Statistical process control (SPC) is a method of quality control which employs statistical methods to monitor and control a process. This helps to ensure that the process operates efficiently, producing more specification-conforming products with less waste (rework or scrap).
SPC is an industry-standard tool that helps operators control quality during the manufacturing process. It can be applied to any process where the output for the “conforming product” which refers to the product meeting specifications can be measured.
Key tools used in SPC include run charts, control charts, a focus on continuous improvement, and the design of experiments. An example of a process where SPC is applied is on manufacturing lines but has been attempted for research and development and software engineering with limited success.
SPC must be practiced in 2 phases: The first phase is the initial establishment of the process, and the second phase is the regular production use of the process. In the second phase, a decision of the period to be examined must be made, depending upon the change in 5M&E conditions (Man, Machine, Material, Method, Movement, Environment) and wear rate of parts used in the manufacturing process (machine parts, jigs, and fixtures).
An advantage of SPC over other methods of quality control, such as “inspection”, is that it emphasizes early detection and prevention of problems, rather than the correction of problems after they have occurred.
In addition to reducing waste, SPC can lead to a reduction in the time required to produce the product. SPC makes it less likely the finished product will need to be reworked or scrapped.
Operators regularly measure product dimensions in real time, then plot those values on a graph with control limits. Control limits identify how a process normally operates, and they have no relation to fitness-for-use criteria such as engineering specification limits.
If a measurement value falls outside of control limits, it indicates that something “abnormal” has occurred. Plot points that fall outside of control limits communicate to an operator that a significant change has happened in the manufacturing process. These “out of control” events provide information to operators that can help them better understand the manufacturing process and prevent similar events from occurring in the future.
As a result, the control chart helps operators to ensure product consistency at the time of manufacture. Control limits are different from engineering specification limits, which are gates that identify “pass-or-fail” criteria and do not provide critical information about how a manufacturing process is currently running.
Data that fall within control limits indicate that the process is operating in a consistent fashion, and that nothing unusual has occurred. When an out-of-control event is indicated, operators check their machinery, materials, and various settings to determine what changed, so that corrective actions can be taken to prevent defects. Therefore, SPC not only helps generate information about how manufacturing lines are running, but SPC serves also as a tool for preventing quality problems.
SPC is a large subject that can involve some pretty complex statistics. However, only a very basic understanding of statistics is required to understand the core methods of SPC. You need to understand standard deviation, probability distributions, and statistical significance.
The standard deviation provides a measure of the variation or dispersion for a set of values. Suppose you want to measure the variation of a manufacturing process that is producing parts. You could start by measuring 30 parts at the end of the process. Each of the parts has a slightly different measurement value. Looking at these values would give you an idea how much variation there is between the parts, but we want a single number which quantifies that variation.
The simplest way of measuring this dispersion would be to find the largest and the smallest values, and then subtract the smallest from the largest to give the range. The problem with using the range is that it doesn’t consider all of the values; it is best purely on the two extremes. The more parts we checked, the bigger the range we would get, so clearly this is not a reliable measure. There is also no way of determining a probability of conformance based on the range.
The standard deviation is the reliable measure that we need; it allows a probability of conformance to be calculated if certainty assumptions are valid. It’s basically the average distance of all the individual values from the mean for all the values.
Run Charts and Control Charts
A run chart is a simple scatter plot with the sample number on the x-axis and the measured value on the y-axis. It presents a view of how the process changes over time.
Control charts are very similar to run charts but they also include control limits and often other zones. For example, there may be horizontal red lines at +/- 3 standard deviations representing the control limits, and additional horizontal lines marking +/-1 and +/-2 standard deviation. The number of standard deviations is often simply referred to as sigma. A control chart is a very important graphical tool used in SPC. It is used to monitor processes to check that they are “in-control.” The regions between the process mean and the +/-1 sigma may be referred to as Zone C, between 1 and 2 sigma as Zone B, and between 2 and 3 sigma as Zone A. It is important to understand that the control limits do not relate to the product specification or tolerance in any way. They simply show the variation of the process when it is under control, so that its current operation can be compared with that state. Process capability is also important and should have been established during phase 1 of the SPC where the process is setup. The Control chart is used during phase 2 to ensure that the process is stable.
Major Players In SPC
BlackBelt, Deskera ERP, OptiProERP, Priority, Realtrac, uniPoint Quality Management, LillyWorks, QT9 Quality Management, IQMS ERP Software, Sage 100cloud, Genius ERP, Vicinity Manufacturing, COSS ERP, Intellect eQMS, E2 Manufacturing System, QuickBooks Enterprise, Infor VISUAL ERP, Epicor Manufacturing, Odoo, MasterControl Quality Management System (QMS)