Six sigma project prioritization and selection methods: a systematic literature review

Pakdil, Fatma

doi:10.1108/ijlss-01-2021-0001

Cited by 9 publications

(9 citation statements)

References 63 publications

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“…von Corswant (2005) highlights that, developing products in interaction between professionals may provide benefits in terms of specifications, increased innovation and possibilities to perform more efficient product development tasks. Pakdil (2022) conducted a literature review and claims that the activities of a group of experts can support professionals to accurately identify project prioritization for better decision-making than decision makers’ “intuitions” in this process.…”

Section: Results and Analysismentioning

confidence: 99%

Roadmap for product development based on design for six sigma method

Francisco

Canciglieri

Sant’Anna

2022

IJLSS

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Purpose The Design For Six Sigma (DFSS) methodology is one of the most important to achieving excellence in an organization’s product development process. This paper aims to propose a roadmap for product development based on the DFSS for the consumer durables manufacturing industries. The proposed roadmap presents a systematic approach to the phases of the product development process, integrating the statistical techniques and quality tools that should be used in each phase. Design/methodology/approach This study presents a detailed roadmap for product development, which was built by identifying gaps in the DFSS methods, based on previous studies on the subject. In this step, the opportunities are provided in all phases from creation to discontinuation of the product in the market. In addition, the roadmap presented was validated by team of stakeholders in the product development process of different industrial companies. Findings The proposed roadmap for the product development process based on six sigma design suggests a visual tool with sequential steps and techniques that allow you to follow the evolution of the development process from idea conception until the product is discontinued in the market. Identifying the priorities of organizations, especially the consumer, regarding the quality and reliability of the product. Practical implications The roadmap seeks to facilitate an understanding of the important stages of the product development process and to provide an approach to improving and optimizing the product before the manufacturing process step through the principles of DFSS methodology. This research provides a guide step by step to apply statistical techniques and quality tools in the product development process to achieve high quality and six sigma level in the manufacturing process. Originality/value The proposed roadmap of this research combines design for sigma and product development concepts, covering a wide spectrum of relevant activities that include the product development process, the application of statistical techniques and the design of high-quality durable consumer goods to match manufacturing technologies.

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Section: Results and Analysismentioning

confidence: 99%

Roadmap for product development based on design for six sigma method

Francisco

Canciglieri

Sant’Anna

2022

IJLSS

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“…Then identification of project problems, limitations and assumptions, research objects and project timeline will be carried out. This project charter functions to provide an initial understanding of a project (Pakdil 2021).…”

Section: Project Chartermentioning

confidence: 99%

Quality Improvement of Ceramic Insulator with Six Sigma Method

Pricilia,

Salomon,

Saryatmo

2023

Proceedings of the International Conference on Industrial Engineering and Operations Management

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Quality control of a product within a company is a demand to fulfill and achieve product quality by existing standards. With quality control, a problem or defect in the product produced can be seen. Research was conducted in a company that operates in the electricity sector by producing products in the form of ceramic insulators. The problem in this company is that there are still many defects caused by several factors such as human error, environment, and also the methods used. There are 4 types of defects produced, namely broken neck of the product, the broken body of the product, broken leg of the product, and porous defects. This research purpose is to find out the causes of defects from any defects that occurred, then find out solutions or corrective actions to provide on which priority failures need to be reduced. The research was carried out using the Six Sigma method with the DMAIC stages, followed by an FMEA assessment table. After data processing, the capability process (Cp) value was 0.7467, and the Cpk was 0.6477. Then, we obtained a DPMO value is 13,250, so the Sigma level obtained for the last 6 months was 3.72. Then, using FMEA method, we obtained the biggest RPN score is broken body of the product with score 140. So, the improvement suggestion is to check and replace lamps used at glazing stations.

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“…Perçin and Kahraman (2010) and Bilgen and Şen's (2012) studies used fuzzy multicriteria decision-making approaches for Six Sigma project selection purposes. On the other hand, Pakdil (2021) provided a systematic literature review on Six sigma project prioritization and selection methods. In the fourth category, there are studies that propose improvements to the statistical analysis and data mining methods applied in Six Sigma.…”

Section: Literature Reviewmentioning

confidence: 99%

Predictive Six Sigma for Turkish manufacturers: utilization of machine learning tools in DMAIC

Uluskan

Karşı²

2022

IJLSS

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Purpose 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. Design/methodology/approach 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. Findings 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. Originality/value 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.

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Six sigma project prioritization and selection methods: a systematic literature review

Cited by 9 publications

References 63 publications

Roadmap for product development based on design for six sigma method

Roadmap for product development based on design for six sigma method

Quality Improvement of Ceramic Insulator with Six Sigma Method

Predictive Six Sigma for Turkish manufacturers: utilization of machine learning tools in DMAIC

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