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.
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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...
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