Defect reduction using Lean Six Sigma and DMAIC

This document summarizes the research paper "Defect reduction using Lean Six Sigma and DMAIC," providing a detailed overview of its methodology, findings, and implications for the die-casting industry. This summary is structured for clarity and readability, suitable for a WordPress page format, and adheres strictly to the content presented in the original paper.

1. Overview

  • Title: Defect reduction using Lean Six Sigma and DMAIC
  • Authors: Condé, G.C.P., Oprime, P.C., Pimenta, M.L., Sordan, J.L., Bueno, C.R.
  • Year: August 2022
  • Journal/Conference: Proceedings of the 5th ICQEM Conference, University of Minho, Portugal, 2022
  • Keywords:
    • Defect reduction
    • Lean Six Sigma
    • DMAIC

2. Research Background

  • Social/Academic Context of the Research Topic:
    • Competitive pressures force companies to seek solutions to eliminate wastes while improving product quality.
    • Lean Six Sigma (LSS) has been considered one of the most effective approaches for business transformation.
    • The auto parts sector has serious efficiency problems caused by high waste rates and unnecessary operations.
    • Despite some applications of LSS and DMAIC in the automotive sector, these methodologies are still poorly applied in auto parts manufacturers according to literature.
  • Limitations of Existing Research:
    • While LSS and DMAIC literature contains many descriptions of practical applications, there is a need for detailed descriptions of the entire process used in structured improvement exercises within the auto parts manufacturing context.
  • Necessity of Research:
    • To present a detailed description of an empirical case study where Lean Six Sigma and DMAIC methodologies are applied to reduce defects in an auto parts manufacturer.
    • To provide a reference for those intending to use similar improvement methodologies for defect reduction in auto parts manufacturing.

3. Research Objectives and Research Questions

  • Research Objective:
    • To present an empirical case study of defect reduction in an auto parts manufacturer using Lean Six Sigma and DMAIC methodologies.
    • To describe in detail the entire process used in a structured improvement exercise.
  • Core Research Questions:
    • How can Lean Six Sigma and DMAIC methodologies be effectively applied to reduce defects in die-casting and machining processes within an auto parts manufacturing company?
    • What are the key variables influencing defect rates in these processes?
    • What solutions can be implemented to sustainably reduce defect incidence and improve sigma levels?
  • Research Hypothesis:
    • The application of DMAIC methodology within a Lean Six Sigma framework can lead to a significant and sustainable reduction in defects in the die-casting and machining processes of auto parts manufacturing.

4. Research Methodology

  • Research Design:
    • Empirical single longitudinal case study.
    • DMAIC methodology (Define, Measure, Analyse, Improve, Control) was followed.
  • Data Collection Methods:
    • Semi-structured interviews conducted in the DMAIC steps sequence.
    • Document analysis.
    • Direct field observations.
  • Analysis Methods:
    • Statistical analysis using Minitab.
    • Design of experiments (factorial experiments).
    • Hypothesis testing.
    • Cause and Effect Matrix.
    • Measurement System Analysis (MSA).
    • Process Capability Analysis.
  • Research Subject and Scope:
    • A manufacturing company in Brazil producing die-casting and machined aluminum auto parts for major vehicle manufacturers.
    • The study focused on defect reduction in the die-casting and machining processes for a specific auto part: Rearview Housing Support (RHS).

5. Key Research Findings

  • Core Research Findings:
    • The analysis pointed out the main defects in die casting and machining phases.
    • Die Casting: "Mold temperature, metal temperature and second stage injection speed influenced the amount of defective die casting parts."
    • Machining: "On the other hand, the incidence of defects in machining process was affected by the fixation method."
    • "Solutions implemented reduced the defect incidence from a chronically high level to an acceptable one."
    • "The sigma level rose from 3.4 σ to 4σ sustainably."
  • Statistical/Qualitative Analysis Results:
    • Define Phase: "Two control charts were elaborated: p-chart for rejection rate of die-casting parts (Figure 1), and p-chart for rejection rate of machining parts (Figure 2)." These charts showed processes with special causes and high defect rates. Baseline sigma level was calculated as "3,4 sigmas".
    • Measure Phase: "Project Team prepared a SIPOC (Suppliers, Input, Process, Output, Customers) matrix (Figure 3)." Detailed variable analysis identified 30 possible variables impacting defect fraction (Table 3). "Cause and Effect Matrix" prioritized seven key variables (Table 4 and 5). "Measurement System Analysis (MSA)" confirmed acceptable measurement system ("all Kappa are above 0,7" in Table 7).
    • Analyse Phase: "Ishikawa Diagrams" (Figure 7 and Figure 8) were used for root cause analysis. "A series of factorials experiments" (Figure 9, 10, 13, 14) identified significant variables:
      • Die-casting: Mold Temperature (x1), Metal Temperature (x2), and 2nd phase injection speed (x3). Optimal combination found in Figure 12: "Mold Temperature in the level = 220° C, Metal Temperature in the level = 700° C, and injection speed (Phase 2) in the level 3 meters per second".
      • Machining: Fixation method (x5). Optimal method found in Figure 16: "type II (alternative) fixation method - x5, named “Torres”".
    • Improve Phase: "Decision Matrix" (Table 10) ranked and selected four solutions for implementation: "Thermal oil using, New Machining Fixation Method, Improve maintenance of foundry components, Alternative tool holder type."
    • Control Phase: "Hypothesis test" (Figure 18 and Figure 19) confirmed significant reduction in defect rates for both die-casting and machining processes ("P-Value = 0,000"). "Process Capability Analysis" (Figure 21 and Figure 22) showed improved and sustained process performance, reaching "4σ" sigma level (Figure 20).
  • Data Interpretation:
    • Statistical analysis and experimental results demonstrated the effectiveness of DMAIC methodology in identifying and addressing root causes of defects.
    • Optimized parameters and implemented solutions led to a significant and sustainable reduction in defect rates and improved process sigma level.
  • Figure Name List:
    • Figure 1 - p-chart for rejection rate of die-casting parts.
    • Figure 2 - p-chart for rejection rate of machining parts.
    • Figure 3 - SIPOC / Die-casting and machining housing support manufacturing process.
    • Figure 4 - Part regions.
    • Figure 5 - Final Process Yield calculation – before improvements.
    • Figure 6 - Pareto Chart – Main reasons for rejecting parts.
    • Figure 7 - Root cause analysis for selected variable x1 (mold temperature).
    • Figure 8 - Root cause analysis for selected variable x7 (cutting Tool Type - Thread).
    • Figure 9 - Pareto Chart of the Effects of the first experimental running – Die-casting.
    • Figure 10 - Pareto Chart of the Effects of the second experimental running – Die casting.
    • Figure 11 - Interaction on second experimental running – Die Casting.
    • Figure 12 - Best combination of variables found during second experimental running – Die casting.
    • Figure 13 - Pareto Chart of the Effects of the first experimental running – machining.
    • Figure 14 - Pareto Chart of the Effects of the second experimental running – machining.
    • Figure 15 - Interaction on second experimental running – machining.
    • Figure 16 - Best combination of variables found during second experimental running – die casting.
    • Figure 17 - Project Team meetings.
    • Figure 18 - Hypothesis Test for die-casting improvements.
    • Figure 19 - Hypothesis Test for machining improvements.
    • Figure 20 - Final Process Yield calculation - improved processes.
    • Figure 21 - Periodic process Capability Analysis – Die casting process.
    • Figure 22 - Periodic process Capability Analysis – machining.
Figure 4 - Part regions.
Figure 4 - Part regions.
Figure 5 - Final Process Yield calculation – before improvements.
Figure 5 - Final Process Yield calculation – before improvements.
Figure 6 - Pareto Chart – Main reasons for rejecting parts.
Figure 6 - Pareto Chart – Main reasons for rejecting parts.

6. Conclusion and Discussion

  • Summary of Main Results:
    • The project successfully applied DMAIC methodology to reduce defects in die-casting and machining processes.
    • Key variables influencing defects were identified and optimized.
    • Implemented solutions led to a sustainable increase in sigma level from "3.4 σ to 4σ".
    • Project goals of reducing reject rates to less than "7% in die-casting process" and less than "1% in machining process" were achieved.
  • Academic Significance of the Research:
    • Provides a detailed empirical case study demonstrating the effective application of Lean Six Sigma and DMAIC in auto parts manufacturing.
    • Contributes to the literature by illustrating the step-by-step process of DMAIC implementation, including the use of specific tools and techniques.
  • Practical Implications:
    • This paper can be used for those who intend to use the same type of improvement methodologies.
    • This study can be used as a reference for managers and engineers in the auto parts industry seeking to implement Lean Six Sigma projects for defect reduction.
    • The detailed methodology and identified key variables provide practical guidance for similar improvement initiatives.
    • The successful case study demonstrates the potential of DMAIC to achieve significant and sustainable improvements in manufacturing processes.
  • Limitations of the Research:
    • "The study are limited to a single case study, without intention of generalizing the results to other types of industries."

7. Future Research

  • Directions for Future Research:
    • Further research could explore the generalizability of these findings to other types of industries and manufacturing processes.
    • Investigating the application of LSS and DMAIC in other areas of auto parts manufacturing beyond defect reduction could be beneficial.
  • Areas for Further Exploration:
    • Exploring the long-term sustainability of the achieved improvements and the factors influencing it.
    • Investigating the impact of organizational culture and employee engagement on the success of LSS and DMAIC implementation in auto parts manufacturing.

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9. Copyright

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