UTILITY OF QUALITY CONTROL TOOLS AND STATISTICAL PROCESS CONTROL TO IMPROVE THE PRODUCTIVITY AND QUALITY IN AN INDUSTRY
RALLABANDI SRINIVASU 1 G. SATYANARAYANA REDDY 2
SRIKANTH REDDY RIKKULA 3
1. Professor & Director in St. Mary’s Group of Institutions, Hyderabad, India.
2. Professor & HOD-MBA in CMR College of Information Technology, Hyderabad, India
3. Associate Professor, MCA Dept. St.Mary’s College of Engg. & Technology, Hyderabad ,India.
E-MAIL: firstname.lastname@example.org , email@example.com, firstname.lastname@example.org
Statistical Process Control (SPC) methods have been widely recognized as effective approaches for process monitoring and diagnosis. Statistical process control provides use of the statistical principals and techniques at every stage of the production. Statistical Process Control (SPC) aims to control quality characteristics on the methods, machine, products, equipments both for the company and operators with magnificent seven. Some simple techniques like the “seven basic quality control (QC) tools” provide a very valuable and cost effective way to meet these objectives. However, to make them successful as cost effective and problem solving tools, strong commitment from top management is required. Statistical process control (SPC) is one of the important tools in quality control (QC). In order to survive in a competitive market, improving quality and productivity of product or process is a must for any company.
Keywords: Statistical Process Control (SPC) ; Statistical Quality Control (SQC); Quality Improvement; Quality Tools and Control Charts
To control quality characteristics on the methods, machine, products, equipments both for the company and operators, the Statistical Process Control (SPC) , Statistical Quality Control (SQC), and Quality Improvement methods have been widely recognized as effective approaches for process monitoring and diagnosis.
Statistical Process Control (SPC)
The primary tool of SPC is the Shewhart control chart. The Shewhart control chart quantifies variation as either special cause or common-cause (natural) variation (Fig. 1). The control limits on control charts quantify variation as that inherent to the process (natural variation data inside the control limits), or variation caused by an event or assignable-cause (special cause variation data located outside the control limits). Data outside the control limits are also referred to as “out of control” points. The study documented the change in sawyer operating targets when sawyers are presented with real-time thickness data in the form of control charts.
Young et al. (2000a, 2000b, 2002a, 2002b, 2005) documented that most sawyers have an anecdotal knowledge of historical lumber thickness averages and variation, i.e., thickness measurements are made infrequently for setup at saw change, shift change, production reporting from last shift, or as a reaction to extreme variation. As saws wear from continuously sawing lumber, the sawyer may experience greater saw deflection at a constant carriage speed (i.e., increased within board variation). Sawyers are reluctant to slow carriage speed and tend to over-size lumber thickness given their imperfect knowledge of real-time lumber thickness at the time of sawing. Over sizing lumber is a costly “hedge” and is not competitive as a long-term business strategy.
Figure 1. — Basic form of a control chart.
Statistical process control is used to describe the variability that can be controlled or cannot be controlled. This variability is also called common cause or special cause. Common cause occurs with the nature of the process. It exists in all processes and it is the variability from the system. Special cause is not the part of the process. It exists almost all processes because of some certain reasons. If there is not variability...
References: . Anjard, R.P. 1995. SPC chart selection process. Microelectronic Reliability. 35(11): 1445–1447.
. Bisgaard, S. 1993. Statistical Tools for Manufacturing. Manufacturing Review. 6(3): 192–200.
. D. Raheja, . Assurance Technologies: Principles and Practices ., McGraw Hill, Inc., 1991.
. FARNUM, N.R., Modern Statistical Quality Control and Improvement, Duxbury Press, Belmont, California, p.500, 1994.
. Freeman, J, G. Mintzas. 1999. Simulating c and u Control Schemes. The TQM Magazine. 11(4): 242–247.
. Gronroos, C. (1983) ‘Strategic management and marketing in the service sector’, Report No. 83-104, Marketing Science Institute, Cambridge, MA.
. Hwang, C.L. and Lin, M.J. (1987) Group Decision Making Under Multiple Criteria: Methods and Applications, Berlin: Springer-Verlag.
. Ishikawa, K. 1985. What is Total Quality Control. Prentice Hall. Englewood Cliff, N.J.
. J. Heizer and B. Render, .Operations Management, 6th Ed., Prentice Hall, 2001.
. Klassen, K.J., Russell, R.M. and Chrisman, J.J. (1998) ‘Efficiency and productivity measures for high contact services’, The Service Industries Journal, Vol. 18, pp.1–18.
. Lee, H., Delene, L.M., Bunda, M.A. and Kim, C. (2000) ‘Methods of measuring health-care service quality’, Journal of Business Research, Vol. 48, pp.233–246.
. Lehtinen, U. and Lehtinen, J.R. (1982) Service Quality: A Study of Quality Dimensions, Helsinki, Finland: Service Management Institute.
. Lowry, C.A. and Montgomery, D.C. (1995) ‘A review of multivariate control charts’, IIE Transactions, Vol. 27, pp.800–810.
. MONTGOMERY, D.C., Introduction to Statistical Quality Control, 3rd Ed., J. Wiley, New York, P.677, 1996.
. Palm, A.C., Rodriguez, R.N., Spiring, F.A. and Wheeler, D.J. (1997) ‘Some perspectives and challenges for control chart methods’, Journal of Quality Technology, Vol. 29, pp.122–127.
. Parasuraman, A., Zeithaml, V.A. and Berry, L.L. (1985) ‘A conceptual model of service quality and its implications for future research’, Journal of Marketing, Vol. 49, pp.41–50.
. R. T. Amsden, H. E. Butler, and D. M. Amsden, . SPC Simplified: practical Steps to Quality, Productivity, Inc. , 1998.
. Ross, P.J. (1988) Taguchi Techniques for Quality Engineering, New York, NY: McGraw-Hill.
. Soteriou, A. and Zenios, S.A. (1999) ‘Operations, quality and profitability in the provision of banking services’, Management Science, Vol. 45, pp.1221–1238.
. Soteriou, A.C. and Chase, R.B. (1998) ‘Linking the customer contact model to service quality’, Journal of Operations Management, Vol. 16, pp.495–508.
. Stoumbos, Z.G., Reynolds, M.R., Ryan, T.P. and Woodall, W.H. (2000) ‘The state of statistical process control as we proceed into the 21st century’, Journal of the American Statistical Association, Vol. 95, pp.992–998.
. Wodall, W. H. 1997. Control Charts Based on Attribute Data: Bibliography and Review. Journal of Quality Technology. 29(2): 172–196.
. Woodall, W.H. and Montgomery, D.C. (1999) ‘Research issues and ideas in statistical process control’, Journal of Quality Technology, Vol. 31, pp.376–386.
. Wyckoff, D.D. (1984) ‘New tools for achieving service quality’, Cornell Hotel and Restaurant Administration Quarterly, Vol. 25, pp.78–91.
. Young, T.M. and P.M. Winistorfer. 1999. Statistical process control and the forest products industry. Forest Prod. J. 49(3):10-17.
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