Search results

In this era of globalisation, as competition intensifies, providing quality products and services has become a competitive advantage and a need to ensure survival. The Six Sigma's problem-solving methodology DMAIC has been one of the several techniques used by organisations to improve the quality of their products and services. This paper aims to demonstrate the empirical application of Six Sigma and DMAIC to reduce product defects within a rubber gloves manufacturing organisation.

The paper follows the DMAIC methodology to systematically investigate the root cause of defects and provide a solution to reduce/eliminate them. In particular, the design of experiments, hypothesis testing and two-way analysis of variance techniques were combined to statistically determine whether two key process variables, oven's temperature and conveyor's speed, had an impact on the number of defects produced, as well as to define their optimum values needed to reduce/eliminate the defects.

The analysis from employing Six Sigma and DMAIC indicated that the oven's temperature and conveyor's speed influenced the amount of defective gloves produced. After optimising these two process variables, a reduction of about 50 per cent in the “leaking” gloves defect was achieved, which helped the organisation studied to reduce its defects per million opportunities from 195,095 to 83,750 and thus improve its sigma level from 2.4 to 2.9.

This paper can be used as a guiding reference for managers and engineers to undertake specific process improvement projects, in their organisations, similar to the one presented in this paper.

This study presents an industrial case which demonstrates how the application of Six Sigma and DMAIC can help manufacturing organisations to achieve quality improvements in their processes and thus contribute to their search for process excellence.

This paper aims to investigate the sources of variation in aluminum profiles hot extrusion process for the purpose of process capability improvement at National Aluminum and Profiles Company (NAPCO) in Palestine. The critical-to-quality characteristics (CTQ) have been determined as base variables to measure the process capability.

The DMAIC (Define, Measure, Analyze, Improve, and Control) Lean Six Sigma (LSS) approach has been adopted to conduct this study. More specifically, historical data analysis and PARETO charts have been employed. The defects' root causes have been determined using the cause-and-effect diagram and decision matrix. A course of suggested actions has been established to help in improving extrusion process capability. Minitab-18 software was used for conducting inferential statistical analysis. A case study considering a geometry CTQ of D3 dimension of bottom sash aluminum profiles 4,332 was selected for analysis.

The results indicated a reduction in DPMO from 89,649 to 15,659, sigma level was improved from 2.84 to 3.65, process yield was improved from 91.04% to 98.43% and cost was reduced from U$75,972 to U$13,250.9 (i.e. a saving of U$62,721). Studying and improving the sigma level of the extrusion process would yield fewer defective products and consequently fewer customer complaints. A validation process which has been conducted during the year 2019 showed a consistent improvement that aligns with the first stage of improvement made on October 1, 2018.

This study focuses on only one critical quality characteristic (CTQ), namely, a geometry CTQ of D3 dimension of bottom sash aluminum profiles 4,332 produced by NAPCO was selected for analysis.

This study would be useful for researchers and practitioners to improve the process capability in aluminum profiles manufacturing industries in general and hot extrusion processes in particular.

Many previous studies on applying LSS-DMAIC methodology have been conducted in aluminum industries in developed countries. According to the literature, it is highly recommended to have more case studies of applying LSS-DMAIC in different industries in developing countries. NAPCO is the only aluminum manufacturing plant in Palestine that hotly extrudes and coats aluminum profiles. Hence, the present study is the first of its kind in NAPCO and in Palestine. Projecting the assessment of the impact of process improvement opportunities and capability analyses into monetary measures are also innovative.

The purpose of this paper is to develop a simulation-based value function (VF) that combines multiple key performance indicators (KPIs) into a unified Sigma rating (SR) for system-level performance assessment and improvement.

Simulation is used as a platform for assessing the multiple KPIs at the system level. A simple additive VF is formed to combine the KPIs into a unified SR using the analytical hierarchy process and the entropy method. Value mapping is utilized to resolve the conflict among KPIs and generate a unified value. These methods are integrated into the standard Six Sigma define-measure-analyze-improve-control (DMAIC) process.

Simulation results provided the Six Sigma DMAIC process with system-level performance measurement and analysis based on multiple KPIs. The developed VF successfully generated unified SRs that were used to assess various performance improvement plans.

The accuracy and credibility of the results obtained from using the proposed VF are highly dependent on the availability of pertinent data and the accuracy of the developed simulation model.

The proposed approach provides Six Sigma practitioners and performance mangers with a mechanism to assess and improve the performance of production and service system based on multiple KPIs when conducting Six Sigma studies.

This paper contributes to the previous research by handling multiple KPIs in Six Sigma studies conducted at the system level using simulation and VF. The research also provides guidelines for using the different methods of weights assessment to form the VF within the DMAIC process.

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.

The case adopted Six Sigma Define-Measure-Analyze-Improve-Control (DMAIC) methodology to achieve the goal of reduction in COPQ.

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.

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.

Six Sigma program is an approach currently adopted by many companies, which involves a highly disciplined and guided process, in which applications of many different tools and techniques aim at generating a cycle of continuous improvement. In this sense, a survey‐based research was conducted in a developing country from which the aim of this paper is to identify and analyse the tools and techniques used in the stages of the DMAIC.

The paper presents parts of the results of a descriptive survey conducted by a postal questionnaire answered by more than 60 Six Sigma users. Specific results of tools and techniques applied in Six Sigma are compared with those prescribed in the literature.

The results confirm the use of Six Sigma tools and techniques suggested by the literature. It also points out that Six Sigma needs to be supported by measurable and reliable data, and thus that the use of tools and techniques is indispensable to the use of the DMAIC.

This is not a probabilistic survey and, therefore, the generalisation is not feasible.

The paper may assist either academic and practitioners when teaching, researching and applying tools and techniques in Six Sigma.

This paper is one of the few published studies that report tools and techniques applied in Six Sigma programme in developing countries.

This study aims to concentrate on quality improvement in plain yogurt production process at company A through adjusting the factors affecting the acidity of the yogurt and determining the optimal level of these factors.

Six Sigma-based framework using define-measure-analyze-improve-control (DMAIC) methodology is adopted through the application of design of experiments tool to focus on customer’s requirements to improve the quality characteristic of plain yogurt production process in dairy products manufacturing company (company A) in Iran.

The results showed that incubation time and fat percentage were significant factors on pH values of yogurt and the optimum settings for these factors were defined as 12 h for the incubation time and 1.5 per cent for the fat percentage.

This study focused solely on the plain yogurt production process in dairy products manufacturing company.

Simplicity of Six Sigma plays a leading role for enabling any dairy manufacturer to determine the problem and minimize its cause through a systematic approach.

Six Sigma has been considered to be a systematic, powerful technique to continuously improve the processes and develop the new products by using effective analytical and statistical tools and methods. This paper presents a Six Sigma-based framework using DMAIC methodology to improve the quality characteristic of plain yogurt production process in dairy products manufacturing company.

This study contributes to show a potential area in which Six Sigma DMAIC approach can promote to improve the quality of yogurt production process. This case can prompt managers of the company to apply Six Sigma method to address complicated problems in other processes, where causes particularly are not clear.

The aim of this paper is to explore the possibility of using the Define-Measure-Analyze-Improve-Control (DMAIC) cycle, process mining (PM) and multi-criteria decision methods in an integrated way so that these three elements combined result in a methodology called the Agile DMAIC cycle, which brings more agility and reliability in the execution of the Six Sigma process.

The approach taken by the authors in this study was to analyze the studies arising from this union of concepts and to focus on using PM tools where appropriate to accelerate the DMAIC cycle by improving the first two steps, and to test using the AHP as a decision-making process, to bring more excellent reliability in the definition of indicators.

It was indicated that there was a gain with acquiring indicators and process maps generated by PM. And through the AHP, there was a greater accuracy in determining the importance of the indicators.

Through the results and findings of this study, more organizations can understand the potential of integrating Six Sigma and PM. It was just developed for the first two steps of the DMAIC cycle, and it is also a replicable method for any Six Sigma project where data acquisition through mining is possible.

The authors develop a fully applicable and understandable methodology which can be replicated in other settings and expanded in future research.

This research aims to enhance the operational excellence and continuous improvement of the retail supply chain in the Saudi Thobe Factory through an integrated approach of Six Sigma DMAIC (Define-Measure-Analyze-Improve-Control) with artificial intelligence (AI).

The study identified the tailoring department as the department with maximum defects by using voice of customer and critical to quality tools. An AI-integrated Six Sigma approach was applied to identify and eliminate nonproductive stages, and a new facility layout was designed to enhance productivity and customer satisfaction.

The use of the factor rating method and simulation using Arena software led to an improved sigma level from 1.597 to 2.237, representing an increment of about 40%. Additionally, the defects per million opportunities reduced from 461,538 to 230,769. The study can help production industry management to optimize facility layouts and improve overall production line efficiency.

This study addresses the lack of published research on the use of an integrated approach of Six Sigma DMAIC with AI in the retail and distribution sector of Saudi Arabia, particularly for small and medium-sized enterprises (SMEs). The study demonstrates how this approach may significantly boost SMEs’ performance and provides a basis for future research in this area.

This study provides a practical example of how an integrated approach of Six Sigma DMAIC with AI can be used in the retail and distribution sector of Saudi Arabia to enhance operational excellence and continuous improvement. The study highlights the potential benefits of this approach for SMEs in the region and provides a framework for future research.

The purpose of this paper is to achieve cost reduction and quality improvement in SME by implementing define, measure, analyze, improve and control (DMAIC) stages of Six Sigma.

The application of DMAIC stages of Six Sigma was carried out in an SME. The cause of the wear out of the guide wheel was discerned in the define stage. In the measure stage, details from the field gathered and sigma level was determined. Using the paired comparison test and FMEA, various causes of the wear out of wheel was analyzed. The alternate material as a solution obtained was compared with present material in the improvement stage. The alternate material was implemented and ensured with proper documents, process change sheet and adoption.

With the application of easy to use tools a cost reduction of INR10,98,096 per annum and the quality improved from 2.9 to 4.4 sigma.

Easy tools were attempted in DMAIC model vs multiple and complex tools usage in large-sized organizations, which was not that much wide spread in SMEs. The study was carried out by changing the raw material of the outer layer of the guide wheel, by applying DMAIC model. A better grade rubber was used for solving the higher wear of the outer layer of the guide wheel. Significant cost reduction and quality improvement can be obtained in SMES by DMAIC model which leads to better brand image.

This study aims to emphasize utilization of Predictive Six Sigma to achieve process improvements based on machine learning (ML) techniques embedded in define, measure, analyze, improve, control (DMAIC). With this aim, this study presents selection and utilization of ML techniques, including multiple linear regression (MLR), artificial neural network (ANN), random forests (RF), gradient boosting machines (GBM) and k-nearest neighbors (k-NN) in the analyze and improve phases of Six Sigma DMAIC.

A data set containing 320 observations with nine input and one output variables is used. To achieve the objective which was to decrease the number of fabric defects, five ML techniques were compared in terms of prediction performance and best tools were selected. Next, most important causes of defects were determined via these tools. Finally, parameter optimization was conducted for minimum number of defects.

Among five ML tools, ANN, GBM and RF are found to be the best predictors. Out of nine potential causes, “machine speed” and “fabric width” are determined as the most important variables by using these tools. Then, optimum values for “machine speed” and “fabric width” for fabric defect minimization are determined both via regression response optimizer and ANN surface optimization. Ultimately, average defect number was decreased from 13/roll to 3/roll, which is a considerable decrease attained through utilization of ML techniques in Six Sigma.

Addressing an important gap in Six Sigma literature, in this study, certain ML techniques (i.e. MLR, ANN, RF, GBM and k-NN) are compared and the ones possessing best performances are used in the analyze and improve phases of Six Sigma DMAIC.