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 (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, 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

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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

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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. is an experiment whose design consists of two or more factors, each with discrete possible values or "levels", and whose experimental units take on all possible combinations of these levels across all such factors. Factorial experiments can be used when there are more than two levels of each factor.

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Chiang & Hsieh (2008) says that, Taguchi uses the orthogonal array to set up the experiment for the advantages of less number and elastic deployment of experiment, and optimizes the process parameters by the analysis of signal-to-noise (S/N) ratio table and graph. The optimal process parameters can improve the robustness of products, so the Taguchi method is also called parameter design. In recent years, the Taguchi method has become a powerful tool for improving productivity during research and development stage so that high quality products can be produced quickly and at low cost The traditional Taguchi method was designed to optimize a single quality characteristic. However, optimization of multiple quality characteristics is much more complicated than optimization of a single quality characteristic. Improving one particular quality characteristic would possibly lead to serious degradation of the other critical quality characteristics. When the results have a conflict between multiple quality characteristics, it is necessary to rely on the subjective experiences of engineers to attain a compromise as a result, uncertainty will be increased during the decision- making process.

Oktem (2007) says, based on orthogonal arrays, the number of experiments which may cause to increase the time and cost can be reduced by using Taguchi technique. It employs a unique design of orthogonal arrays in which only a few experiments are required to study the entire parameter space. Taguchi offers the use of the S/N ratio to identify the quality characteristics applied for engineering design problems. The S/N ratio characteristics can be divided into three steps:

 The smaller, the better;  The larger, the better; and  The nominal, the best.

2.5 Global Comparison Global comparison of the selected strategies is made the basis of on four dimensions.  The type of improvements that are pursued,  The type of data that are used,  The main phases in the strategy, and  The typical user who applies the strategy

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Table 1: Global comparison S. Approach Type of Types of Main Phases Typical User No information Improvement 1 SPC Qualitative Stabilization Planning Multidisciplinary Observational Analyze/ (operators and quantitative improve non Engineers) experimental Control 2 Taguchi Qualitative Optimization Planning Production Experimental Analyze/ Engineers Qualitative improve non experimental Analyze/improve experimental 3 Six Sigma Qualitative Optimization Planning Middle manager Observational Analyze/ and Specialist quantitative improve non Experimental experimental Quantitative Analyze/improve experimental 4 Shainin Observational Stabilization Planning Production quantitative Optimization Analyze/ Engineers Experimental improve non Quantitative experimental Analyze/improve experimental Control

3 Conclusion In order to get good manufacturing products and to survive in this competitive market, quality improvement is necessary which is achieved by these quality improved techniques.

When the existing process is in needs improvement, the six sigma methodology can be successfully used. In case comparison between different process parameters is considered, Taguchi‟s orthogonal arrays are the statistical experimental designs is very much useful.

References Casab, J., Orsolya, D., Anna, L., Eya, A. and Lstyan, N. (2003). “Taguchi Opti"mization of Elisa Procedures.” Journal of Immunological Methods, 223 (2), 37–146.

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Chiang, Y.-M., & Hsieh, H.-H. (2009). The use of the Taguchi method with grey relational analysis to optimize the thin-film sputtering process with multiple quality characteristic in color filter manufacturing. Computers & Industrial Engineering, 56(2), 648-661.

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Dhafr, N., Ahmad, M., Burgess, B., &Canagassababady, S. (2006). Improvement of quality performance in manufacturing organizations by minimization of production defects. Robotics and Computer-Integrated Manufacturing, 22(5-6), 536-542.

George, P. M., Pillai, N., & Shah, N. (2004). Optimization of shot peening parameters using Taguchi technique. Journal of Materials Processing Technology, 153-154, 925-930.

Kwak. Y &Anbari. F (2006). Benefits, obstacles and future of six sigma approach, Technovation, vol.26.pp.708-715

Oktem, H., Erzurumlu, T., &Uzman, I. (2007). Application of Taguchi optimization technique in determining plastic injection molding process parameters for a thin-shell part. Materials & Design, 28(4), 1271-1278.

Paliska, G., Pavletic, D., &Sokovic, M. (2007). Quality tools – systematic use in process industry. Manufacturing Engineering, 25(1), 79-82.

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Verma, A. K., Srividya, A., Mannikar, A. V., Pankhawala, V. A. and Rathanraj, K. J. (2004). “Shainin Method: Edge Over Other Doe Techniques.” IEEE International Engineering Management Conference. pp.1110-1113

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