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9 Process Capability and Six Sigma Methodology Including Fuzzy and Lean Approaches zlem envar and Hakan Tozan Marmara University, Turkish Naval...
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Please complete following case study and analysis in 600 words

  • a draft of your analysis should include these sections:

  1. Introduction

Identify the key problems and issues in the case study.

  • Formulate and include a thesis statement, summarizing the outcome of your analysis in 1–2 sentences.
  • Background
  • Set the scene: background information, relevant facts, and the most important issues.
  • Demonstrate that you have researched the problems in this case study.
  • Alternatives
  • Outline possible alternatives (not necessarily all of them)
  • Explain why alternatives were rejected
  • Constraints/reasons
  • Why are alternatives not possible at this time?
  • Proposed Solution
  • Provide one specific and realistic solution
  • Explain why this solution was chosen
  • Support this solution with solid evidence
  • Concepts from class (text readings, discussions, lectures)
  • Outside research
  • Personal experience (anecdotes)
  • Recommendations
  • Determine and discuss specific strategies for accomplishing the proposed solution.
  • If applicable, recommend further action to resolve some of the issues
  • What should be done and who should do it?
  • Finalizing the Case

9 Process Capability and Six Sigma Methodology Including Fuzzy and Lean Approaches Özlem Ş envar and Hakan Tozan Marmara University, Turkish Naval Academy Turkey 1. Introduction Process capability analysis (PCA) and Six Sigma methodology occupy important places in quality and process improvement initiatives. As a fundamental technique in any production, quality and process improvement efforts, PCA is used to improve processes, products or services to achieve higher levels of customer satisfaction. In order to measure process capability numerically, process capability indices (PCIs) have been developed. Six Sigma is widely recognized as a systematic methodology that employs statistical and non- statistical tools and techniques for continuous quality and process improvement and for managing operational excellence because it challenges to maximize an organization’s return on investment (ROI) through the elimination of nonconforming units or mistakes in the processes (Antony et al., 2005). The application of Six Sigma methodology provides reduction in variance and augmentation in the process capability, which is defined as the proportion of actual process spread to the allowable process spread that is measured by six process standard deviation units. Similar to Six Sigma methodology, in a process capability study, the number of standard deviations between the process mean and the nearest specification limits is given in sigma units. The sigma quality level of a process can be used to express its capability that means how well it performs with respect to specifications. After Zadeh (1965) introduced the Fuzzy Logic (FL) to the scientific world, this new phenomenon rapidly became an essential systematic used in nearly every field of science. Due to its capability of data processing using partial set membership functions, an enormous literature about FL is developed with full of its applications. In addition, the ability of donating intermediate values between the expressions mathematically turns FL into a strong device for impersonating the ambiguous and uncertain linguistic knowledge (Ross, 2004). But although studies about FL are extremely wide, its application to quality control and especially to PCA is relatively narrow. The aim of this chapter is to carry out a literature review of PCA, fuzzy PCA, PCIs, to make comparisons between PCIs, to introduce ppm and Taguchi Loss Function, to discuss the effects of estimation on PCIs as well as to provide general discussion about sample size determination for estimating PCIs. Another objective of this chapter is to provide the investigation of the relationship between Process Capability and Six Sigma along with the examination of Six Sigma methodology, and a relatively new approach called Lean Six Sigma methodology, and to identify the key factors that influence the success of Six Sigma project implementation for improving overall management process. www.intechopen.com
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Products and Services; from R&D to Final Solutions 154 2. Process capability 2.1 Process Process is defined as a combination of materials, methods, equipments and people engaged in producing a measurable output. As a matter of fact, all processes have inherent statistical variability, which can be identified, evaluated and reduced by statistical methods. The source and amount of variability should always be considered by organizations. In order to satisfy customer requirements, organizations must improve the quality by reducing variance in production processes. The less variation the system has, the better quality it provides. Thereby, the variability of critical-to-quality characteristics (CTQs) is a measure of the uniformity of outputs. When the variation is large, the numbers of products that are nonconforming are large. Nonconforming (NC) is the failure of meeting specification limits whereas specifications are the desired measurements for a quality characteristic. 2.2 Process capability In particular, process capability deals with the uniformity of the process. Variability of CTQs in the process is a measure of the uniformity of outputs. Here, variability can be thought in two ways: one is inherent variability in a CTQ at a specified time, and the other is variability in a CTQ over time. It should be considered that process capability study frequently measures functional parameters or CTQs on the product. It does not measure the process itself (Montgomery, 2009). Process capability compares inherent variability in a process with the specifications that are determined according to the customer requirements. In other words, process capability is the proportion of actual process spread to the allowable process spread, which is measured by six process standard deviation units. Process capability compares the output of a process that is an in-control state to the specification limits by using PCIs. To sum up, a capable process is the one where almost all the measurements fall inside the specification limits and process capability study can be conducted to indicate the extent to which the process can meet these specifications. In a true process capability study, when there is direct observation of the process, inferences can be made about the stability of the process over time by directly controlling or monitoring data collection activity and understanding the time sequence of the data. However, when there is no direct observation of the process, only sample units of product are known, in this case, the study is called product characterization. In a product characterization study, distribution of the product quality characteristic or the fraction that conforms to specifications, which is referred to as process yield, can only be estimated, notably information about stability or dynamic behavior of the process cannot be given (Montgomery, 2009). 2.3 Process Capability Analysis (PCA) PCA involves statistical techniques, which are useful throughout the product cycle. Generally, PCA is used in development activities prior to manufacturing process, in quantification of process variability, in analysis of this variability relative to specifications and in elimination or reduction of the process variability (Montgomery, 2009). As a fundamental technique in any production, quality and process improvement efforts, PCA is used to improve processes, products or services to achieve higher levels of customer satisfaction. PCA has become widely adopted as the measure of performance to evaluate the ability of a process to satisfy customer requirements in terms of specification limits (English www.intechopen.com
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