Statistical Process Control

3 sigma, 6 sigma

.26% defective, 3.4 defects for every million produced

Variable Data

Can be measured using a continuous scale, x bar and R charts

For Cp and Cpk if more than 1 then

Capable

Type II Error

Continuing to think everything is fine when a change has occurred (ex. mean may have shifted)

P charts

Designed to track proportion defective in a large sample

For Cp and Cpk if less than 1 then

Incapable

Characteristics of design specifications

Preset by design engineers to define acceptable ranges Based on customer expectations and how the product works (not statistics!)

Statistical Process Control

SPC, used to determine whether a process is performing as expected, goals are predictability and repeatability, SPC is an auditing procedure.

Attribute level data

Simply cataloged by descriptive and discrete characteristics, p charts and c charts

C Charts

Used to track average number of defects per single unit of output

Centered Process (Cp)

Where the mean of the process and the middle of the specification limits are equal

Type I Error

Willingness to think something's wrong when it's actually not (False Alarm)

Control Charts for Variable Data

Mean (x-bar) charts track the average performance (central tendency) over time. Range (R) charts tract the variation of performance (distribution) over time.

Un-centered Process (Cpk)

Mean of the process and the middle of the specification limits are not equal

Process capability

Measure of the ability of a process to meet preset design specifications and determines whether the process can do what we are asking it to do.

Barely Capable

Specification limits and process variability are equal; Capability Index --Cp equal to 1.

Incapable

Specification limits are narrower than process variability; capability index--Cp less than 1.

Highly Capable

Specification limits are wider than process variablity; Capability Index-- Cp greater than 1