Volume 8 Number 1 Year - 2014

Number of articles: 10



ANALYSIS OF ECONOMICS OF QUALITY IN MANUFACTURING INDUSTRIES

Authors: A. Sailaja, P.C. Basak, K.G. Viswanadhan

Abstract: In modern industries, much emphasis is given to quality as it is the most effective tool which can capture, retain and enlarge customer base. The customer satisfaction is the ultimate goal of business and no other strategy can achieve it other than attaining best quality. An improvement in quality enhances customer satisfaction, taper s manufacturing costs and of course in turn, increases productivity. But in business scenario, improving quality should be considered along with the expenses associated with it. The strategy should be to achieve high Quality in a most economic way. So identifying effective methods for the analysis of economics behind quality and reduction of costs associated with achieving quality is a serious potential management problem and should be looked in to and analyzed. Economics of Quality analysis which is also termed as cost of quality or quality cost analysis has emerged as a powerful management tool for assessing the present quality level of the organization and to identify the improvement opportunities and also supports in decision making. This paper presents a study on quality cost analysis of two manufacturing units and tries to analyze the interrelationships between quality cost categories using statistical methods. The secondary data collected from the financial records of two firms under manufacturing sector is used for this analysis. The Pearson product momentum correlation coefficient between different quality cost categories provides insight to the relationships between different quality cost elements and in turn helps management to set action priorities to be addressed to achieve good quality at lower cost. The regression analysis helps the management in estimation or prediction of the unknown value of one variable from the known value of the other variable.

Keywords: Cost of Quality, COQ models, Correl ation Analysis, Regression analysis

Article info: pp. 121-138

Recieved: 14.02.2014  Accepted: 20.03.2014  UDC: 65.012.7   Downloads: 652


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