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Quality Improvement Techniques Quality Improvement Techniques Vikas Kumar1*, Sarita Choudhary2 1,2Government Polytechnic College, Lecturer Mechanical Engineering, Sikar Rajasthan, India Abstract In the current scenario of highly competitive international markets, quality is the key to survival. Quality is improved and productivity is increased by reducing the defective-rate of products, and by using the Man, Machine and Material in the best way possible. Quality is improved when the defect rate in the process is reduced. So for this purpose, several approaches are used, such as Analysis of Variance (ANOVA), Shainin DOE technique, Statistical Process Control, Trial and Error method and Taguchi Method. Keywords: Quality, ANOVA, Six Sigma, Shainin, Design of Experiment(DOE), Taguchi, SPC 1. Introduction Manufacturing companies are under increasingly diverse and mounting pressures due to more sophisticated markets, changing customer choice and global competition (Dangayach&Deshmukh, 2003). In this competitive scenario, both leading manufacturers and service providers have come to see quality as a strategic weapon in their competitive battles. As a result, they have committed substantial resources to developing metrices like as defect rates, response time, delivery commitments, and evaluation of products, services and operations performance. Quality is cited as the single most important factor in determining market share. The quality measures represent the most positive step taken to date towards broadening the basis of business performance measurement (Dharf et al., 2005). The improvement in the process and reduction in the variations is achieved by a fundamental understanding of quality improvement is essential to compete effectively in today‟s international markets (Kolarik, et al., 1995). Quality is improved when the defect rate in the process is reduced. So for this purpose, several approaches are used, such as Analysis of Variance (ANOVA), Shainin DOE technique, Statistical Process Control, Trial and Error 13 | P a g e method, Taguchi Method, and Fisher‟s Criterion. Taguchi method is one of the best methods that can be used for quality improvement. 1.2 Quality There are many definitions of quality, however, the widely accepted definitions are „fitness for use‟, „conformance to requirements‟, and „the totality of characteristics of an entity that bear on its ability to satisfy stated and implied need‟.Although there are many ways to define quality, there is a worldwide acceptable Definition stated in ANSI / ASQ Standard A-3 1987, where: “Quality is the totality of features and characteristics of a product or service that bear on its ability to satisfy implied or stated needs” 1.3 Tools for quality improvement Continuous quality improvement process assumes, and even demands that team of experts in field as well as company leadership actively uses quality tools in their improvement activities and decision making process. All the phases of the production process, demand use of quality improvement tools, right from the first phase of product development up to the last phase of product marketing and customer support. Presently, the quality experts and managers find it difficult to choose the appropriate tool, as a large number of quality management and assurance tools are at their disposal. In the conducted research, there is possibility of successful application of 7QC tools in several companies including power generation industry, health services, tourism industry and government. (Paliska et al., 2007). The seven analysed quality tools generally used for any Quality Improvement initiative are: Flow chart Cause-and-Effect diagram Check sheet Pareto diagram Histogram Scatter plot Control charts 2 Quality improvement techniques Statistical techniques such as Design of Experiments, Taguchi methods and Shainin DOE techniques play a vital role in improvement of the product performance. These techniques are 14 | P a g e finding greater prominence in the industry through the development and implementation of Six Sigma strategy (Antony &Jiju, 2008). Yet, the effective implementation of Taguchi experimental design technique within industry can be considered to be poor at present. Companies cite the complexity of the technique as being the major limiting factor as to its use (Thomas and Antony, 2005). The DOE technique developed by Dorian Shainin provides a simple yet powerful approach that can be easily implemented in an industrial environment. It provides a new perspective on the application of DOE techniques so as to simulate greater use and development of these statistical methods in the industry. The four quality improvement techniques are: Statistical Process Control (SPC) Six Sigma Shainin System and Taguchi Method 2.1 Statistical Process Control (SPC) Statistical process control (SPC) charts can be used for monitoring the performance of a feedback (closed-loop) control system. The process drifts off the target due to noise (disturbance) if no compensatory adjustments are made to the process. Disturbance is the output of a linear filter when subjected to white noise (random shocks).Seven Quality Tools are available to help organizations to better understand and improvetheir processes. These tools are described below and most are available in Statistics.The essential tools for the discovery process are: Check Sheet Cause-and-Effect Diagram Flow Chart Pareto Chart Scatter Diagram Histogram Control Charts 15 | P a g e 2.2 Six Sigma Six sigma seeks to improve the quality of process outputs by identifying and removing the causes of defects (errors) and minimizing variability in manufacturing and business processes. From the statistical point of view, the term six sigma is defined as having less than 3.4 defects per million opportunities or a success rate of 99.9997%, where sigma is a term defined as a variation about the process average ( Kwak&Ambari, 2006). Six sigma is a systematic, data-driven approach that applies the DMAIC process and the design for six sigma methods (DFSS). Six Sigma makes use of the DMAICmethodology for improvement of process. DMAIC is the abbreviation for Define, Measure, Analyse, Improve and Control. A specific goal is realized by each phase of the DMAIC method. The fundamental principle of six sigma method is the rigorous application of statistical tools and techniques to enhance the sigma capability of an organisation.‟ 2.3 Shainin system Shainin is devoted to helping product development and manufacturing companies improve their performance through technical problem solving and problem prevention.Shainin put several techniques both known and newly invented, in a coherent stepwise strategy for problem solving in a manufacturing environment. This strategy is called the Shainin Approach or statistical engineering (Shainin, 1993).Shainin identified and categorised the major factor contributing to the variance as Pink X, Red X and Pale Pink X . Red X being the major factor causing variance, Pink X the second factor and Pale Pink X being the third. Shainin DOE approaches are effective in reducing the variation and improving the process. Shainin Techniques work on the principle of elimination, the approach is data based; no conclusions are based on judgments and opinions of various people. Shainin DOE tools have no complex statistics and mathematics. They are based on “Engineering” and “Common sense”. Tools pinpoint the root cause through data and not through atmospheric analysis of the people (Verma et. al., 2004). Problem solving process follows the following phases. Phase –1 – Definition Phase – 2 – Measure and Analyze Phase – 3 - Improve 16 | P a g e Phase – 4 - Control 2.4 Taguchi Technique The Taguchi method is a powerful design of experiments (DOE) tool developed by G. Taguchi. It provides a clear, effective and methodical approach to optimize the cost, quality and performance features of designs. The Taguchi method is an advantage when the process parameters are distinct and qualitative. Taguchi proposed that the engineering optimization of a process should be carried out in a three-step approach: System design, Parameter design, and Tolerance design. In the system design, a primary functional prototype design is produced by applying scientific and engineering knowledge. This primary prototype design consists of design stages of the product and of the process. Hence follows the parameter design. Optimizing the settings of the process parameter values is the purpose of the parameter design. Lastly, the optimal settings recommended by the parameter design are analyzed in the tolerance design. The parameter design is the pivotal step in the Taguchi method for achieving high quality without affecting the cost. A unique design, called orthogonal arrays, is used in the Taguchi method. In the orthogonal arrays design, only a few experiments are required to study the entire parameter space. ANOVA, a statistical analysis of variance, is performed for identifying the statistically important process parameters. Based on the above analysis, a prediction about the optimal combination of the process parameters can be made (George et al., 2004). Casab (2003) demonstrates that, the Taguchi method is capable of establishing an optimal design configuration, even when significant interactions exist between and among the control variables. The Taguchi method can also be applied to designing factorial experiments and analyzing their outcomes. Factorial experiment is an experiment whose design consists of two or more factors, each
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