Industrial Examples - Process Capability in Total Quality Management
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The document presents an overview of process capability in total quality management, detailing its definition as a statistical measure of process variability and its applications in quality improvement initiatives like Six Sigma. It discusses the methods for assessing process capability, including the importance of ensuring representative sampling and the complications involved in accurate estimates. The document also highlights practical concerns regarding the sensitivity of capability estimates to distribution assumptions and suggests alternative strategies for non-normal distributions.
Industrial Examples - Process Capability in Total Quality Management
- 1. Presented by Dr. R. RAJA, M.E., Ph.D., Assistant Professor, Department of EEE, Muthayammal Engineering College, (Autonomous) Namakkal (Dt), Rasipuram – 637408 Industrial Examples - Process Capability in Total Quality Management MUTHAYAMMAL ENGINEERING COLLEGE (An Autonomous Institution) (Approved by AICTE, New Delhi, Accredited by NAAC, NBA & Affiliated to Anna University), Rasipuram - 637 408, Namakkal Dist., Tamil Nadu.
- 2. Industrial Examples - Process capability What is Process Capability? Process capability is defined as a statistical measure of the inherent process variability of a given characteristic. You can use a process-capability study to assess the ability of a process to meet specifications. During a quality improvement initiative, such as Six Sigma, a capability estimate is typically obtained at the start and end of the study to reflect the level of improvement that occurred. Several capability estimates are in widespread use, including: Potential capability (Cp) and actual capability during production (Cpk) are process capability estimates. Cp and Cpk show how capable a process is of meeting its specification limits, used with continuous data. 11/11/2020 2
- 3. Count.. They are valuable tools for evaluating initial and ongoing capability of parts and processes. "Sigma" is a capability estimate typically used with attribute data (i.e., with defect rates). Capability estimates like these essentially reflect the nonconformance rate of a process by expressing this performance in the form of a single number. Typically this involves calculating some ratio of the specification limits to process spread. 11/11/2020 3
- 4. Count.. Assessing Process Capability Assessing process capability is not easy. Some textbooks teach users to wait until the process reaches equilibrium, take roughly 30 samples and calculate their standard deviation; however, it is difficult to know when the process reaches a state of equilibrium and if the recommended samples are representative of the process. The measurement of process capability is more complicated than that. 11/11/2020 4
- 5. Count.. For example, suppose you have a rotary tablet press that produces 30 tablets, one from each of 30 pockets per rotation. If you’re interested in tablet thickness, you might want to base your estimate of process capability on the standard deviation calculated from 30 consecutive tablets. Better yet, you might assure representation by taking those 30 consecutive tablets repeatedly over eight time periods spaced evenly throughout a production run (Table 1). You would pool the eight individual standard deviations yielding a thickness capability estimate based on (8 X (30 - 1)) = 232 degrees of freedom. 11/11/2020 5
- 6. Count.. For greater assurance yet, you might want to include several production runs with perhaps fewer sampling times per production run. Estimates of the process capability made this way would be representative and independent of process mean changes that might take place from one sampling time to the next. Because the pooled, within-group standard deviation is calculated on observations taken close together in time, there is no opportunity for it to be contaminated by assignable sources of variation. It is as close to pure capability as you’re likely to get. 11/11/2020 6
- 7. Count.. Practical Concerns when Conducting Capability Studies There are both positive and negative aspects to capability estimates. For example, Cp and Cpk estimates are highly sensitive to the assumption that one is sampling from a normal distribution that is, most of the data points are concentrated around the average (mean), forming a bell shaped curve. Furthermore, sampling from a stable system is essential to obtaining meaningful estimates of process performance for future production. Many quality practitioners report solely the numerical values of the capability estimates. Others, however, note that the capability estimates are themselves merely statistics, or point estimates of the true capability of a process. As such, the use of confidence intervals for the true capability values may also be reported. 11/11/2020 7
- 8. Count.. When sampling from stable, but non-normal distributions, other strategies to obtain meaningful capability estimates may be appropriate, including: Transforming the data to be approximately well modeled by a Normal distribution. Using an alternative probability distribution, such as Weibull or lognormal distributions. 11/11/2020 8