsThe results show that impact on customer, financial impacts, and impact on operational goals are the most significant factors in selecting SS improvement project.Originality/valueThis study is a first attempt to determine the relative weight among SS project selection criteria, which help the practitioner to allocate their limited resources in implementing SS project.

The ‘pit-crew’ model for improving door-to-needle times in endovascular stroke therapy: a Six-Sigma project

BackgroundDelays in delivering endovascular stroke therapy adversely affect outcomes. Time-sensitive treatments such as stroke interventions benefit from methodically developed protocols. Clearly defined roles in these protocols allow for parallel processing...

Determining Six Sigma success factors in Turkey by using structural equation modeling

Since it includes strong statistical and executive techniques, Six Sigma (SS) succeeded in many countries and different sectors. Especially successful SS applications of many international companies have increased the interest of other companies. As a result of this, the number of implemented SS projects in various countries has increased. Although successful SS projects are often in mind, the number of failed projects because of various reasons is not as low as to be ignored. As well as there are many factors that affect the success level of SS projects, and these factors vary according to countries. In this study, a survey was applied to 117 people who have 1 of SS belts in order to determine success levels of the SS projects in Turkey. By using explanatory factor analysis and structural equation modeling, critical success factors were determined. According to the results, project selection and its scope, quality culture and defining and measuring of metrics were determined as the top factors that are affecting success levels of SS projects applied in Turkey. The results of the study were also compared with the results of similar projects implemented in other countries.

Project barriers to Green Belts through critical success factors

Purpose – The purpose of this paper is to utilize critical success factors (CSF) and identify items Green Belt (GB) practitioners note as barriers to completion of Six Sigma (SS) projects in a major manufacturer setting. Design/methodology/approach – The design of this paper is a descriptive study of a single location of a global manufacturer’s internal data and survey of accredited GBs who have completed an SS project for company accreditation utilizing company focus on CSFs. Findings – The results demonstrate the GB practitioners have competing priorities, have time constraints and lack project management skills that reduce timely completion of SS projects. Top management responsibility for SS GB projects are defined through the CSFs of leadership, project management and project selection. Research limitations/implications – This study pertains to the single manufacturing location of a major, multinational company. The survey of SS GBs is limited to those individuals who have become accredited to company requirements, in the initial stages of strategic implementation, resulting in a small sample size. All GB projects follow the DMAIC (Define, Measure, Analyze, Improve and Control) methodology. The implications may be reproduced in similar environments where GBs conduct SS projects to test the robustness of the study. Practical implications – This study underscores the importance of proper coaching and mentoring of SS practitioners, especially those who are expected to contribute as GBs in a part-time manner. Implementation of SS goes beyond initial deployment and requires active mentoring of GB practitioners to make sure that SS projects get proper focus. The results are relevant to both researchers and practitioners. Originality/value – This paper examines SS projects with a GB perspective, an important contribution to SS but lacking in the literature. While GBs are important to SS implementation, and serve as a pathway to fulltime SS personnel, there are few studies that note this work. This study will support practitioners in the importance of wider SS deployment through active support of GBs, where top management responsibility for GB success is defined through CSFs for improvement.

A Team Based Approach to Reducing Catheter-associated Urinary Tract Infections

Catheter-associated urinary tract infections (CAUTI), the most common healthcare-associated infection, lead to substantial morbidity and increased healthcare costs. In 2011, an 810-bed academic hospital, set a 2-year quality goal of 5% CAUTI reduction each year from the previous year. Achievement of the goal was tied to a hospital-wide employee financial incentive. Unit-level Six-Sigma Projects guided our multidisciplinary strategy to three focus areas for urinary catheters: appropriate insertion indication, maintenance, and timely removal.

Evaluation of the Success of Six Sigma Projects by Data Envelopment Analysis

Six sigma provides a methodology for problem solving by using project management tools systematically. Theprimary aim of six sigma applications is minimizing defects. Minimizing defects will ensure the outputs andcustomers’ satisfaction increase and the costs decrease. However, the number of the firms that state that theycouldn’t get the results from six sigma programs as they expected is not few. The main reason for failure of sixsigma programs is not to balance the costs with gains of six sigma projects. So, evaluating the performance of sixsigma projects is an important issue for the firms that apply six sigma programs.In this paper, data envelopment analysis was used to evaluate the performance of six sigma projects which isstated as critical success factor for six sigma programs. Data envelopment analysis which is based on linearprogramming helps to determine the efficient and inefficient decision making units by comparing the relativeefficiencies of decision making units. Recently, there are some papers in the literature which apply dataenvelopment analysis for evaluating the performance of R&D and software projects. There are also some paperswhich discuss the application of data envelopment analysis in the selection of six sigma projects. Howeverevaluating the performance of six sigma projects with data envelopment analysis was not discussedcomprehensively in the literature. This paper is original by showing that data envelopment analysis which can beused in evaluating the performance of organizations can also be used for evaluating the performance of six sigmaprojects. The application of data envelopment analysis for evaluating the performance of six sigma projects wasshown by a case study.

Integrating Six Sigma tools using team‐learning processes

PurposeMany of the benefits of Six Sigma (SS) programs stem from the proper use of tools within team‐based improvement projects. However, teams often struggle with selecting and integrating the best tools to use in their projects, compromising not only the project completion but also the solution implementation and sustainability over time. The purpose of this paper is to present a systematic approach to articulate SS tools based on team learning, in order to facilitate tool selection and alignment, team collaboration, solution ownership, and overall solution quality and sustainability.Design/methodology/approachThe action research approach was adopted to explore the link between team learning practices and tool application that combined participation in and reflection upon a successful SS project from the electronic manufacturing industry.FindingsSS tools can be more effectively aligned and integrated with team learning practices. Team discussions and dialogues on SS tool outcomes are pivotal to the development of practical interpretations, allowing the team to create new knowledge, change mental models regarding pre‐conceived solutions, and enable team members to collectively select and interpret tools in a coherent way, leading to the buy‐in of the final solution and the SS program as a whole.Originality/valueThe paper yields value to practitioners and researchers interested in successful SS project execution and program sustainability, by providing a team‐learning approach for effective tool integration. In addition, this paper highlights the need for additional research on team learning within SS programs.

Assessment and optimization of human factors

Accelerating global growth and frequent man-made crises generate uncertainties and demand new alternatives and viable solutions in technology, energy, and health as well as political, ecological, and media organizations. This paper focuses on the key role of human communication and the increasing signifi cance of ‘human factors’ (HF). In technology, currently 70 to 90% of all defi ciencies originate from the complex HF-area, which includes human errors as well. In spite of human complexity, it is useful to study nature and compare it with our communication and behavior. Starting from control engineering and biocybernetics, a simple building block is discussed: the control circuit with negative feedback and stability. Two examples are then presented from biology and mechanics to illustrate communication, cooperation, self-regulation and stabilizing mechanisms: (i) The mutualism between a tree and animals with fast adaptation to new symbioses and (ii) The elastoplastic, self-controlled buckling with ‘snap-through’ and stabilizing mechanisms. Observing human behaviour and nature lead to an integral mental approach with the Limbionik® Method (LM), which focuses on interand intrapersonal communication. The HF is assessed, utilizing what is called the UB or ‘Urbewusstsein’. Next the objectives, assumptions, features and the basic LM methodology are discussed in detail. First experience with individuals and small groups is reported, covering success, performance, work and health. Generally speaking, the LM makes individuals and organizations fi t for the accelerating pace of today’s life. Three examples illustrate a successful LM application: (i) The treatment of clients, suffering from a burnout syndrome. Self-realization of their UB with personal fi lters leads them to choose a stable, lasting recovery path. (ii) A Six-Sigma project support in engineering and fabrication of large steam turbines. Including the HF into Six Sigma allowed to identify the majority of human-related root causes (80% of total) and defi ne remedies. (iii) An engineering/fabrication improvement project for large turbogenerators, involving the varnishing process of stator core sheets. Corrections included the introduction of robust machine and human processes. After summarizing the salient characteristics of the LM philosophy, future activities on both HF and LM are outlined.

Operational risk management: Using loss-data more effectively

Using operational risk loss-data is paying off. It can now be demonstrated that the operational risk in processing transactions is containable. Continuous infrastructure improvements (such as via lessons learned, concentration analysis or Six-Sigma projects) and diligent preparation of changes (especially outsourcings and new products) are the instruments of choice to do so.The common causes of operational risks continue to be mishaps in the client interface or trader fraud. Operational risk remains rather low correlated with credit and market risk, and thus hardly increased during the financial crisis. This is despite the fact that operational risk within credit and market risk contributed to some of the biggest losses the industry has experienced over recent years.Based on the loss-data gathered over the last 10–15 years, the operational risk discipline has matured sufficiently to inform strategic and investment decision-taking, in the same way as credit or market risk has done for many years. The operational risk inherent in investments (such as acquisitions or new products) can now be priced, as well as divestments (such as outsourcings or reduced controls), and can provide a business case of prevented operational risk losses to justify IT developments or increased resources for new controls. This requires sophisticated capital modelling, plenty of correlation and root-cause research, and dedicated development of predictive and sensitive key risk indicators.Using loss-data creates transparency and validates many pre-perceptions around operational risk. At the same time, it drives the continuous improvement of the loss-data capturing process with regards to completeness and correctness. Clear governance, systematic data reconciliation and quality assurance are key elements to establishing the foundations for operational risk managers to earn their seat at the decision-making table in a modern financial organisation.

Disentangling causal relationships of a manufacturing process using genetic algorithms and six-sigma techniques

The manufacturing of hard disk drive storage is a highly competitive industry due to ever changing technology and the introduction of solid-state storage. Therefore manufacturers have introduced business improvement techniques to help leverage manufacturing advantages over competitors. This paper aims to investigate the integration of business improvement methodologies with intelligent techniques for the improvement of manufacturing efficiency. The authors use a case study based on the disentangling of causal relationships during the manufacturing of hard disk drive components. This paper reports on the application of intelligent system techniques to aid implementation of business improvement methodologies within this manufacturing environment. The results reported highlight that significant gains may be made when utilizing such a hybrid approach. The approach is validated by determining the secondary effects that degraded a previously implemented six-sigma project and also by identifying areas of investigation that enabled engineers to improve process yield.

Six-Sigma project selection using national quality award criteria and Delphi fuzzy multiple criteria decision-making method

Six-Sigma is a tactical tool of significant value in achieving operational excellence. The project selection decision, under a resources constraint, is the early stage of implementation for a Six-Sigma intervention. The project selection decision is challenging due to its fuzzy group decision-making aspect inherent to the problem. The present study proposes to adopt national quality award criteria as the Six-Sigma project selection criteria, and proposes a hierarchical criteria evaluation process. The strategic criteria are evaluated by the management team using a Delphi fuzzy multiple criteria decision-making method. Then, the tactical sub-criteria which contain additional operational issues are evaluated by the Six-Sigma Champion. The proposed methodology is successfully applied in solving the project selection problem deriving from a component manufacturer. The empirical outcomes are promising. Moreover, the results show that the higher a project’s priority is, the greater the financial gains will be on completion of the project. Accordingly, the proposed methodology can prioritize the financial gain – which is the key performance indicator for a Six-Sigma project. Additionally, the quality status of the case company has been significantly improved through implementation of the Six-Sigma project. The systematic evaluation process also influences employees to adopt an analytical operations philosophy. Moreover, the commercial objectives of the company are brought into focus by the proposed methodology.

Ford-Otosan Optimizes Its Stocks Using a Six-Sigma Framework

Ford-Otosan, a producer of light and medium commercial vehicles, wanted to improve order-to-delivery times while reducing costs in the course of expanding its capacity. It started a six-sigma project to reduce finished-goods inventory while ensuring on-time shipping of orders. Finished-goods inventory consists of vehicles ready for shipment and those needing additional parts or testing. We developed an integer-programming model in a spreadsheet environment to reduce the number of vehicles awaiting shipment and a shop-floor control system to handle perturbations caused by vehicles that require additional work. Upon implementing these systems, we obtained and analyzed data from the new process and found major improvements, including savings of about $1.2 million in 2003 and $2.6 million in 2004.