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Introduction To SPC

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Information about Introduction To SPC

Published on November 5, 2007

Author: lnmishra

Source: slideshare.net

Description

Statistical Process Control
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Introduction to Statistical Process Control

Statistical Process Control SPC is a family of tools used to monitor, control, and improve processes.  It involves tabulating, depicting, and describing data sets by applying the seven basic tools of quality and a formalized body of techniques.  Understanding and using SPC does not require knowledge of statistics.  Rather one uses applied general math and a reliable software program such as Excel.

SPC is a family of tools used to monitor, control, and improve processes. 

It involves tabulating, depicting, and describing data sets by applying the seven basic tools of quality and a formalized body of techniques. 

Understanding and using SPC does not require knowledge of statistics.  Rather one uses applied general math and a reliable software program such as Excel.

The Seven Basic Tools of Quality include Flowchart/process map Check sheet Cause-effect diagram Pareto chart Histogram Control chart/Run Chart Scatter diagrams Scattergram

Flowchart/process map

Check sheet

Cause-effect diagram

Pareto chart

Histogram

Control chart/Run Chart

Scatter diagrams

Ensuring successful delivery An essential element of producing a high quality product or service is insuring that the characteristics of that product remain constant over time. Product quality is directly dependent on the process capability. Two key process requirements are – Capability and Stability SPC charts are widely used to determine whether a process is capable and stable over time. Process Inputs Outputs

Understanding Control Charts Control charts are graphical representation of product / process performance over time with Control Limits. It may or may not have Specification limits. They are widely used to determine whether a process is capable and stable over time. Control Limit – The actual performance of the process Specification Limits – What the process is required to perform Capable – Process Performance well within Specification Limits Stable – Process does not have special causes and have no trends/cycles

Creating SPC Charts Central Line or Process Average CL UCL LCL x + 3  - 3  TIME y Upper Control Limit Lower Control Limit UCL = Process Average + 3 Standard Deviations LCL = Process Average - 3 Standard Deviations Unacceptable Deviation, beyond control limits

Is this process good enough?

Is this process good enough?

Is this process good enough?

Is this process stable?

Is this process stable?

Is this process stable?

Is this process stable?

Spotting instabilities Data points beyond control limits 5 points in a up or down trend Data points hugging center line Data points hugging upper and control limits

Data points beyond control limits

5 points in a up or down trend

Data points hugging center line

Data points hugging upper and control limits

Summarizing SPC Charts Determining if the long term process average is rising, falling, or remaining the same. Identifying common causes of variation in our processes. Common cause refers to that fact that the processes we use contain sources of variation. We should seek to reduce or limit common causes of variation [i.e. improve the process capability]. Calling attention to data points which falls beyond the statistically determined control limits. Such points generally represent special causes of variation. Sometimes these data points can be attributed to individuals. By changing the behavior of some individuals we can improve results.

Determining if the long term process average is rising, falling, or remaining the same.

Identifying common causes of variation in our processes. Common cause refers to that fact that the processes we use contain sources of variation. We should seek to reduce or limit common causes of variation [i.e. improve the process capability].

Calling attention to data points which falls beyond the statistically determined control limits. Such points generally represent special causes of variation. Sometimes these data points can be attributed to individuals. By changing the behavior of some individuals we can improve results.

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