Application of Six Sigma Methodology in an Automotive Manufacturing Company: A Case Study

Jou, Yung‐Tsan; Silitonga, Riana Magdalena; Lin, Ming-Chang; Sukwadi, Ronald; Rivaldo, Jovian

doi:10.3390/su142114497

Cited by 5 publications

(4 citation statements)

References 20 publications

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“…Building upon three iterations of the Delphi technique involving expert scholars [ 16 ], the analysis results revealed the primary facets of the four key dimensions influencing the quality of polypropylene extrusion molding. The quality control resolution process is illustrated with a fishbone diagram [ 17 , 18 ] in Figure 2 . From this analysis, we determined the ten most critical success factor elements.…”

Section: Research Methods and Analysis Experimentsmentioning

confidence: 99%

Applying the Taguchi Method to Improve Key Parameters of Extrusion Vacuum-Forming Quality

Chen,

Huang

2024

Polymers

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This research investigates the control of thickness and weight in plastic extrusion vacuum-thermoforming products to identify optimal key parameters for cost reduction and energy savings. The initial step involves identifying crucial influencing factors. In this step, the Delphi technique was employed through a questionnaire administered to a panel of expert scholars to ensure minimal error and maximal reliability in determining key influencing factors. Consensus was sought to establish appropriateness and consistency. Subsequently, the Taguchi method was applied for quality design and planning of the extrusion vacuum-forming process. The experimental design parameters were selected using an L18 (21 × 37) orthogonal array, and the desired quality characteristics were determined. Comparative analysis of quantitative production data from two consecutive experiments was conducted, and based on F-values and contribution analysis, the combination of control factors maximizing the Signal-to-Noise (S/N) ratio was identified. The objective is to seek optimal parameters for improving the quality of the plastic polypropylene (PP cup lid) manufacturing process, reducing process variability, and identifying the most robust production conditions. Through multiple actual production prediction experiments, it was determined that five control factors, “polypropylene new material ratio,” “T-die lips adjustment thickness”, “mirror wheel temperature stability”, “molding vacuum pressure time”, and “forming mold area design”, contribute to the maximization of the S/N ratio, i.e., minimizing variability. Statistical validation confirms a significant improvement in product quality and weight control. Noteworthily, the quality control model and experimental design parameters established in this study are also applicable to other plastic products and bio-based materials, such as PET, HIPS, and biodegradable PLA lids with added calcium carbonate. The results of the experimental production demonstrate its ability to consistently control product weight within the range of 3.4 ± 0.1 g, approaching the specified tolerance limits. This capability results in approximately 2.6% cost savings in product weight, contributing significantly to achieving a company’s KPI goals for environmental conservation, energy efficiency, and operational cost reduction. Therefore, the findings of this study represent a substantial and tangible contribution.

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Section: Research Methods and Analysis Experimentsmentioning

confidence: 99%

Applying the Taguchi Method to Improve Key Parameters of Extrusion Vacuum-Forming Quality

Chen,

Huang

2024

Polymers

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“…e results of the association formed will be used as a priority for making improvements at the improve stage. Furthermore, a fishbone diagram is used to identify the causes of defects that occur based on influencing aspects such as environmental factors, machines, operators, and so on [18].…”

Section: Calculation Of Dpmo Value and Sigma Levelmentioning

confidence: 99%

Enhancing Quality Control of Packaging Product: A Six Sigma and Data Mining Approach

Ramadhani,

Fitriana,

Habyba

et al. 2023

J. Optimasi Sist. Ind.

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This study explores the application of Six Sigma and data mining methodologies to address the high defect rate in the packaging of Wardah Lightening Powder Foundation. Faced with quality control challenges, the research aims to systematically identify the root causes of packaging defects and develop strategic measures to enhance product quality. Employing a comprehensive Six Sigma approach, the study incorporates various analytical tools, including SIPOC diagrams, Critical-to-Quality (CTQ) characteristics, control charts, Pareto diagrams, and Failure Modes and Effects Analysis (FMEA). These tools facilitate a detailed investigation of the packaging process, highlighting significant failure types and inefficiencies. The research methodology involves an extensive data collection and analysis phase, utilizing data mining techniques to delve into historical defect data. This analysis uncovers underlying patterns and correlations that contribute to packaging failures. Based on these findings, the study proposes targeted interventions to mitigate defect levels. These interventions include the implementation of alarm systems and buzzers on production lines to promptly address issues, and the redesign of ink storage labels for clearer communication and error reduction. The outcomes of this study demonstrate a substantial improvement in packaging quality, evidenced by a marked reduction in defect rates. This enhancement not only contributes to operational efficiency but also plays a crucial role in elevating customer satisfaction levels. The research underscores the effectiveness of integrating Six Sigma with data mining in identifying, analyzing, and resolving quality issues in manufacturing processes. It provides valuable insights for organizations in the packaging industry seeking to optimize their quality control mechanisms and achieve higher standards of product excellence.

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“…DMAIC cycle includes a number of different statistical and management tools used in each phase such as Voice of the Customer (VOC) tools, Critical to Quality (CTQ), Quality Function Deployment (QFD), SIPOC (Suppliers-Inputs-Process-Outputs-Customers), Failure Mode and Effect Analysis (FMEA), Root Cause Analysis (RCA), Measurement System Analysis (MSA), and statistical process control (SPC) [65,69,91,99]. Guidance on how to use the tools in certain phases is described, among others, in the standard ISO 13053 [49].…”

Section: Six Sigma and Maintenancementioning

confidence: 99%

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

Antosz,

Jasiulewicz - Kaczmarek,

Machado

et al. 2023

Eksploatacja i Niezawodność – Maintenance and Reliability

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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.

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Application of Six Sigma Methodology in an Automotive Manufacturing Company: A Case Study

Cited by 5 publications

References 20 publications

Applying the Taguchi Method to Improve Key Parameters of Extrusion Vacuum-Forming Quality

Applying the Taguchi Method to Improve Key Parameters of Extrusion Vacuum-Forming Quality

Enhancing Quality Control of Packaging Product: A Six Sigma and Data Mining Approach

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

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