Sigma Define-Measure-Analyze-Improve-Control Methodology Research Articles

A Six Sigma DMAIC methodology as a support tool for Health Technology Assessment of two antibiotics.

Health Technology Assessment (HTA) and Six Sigma (SS) have largely proved their reliability in the healthcare context. The former focuses on the assessment of health technologies to be introduced in a healthcare system. The latter deals with the improvement of the quality of services, reducing errors and variability in the healthcare processes. Both the approaches demand a detailed analysis, evidence-based decisions, and efficient control plans. In this paper, the SS is applied as a support tool for HTA of two antibiotics with the final aim of assessing their clinical and organizational impact in terms of postoperative Length Of Stay (LOS) for patients undergoing tongue cancer surgery. More specifically, the SS has been implemented through its main tool, namely the DMAIC (Define, Measure, Analyse, Improve, Control) cycle. Moreover, within the DMAIC cycle, a modelling approach based on a multiple linear regression analysis technique is introduced, in the Control phase, to add complementary information and confirm the results obtained by the statistical analysis performed within the other phases of the SS DMAIC. The obtained results show that the proposed methodology is effective to determine the clinical and organizational impact of each of the examined antibiotics, when LOS is taken as a measure of performance, and guide the decision-making process. Furthermore, our study provides a systematic procedure which, properly combining different and well-assessed tools available in the literature, demonstrated to be a useful guidance for choosing the right treatment based on the available data in the specific circumstance.

Advanced Care Planning and Palliative Care in the Heart Failure Population

Purpose The aim of this Lean Six Sigma project was to identify Heart Failure patients who were appropriate for Palliative Care and to facilitate advanced care planning documentation. Background Heart failure (HF) is a chronic debilitating disease that can lead to high mortality and frequent hospital admissions. The downward trajectory of the disease process frequently results in unpredictable decompensation followed by improvement in symptoms. The waxing and waning of symptoms can create a barrier to timely palliative care (PC) referral that bridges chronic services and end-of-life-care. At Memorial Medical Center PC referrals were 8.2% for HF patients 6/2015 – 5/2016 versus the national median of 14%. Evidence supports the HF team can be instrumental in helping patients and their families discuss symptom management, treatment options and PC services. Methods Lean Six Sigma DMAIC (Define, Measure, Analyze, Improve, Control) methodology was used for the project. A literature search was performed to identify best practices, including the 2013 ACCF/AHA Guidelines for Management of HF. A PC screening tool currently utilized in the inpatient setting that is valid, reliable and evidence-based, was implemented in the outpatient clinic. An Advanced Care Plan template was developed in the clinic note to address the advanced planning conversation. A process was also developed for identifying and scanning advanced directives into the patient chart with activation of a red banner that easily identifies the advanced directives. Patients who report not having advanced directives were provided educational material regarding advanced directives. The change strategy involved stakeholders and training for all members of the care team on the use and documentation of the Advanced Care Plan template. Communication on implementation and results occurred through unit-based-council meetings, emails and direct conversations with APNs, RNs and other interdisciplinary HF clinic team members. Ongoing dialog occurred between the HF team, physicians, leadership and other clinicians at the HF Steering Committee meetings and monthly project “tollgate” meetings. The change process was implemented in stages with assessment following each process change. The first phase was assessment of the patient's advanced directives and the process of scanning them into the patient chart with activation of the red banner. The second phase was implementation of the Palliative Care Tool. The final stage was the implementation of the Advanced Care Plan template. Descriptive statistics and t testing were used to analyze the data. Conclusions Advanced Care Planning conversations are an important aspect of the care provided to HF patients. A template that allows for patient individualization, provides consistency of documentation among providers and serves as a trigger is critical to capture conversations and provide transparency for all caregivers. Nurses are in a key position to screen, initiate and facilitate Palliative Care conversations among HF patients and their families. Results Patient charts (N=51) were audited prior intervention which revealed 53% had documentation of Advanced Directives. RN documentation of Advanced Directives improved by (N=101) 81% (p=0.000). Advanced Directive documents scanned into EMR also improved from 24% to 67% (p=0.003). The PC tool was utilized in 30 of 41 (73%) of appropriate patients (1st consult or 1st follow-up visit post hospitalization for all patients seen by all providers). Two patients screened with high scores. Discussions were documented with both patients regarding disease progression and PC. One patient was sent back to their primary care provider for a PC referral. The other patient had multiple recent hospitalizations prior to the HF Clinic appointment, declined a PC referral at the time, but was hospitalized the following week, discharged to hospice care and died one month later without further hospital admissions. After implementation of the Advanced Care Plan template (N=38) 79% of patients seen in the HF clinic had documentation that was transparent to other providers (p=0.000).

Six Sigma Implementation Efficiency in an Automobile Firm

Six Sigma a new paradigm and fast growing manufacturing strategy executed and streamlined across recent automobile firms. This methodology have been faster pace of growth worldwide which helps to enhance productivity and quality improvement on existing process robust to monitor quality variations across manufacturing firms. To respond to quality changes happening every firm within the industry aligning themselves to show continuous process changes by building their existing infrastructure through Kaizen, JIT manufacturing, Business Process Reengineering practices to attain global competitive advantage and also to meet changing global customer demands by providing high quality products. This study results discussed six sigma implementation practices and various defects analysis methods followed in an automobile firm for quality and productivity improvement. This study used systematic literature review approach by analyzing existing major defective parts from the overall manufacturing products of the firm and has been analyzed using quality tools like Pareto analysis, Cause and Effect Analysis, 5 - Why analysis and Scatter diagram. The findings from this study definitely allow future researchers to progress their research activity in this field of activity helps them to open up new opportunities on six implementation issues to develop and evolve. Overall this study concluded with the suggestion of implementing Six Sigma DMAIC (Define–Measure-Analyze-Improve-Control) methodology which provides a framework to identify, quantify and eliminate sources of variation by optimizing operation variables which helps the firm to improve and sustain performance. The motive of this study is to achieve higher quality level for mutual benefits of organization and customer.

Application of Six Sigma methodology in reducing oil consumption in a manufacturing process

This article discusses the successful application of Six Sigma define-measure-analyse-improve-control (DMAIC) methodology in an engineering industry for reducing the oil consumption during a machining process. As the cutting oil prices are very high, excessive consumption of cutting oil will significantly increase production cost. This will even affect the profitability and competitiveness of the organisation. Hence, Six Sigma DMAIC methodology was applied to this process for reducing the oil consumption. The potential causes of high oil consumption were identified through a brainstorming session. Based on the analysis of the data on these causes, root causes were identified and solutions were implemented. As a result of this study, the oil consumption was reduced by 40%, which was a remarkable achievement for the organisation.

Application of Six Sigma methodology in reducing oil consumption in a manufacturing process

This article discusses the successful application of Six Sigma define-measure-analyse-improve-control (DMAIC) methodology in an engineering industry for reducing the oil consumption during a machining process. As the cutting oil prices are very high, excessive consumption of cutting oil will significantly increase production cost. This will even affect the profitability and competitiveness of the organisation. Hence, Six Sigma DMAIC methodology was applied to this process for reducing the oil consumption. The potential causes of high oil consumption were identified through a brainstorming session. Based on the analysis of the data on these causes, root causes were identified and solutions were implemented. As a result of this study, the oil consumption was reduced by 40%, which was a remarkable achievement for the organisation.

Framework for continuous improvement of production processes

This research introduces a new approach of using Six Sigma DMAIC (Define, Measure, Analyse, Improve, Control) methodology. This approach integrates various tools and methods into a single framework, which consists of five steps. In the Define step, problems and main Key Performance Indicators (KPIs) are identified. In the Measure step, the modified Failure Classifier (FC), i.e. DOE-NE-STD-1004-92 is applied, which enables to specify the types of failures for each operation during the production process. Also, Failure Mode and Effect Analysis (FMEA) is used to measure the weight of failures by calculating the Risk Priority Number (RPN) value. In order to indicate the quality level of process/product the Process/Product Sigma Performance Level (PSPL) is calculated based on the FMEA results. Using the RPN values from FMEA the variability of process by failures, operations and work centres are observed. In addition, costs of the components are calculated, which enable to measure the impact of failures on the final product cost. A new method of analysis is introduced, in which various charts created in the Measure step are compared. Analysis step facilitates the subsequent Improve and Control steps, where appropriate changes in the manufacturing process are implemented and sustained. The objective of the new framework is to perform continuous improvement of production processes in the way that enables engineers to discover the critical problems that have financial impact on the final product. This framework provides new ways of monitoring and eliminating failures for production processes continuous improvement, by focusing on the KPIs important for business success. In this paper, the background and the key concepts of Six Sigma are described and the proposed Six Sigma DMAIC framework is explained. The implementation of this framework is verified by computational experiment followed by conclusion section.DOI: http://dx.doi.org/10.5755/j01.ee.26.2.6969

Application of Six Sigma methodology in a small-scale foundry industry

Purpose – The purpose of this article is to illustrate how the Six Sigma methodology was applied to a small-scale foundry industry to reduce the rejections and rework in one of its processes. Design/methodology/approach – The research reported in this paper is based on a case study carried out in an industry using the Six Sigma Define-Measure-Analyze-Improve-Control (DMAIC) approach and its application in improving the leaf spring manufacturing process of a foundry shop. Findings – The root causes for the problem of rejection and rework were identified through data-based analysis at different stages in the project. The process parameters were optimized and measures for sustainability of the results were incorporated in the process. As a result of this study, the overall rejection was reduced from 48.33 to 0.79 per cent, which was a remarkable achievement for this small-scale industry. This was leading to improvement in on-time delivery to the customer. The finance department of the company estimated the annualized savings due to the reduction in rejection and was to the tune of USD8,000 per year. Research limitations/implications – The paper is based on a single case study executed in a company, and hence, there is limitation in generalizing the specific results from the study. However, the approach adopted and the learning from this study can be generalized. Originality/value – This article illustrates the step-by-step application of Six Sigma DMAIC methodology in a small-scale foundry industry to solve an age-old problem in the organization. Thus, this article will be helpful for those professionals who are interested in implementing Six Sigma to such industries.

Reducing the voluntary turnover rate of dispatched employees by the DMAIC process

The purpose of this paper is to discuss how Six Sigma DMAIC (Define, Measure, Analyse, Improve, Control) methodology was applied to reduce the voluntary turnover rate of dispatched employees in company M. The root causes of turnover were identified, including salary and benefits, converting opportunity, job hunting, etc. Improvement measures including increasing salary, providing converting opportunities, developing career plans, providing training, job rotation, and humane care for the dispatched employees were proposed and implemented. After taking improvement actions, the weekly average turnover rate of dispatched employees declined from 2.5% to 1.4%, with concomitant savings of human resource costs and improvements in production quality and yield stability. The study provides a new perspective for enterprises interested in reducing dispatched employee turnover.

Sigma Level Improvements in MIG Welding using DMAIC Approach

There is no substitute to quality and for survival in todays cutthroat market, business corporations must offer products with excellent value. In most instances, more superior the quality the more competitive their product becomes; especially when they are competing in the buyer market. In addition to the need of delivering a quality product, economic factors such as global competition, declining profit margin, customer demand for variety of products and reduced leadtime etc. have a major impact on manufacturing industries. This paper presents a case study regarding the implementation of Six Sigma DMAIC (DefineMeasure-Analyze-Improve-Control) methodology in a welding facility of a Steering manufacturing plant in Gurgaon and discusses the statistical analysis performed for bringing the welding processes in the desired sigma limits. During this study, first of all the vital factors causing defects in welding were identified and forcing the re-work on facility and also affecting the process and then certain improvement procedures were taken to improve the capability of one individual welding process, Metal Inert Gas (MIG) welding process. Cost of re-work was also measured before and after implementation to validate the improvement results achieved.

Process improvement in farm equipment sector (FES): a case on Six Sigma adoption

Purpose – The purpose of this study is to exhibit how a leading company operating in farm equipment sector (FES) in India utilized Six Sigma statistical tools to reduce field failures of tractor assembly and thereby improved customer satisfaction. Design/methodology/approach – The study adopted Six Sigma define-measure-analyse-improve-control (DMAIC) methodology in order to identify numerous critical process improvements. Findings – After completing the define, measure and analyze phase, it was found that the processes of lapping, spring scragging, and assembly of valve body were incapable. The major recommendations made during the improve phase were to change the hydraulic oil; replacement of manual grinder with tool and cutter grinder for flat drill grinding; automation of lapping system; improvement in spring scragging system, change in piston design, etc. As a result of the implementation of remedies, field failure reduced by 95 percent producing a cost saving of almost INR 4.366 million/annum. Originality/value – This specific case demonstrates the successful application of Six Sigma DMAIC methodology in FES for driving down the field failures and improved customer satisfaction.

Adoption of Six Sigma DMAIC to reduce cost of poor quality

Purpose – The purpose of this paper is to demonstrate the systematic application of Six Sigma tools for identification and reduction of cost of poor quality (COPQ). The study examines one of the chronic problems of failure of cooling fan assembly at repair division of a company dealing in helicopter components. Design/methodology/approach – The case adopted Six Sigma Define-Measure-Analyze-Improve-Control (DMAIC) methodology to achieve the goal of reduction in COPQ. Findings – After completing the Define, Measure and Analyze phase, it was found that use of extreme tolerances and cross-fitment of bearings are the root cause of cooling fan assembly failure. The major recommendations made during the Improve phase were to design a bearing matching software for improving the cross-fitment of bearings and to procure a hydraulic jig with electronic jig instead of manual jig. The value of implementing these recommended solutions equate to a saving of INR 34 lacs per annum. Since it was a chronic problem, the company expects this to be a recurring saving. Originality/value – This specific case exhibits the successful application of Six Sigma DMAIC methodology in repair and maintenance for driving down the cost of failure and improved processes.

Process improvement through Six Sigma with Beta correction: a case study of manufacturing company

This article discusses the successful implementation of Six Sigma DMAIC (Define–Measure–Analyse–Improve–Control) methodology along with Beta correction technique in an automotive part manufacturing company. The implementation of Six Sigma approach resulted in reduction of process capability-related problems and improved the first pass yield from 94.86 % to 99.48 %. After studying the baseline performance of the process, a brainstorming session was conducted with all stakeholders of the process for identifying the potential causes of the problem. Data were collected on all the identified potential causes and various statistical analyses like regression analysis, hypothesis testing, and Taguchi methods were performed for identifying the root causes. Solutions were identified and implemented for the validated root causes, and results were observed. The Beta correction technique was introduced for monitoring the process in the control phase. Implementation of Six Sigma methodology with Beta correction technique had a significant financial impact on the profitability of the company. An approximate saving of US$87,000 per annum was reported, which is in addition to the customer-facing benefits of improved quality on returns and sales. This study contributes uniquely by elucidating the synergistic impact of Beta correction for greater effectiveness of Six Sigma programmes in the engineering industry.

Reducing patient waiting time in a pathology department using the Six Sigma methodology

Purpose – Six Sigma as a powerful business strategy has been around for almost a decade and has grown exponentially in healthcare industry during the past five years. During the last five years or so, many leading healthcare institutions have implemented Lean and Six Sigma methodologies with remarkable results in terms of reducing ER cycle time, increasing timely completion of medical records, increasing bed availability, improving patient flow, enhancing patient safety, increasing capacity of the theatres, reducing medication errors and so on and so forth. The purpose of this paper is to reduce patient waiting time in a pathology department of a super-specialty hospital attached to a manufacturing company. The average waiting time for patients was estimated to be around 24 minutes with a standard deviation of 17.5 minutes. This was not acceptable in the eyes of patients, and hence, it was highly desirable to understand the reasons for excessive waiting times as well as the root causes of variation in the waiting times. Design/methodology/approach – The Six Sigma DMAIC (Define-Measure-Analyze-Improve-Control) methodology was adopted for reducing the average waiting time and variation in waiting times. Findings – During the execution of this case study, a number of non-value added activities were identified within the process and actions were initiated to systematically eliminate different forms of waste using the principles of Lean thinking. Cause-and-effect analysis was carried out to identify the potential causes for unacceptable waiting times and data were collected to validate these causes. The tools such as hypothesis test, box plot, dot plot etc. were used to analyse the data through Minitab statistical software and conclusions were made. As a result of this project, the average waiting time reduced from 24 minutes to 11 minutes (i.e. over 50 per cent reduction), the standard deviation reduced from 17.5 minutes to 10.04 minutes(over 40 per cent reduction). Originality/value – This paper highlights the use of data driven and scientific problem solving methodology such as Six Sigma with the involvement of hospital staff members like nurses, clinicians and technicians. The results of the case study have provided greater stimulus among the senior management team for deploying the use of DMAIC methodology for tackling process and patient care related problems in the hospital.

Security Policy Management Process within Six Sigma Framework

This paper presents a management process for creating adaptive, real-time security policies within the Six Sigma (6σ) framework. A key challenge for the creation of a management process is the integration with models of known Industrial processes. One of the most used industrial process models is Six Sigma which is a business management model wherein customer centric needs are put in perspective with business data to create an efficient system. The security policy creation and management process proposed in this paper is based on the Six Sigma model and presents a method to adapt security goals and risk management of a computing service. By formalizing a security policy management process within an industrial process model, the adaptability of this model to existing industrial tools is seamless and offers a clear risk based policy decision framework. In particular, this paper presents the necessary tools and procedures to map Six Sigma DMAIC (Define-Measure-Analyze-Improve-Control) methodology to security policy management.

Minimization Of Rewok In Belt Industry Using Dmaic

The fast changing economic conditions such as global competition, declining profit margin, customer demand for high quality product, product variety and reduced lead–time etc. had a major impact on manufacturing industries. To respond to these needs a new paradigm in this area of manufacturing strategies is Six Sigma. The Six Sigma approach has been increasingly adopted world wide in the manufacturing sector in order to enhance productivity and quality performance and to make the process robust to quality variations. This paper discusses the quality and productivity improvement in a manufacturing enterprise through a case study. The paper deals with an application of Six Sigma DMAIC(Define–Measure- Analyze-Improve-Control) methodology in an industry which provides a framework to identify, quantify and eliminate sources of variation in an operational process in question, to optimize the operation variables, improve and sustain performance viz. process yield with well-executed control plans. Six Sigma improves the process performance (process yield) of the critical operational process, leading to better utilization of resources, decreases variations & maintains consistent quality of the process output.

Quality and productivity improvement using Six Sigma and Taguchi methods

This research study proposes an innovative analysis for controlling the defects in aluminium die casting business. Six Sigma DMAIC (define, measure, analyse, improve, control) methodology is used to analyse the problem. Process validation is done with Taguchi DOE and ANOVA analysis at 95% confidence showed that the metal temperature is the vital process parameter causing defects. Confirmation experiment at optimum process parameter level showed defect level reduced from 17.22% to 4.8%. Since aluminium die casting is one of the most widely used process for producing automobile components, this study provides a methodology for improving the die casting business excellence.

Improving Patient Satisfaction with Pain Management Using Six Sigma Tools

Patient satisfaction as a direct and public measure of quality of care is changing the way hospitals address quality improvement. The feasibility of using the Six Sigma DMAIC (Define, Measure, Analyze, Improve, Control) methodology to improve patient satisfaction as it relates to pain management was evaluated. This project used the DMAIC methodology to improve patients' overall satisfaction with pain management on two inpatient units in an urban academic medical center. Pre- and postintervention patient surveys were conducted. The DMAIC methodology provided a data-driven structure to determine the optimal improvement strategies, as well as a long-term plan for maintaining any improvements. In addition, the Change Acceleration Process (CAP) was used throughout the project's various DMAIC stages to further the work of the team by creating a shared need to meet the objectives of the project. Overall satisfaction with pain management "excellent" ratings increased from 37% to 54%. Both units surpassed the goal of at least 50% of responses in the "excellent" category. Several key drivers of satisfaction with pain management were uncovered in the Analyze phase of the project, and each saw rating increases from the pre-intervention to postintervention surveys. Ongoing monitoring by the hospital inpatient satisfaction survey showed that the pain satisfaction score improved in subsequent quarters as compared with the pre-intervention period. The Six Sigma DMAIC methodology can be used successfully to improve patient satisfaction. The project led to measurable improvements in patient satisfaction with pain management, which have endured past the duration of the Six Sigma project. The Control phase of DMAIC allows the improvements to be incorporated into daily operations.