Spectrum > Statistical Process Control

Statistical Process Control

Spectrum’s Statistical Process Control (SPC) provides the continuous analysis of quality management data to monitor quality and compliance activities, and helps in identifying areas for potential improvement. Manufacturing operations that use statistical analysis to monitor the daily production of their process on the manufacturing line can increase their product yields, improve their product quality, and improve their profitability. With statistical analysis, the monitoring of process events to reveal trends and detect serious problems can become straightforward.

When attempting to implement improvements to work in process, it is necessary to have a way to measure the effectiveness of the process improvement. Spectrum fulfills this function through the use of control charts, also known as 'process-behavior charts', tools used to determine whether a manufacturing or business process is in a state of “statistical control” or not. A process is considered to be in statistical control when it is operating in a stable, repeatable, predictable way. If the chart indicates that the process is currently under control then it can be used with confidence to predict the future performance of the process. If the chart indicates that the process being monitored is not in control, the pattern it reveals can help determine the source of variation to be eliminated to bring the process back into control. The desirability of the state of statistical control is that, whilst in control, a process can be relied upon to produce consistent output. The process is, thus, more easily managed because there is less uncertainty about how it will perform. A process which is out of control cannot be relied upon in this way. It may be subject to sudden changes of performance; it will be difficult to manage and may be prone to the occasional production of output which does not meet the requirements. This is the key to effective process control and improvement.

SPC enables us, firstly, to establish whether a process is in statistical control. If it is not in control, analysis of the collected information will usually indicate what actions are necessary to put it in control. When this has been achieved, SPC is used to ensure that ongoing statistical control is maintained by highlighting the possible occurrence of future variations, identifying how the system might be improved so that common variations may be reduced

There are many different types of control charts embedded within Spectrum’s SPC. Depending on the type of analysis being done, a different type of control chart can be used. Some of the types of charts include:

X Bar – R Charts
The X bar R chart is a type of control chart that can be used with variable data. Like most other variables control charts, it is actually two charts, combining lot or subgroup averages (X bar) and subgroup ranges (R). These charts are a very powerful tool for monitoring variation in a process and detecting changes in either the average or the amount of variation in the process, and is typically used to measure the amount of time it takes to complete something.

The R chart is a measure of the short-term variation in the process. Subgroups should be formed to minimize the amount of variation within a subgroup. This causes the Xbar chart to do the work in detecting process changes. The X-bar/R chart is normally used for numerical data that is captured in subgroups in some logical manner.

p Charts
In industrial statistics, the p-chart is a type of control chart that is very similar to the X-bar chart except that the statistic being plotted is the sample proportion rather than the sample mean. Since the proportion deals with the percentage of successes, clearly the appropriate data for p-charts needs to be attribute data where the outcomes for each trial can be classified as either a success or a failure (conform or non-conform, yes or no, etc.). The subgroup size should ideally be equal, although unequal sample sizes can be accommodated.

X Bar – s Charts
The X bar – s chart is a type of control chart that is used with variables data. Like most other variable control charts, it is actually two charts, combining subgroup averages (X bar) and subgroup standard deviations (s).

s charts
A standard deviation control chart is a data analysis technique for determining if a measurement process has gone out of statistical control. The S chart is sensitive to changes in variation in the measurement process. It measures the standard deviation for each sub-group (x axis) and the sub-group designation (y axis).

PARETO Diagram
A Pareto diagram (named after an Italian sociologist and economist, Vilfredo Pareto) is a special type of bar chart used to determine when faced with multiple problems, which problem to work on first. It is a graphical overview, that shows the order of the most frequently occurring errors or sources of errors, and so can be used to determine how often causes of problems occur. The problem or cause is listed on the x (horizontal axis). The frequency of occurrence of cost associated with each problem or cause is plotted on the y (vertical) axis. The problems or causes on the x axis are listed in decreasing order. The problem or cause that happens most frequently or costs the most is listed first. The problem or cause that happens least frequently or costs the least is listed last. The Pareto diagram allows us to separate the “vital few” from the “trivial many”. This permits us to focus our time and resources where they will be most beneficial.

Control charts represent a picture of how a process varies over time. Histograms, on the other hand, present a picture of how the process “stacks up” over time. They illustrate how many times a certain data value or range of data values occurred in a given time frame. Histograms provide estimates of the location, the spread and the shape of a distribution.

Capability Analysis (Cp, Cpk)
Bringing a process into statistical control is not process improvement. Bringing a process into statistical control is putting the process where it should be. Once the process is in statistical control, real efforts at process improvement can begin. Process Capability is one method of measuring the effectiveness of a process in meeting customer specifications as well as measuring process improvement efforts.

Six Sigma
Six Sigma as an effective way to implement statistical thinking, a philosophy of learning and action based on the following fundamental principles:

  • All work occurs in a system of interconnected processes;
  • Variation exists in all processes;
  • Understanding and reducing variation are the keys to success.

Spectrum supports a Six Sigma approach to quality, which (if used correctly) can deliver superior value to customers, gain competitive edge and cost saving, and can be used to drive ongoing process improvement.

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A unique solution with integrated Statistical Process Control.

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Spectrum has the capability to acquire data directly from laboratory instruments.

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