Inherent or Natural Variation

Inherent or Natural Variation
Due to the cumulative effect of many small unavoidable causes
A process operating with only chance causes of variation present is said to be “in statistical control”

Special or Assignable Variation

  • Due to

a)  improperly adjusted machine
b)  operator error
c)  defective raw material

  • A process operating in the presence of assignable causes of variation is said to be “out-of-control”
Understanding Variation

Understanding Variation

Sources of Variation

  • within unit(positional variation)
  • between units(unit-unit variation)
  • between lots(lot-lot variation)
  • between lines(line-line variation)
  • across time(time-time variation)
  • measurement error(repeatability & reproducibility)

What is SPC?

Statistical

  • Anything that deals with the collection, analysis, interpretation & presentation of numerical data
  • Gaining information for making informed decisions

Process

  • Combination of machines, tools, methods, materials & people employed to attain process specification
  • A similar procedure/event that is happening repetitively

Control

  • To keep something within a desired condition
  • Make something behave the way we want it to behave
SPC

SPC

The use of statistical techniques such as control charts to analyze a process, take appropriate actions to achieve & maintain a stable process, & improve process capability.

SPC Trend Rules

How to Interpret a Control Chart?

control chart

control chart

Remark:It is based on the Normal Distribution.

SPC Trend Rules
Rule#1: A single point beyond either control limit
Uses: Detects very large/sudden shifts
False alarm rate: 0.27%
Example:

SPC Trend Rule#1

SPC Trend Rule#1

Rule# 2: 9 consecutive points on the same side of the centerline
Uses: Detects small shifts or trends
False alarm rate: 0.39%
Example:

SPC Trend Rule#2

SPC Trend Rule#2

Rule# 3: 6 consecutive points steadily increasing or Decreasing
Uses: Detects strong trends
Example:

SPC Trend Rule#3

SPC Trend Rule#3

Rule# 4: 14 (or more) consecutive points are alternating up and down.
Uses: Detects systematic effects, such as alternating machines, operators, suppliers, etc.
Example:

SPC Trend Rule#4

SPC Trend Rule#4

Rule# 5: 2 out of 3 consecutive points at least 2 std dev beyond the centerline, on the same side
Uses: Detects large changes
False alarm rate: 0.30%
Example:

SPC Trend Rule#5

SPC Trend Rule#5

Rule# 6: 4 out of 5 consecutive points on the chart are more than 1 std dev away from the CL
Uses: Detects moderate-sized changes
False alarm rate: 0.53%
Example:

SPC Trend Rule#6

SPC Trend Rule#6

Rule# 7: 15 (or more) consecutive points are within 1 std dev of the CL
Uses: Detects a decrease in process variation
Example:

SPC Trend Rule#7

SPC Trend Rule#7

Rule# 8:  8 (or more) consecutive points are on both sides of the CL, but none are within 1 std dev of it.
Uses: Detects an increase in process variation
Example:

SPC Trend Rule#8

SPC Trend Rule#8

Control Chart Classifications

Classifications of control charts are depending on the type of data:

1.Variables data

  • A characteristic measured on a continuous scale resulting in a numerical value
  • Examples: Void Size, Bond Pull Strength, Coplanarity, Ball Height, etc.

2.Attributes data

  • A characteristic measured by # of conforming & non-conforming to a specification.  Output is classified as pass/fail or accept/reject.
  • E.g. Broken Wire, Lifted Bond, FM, Chipping, Bent Lead,  etc.
  • Can be expressed in terms of fraction, percentage, count or DPM

 

Control Charts For Variables

Control Charts For Variables

Control Charts For Variables

Control Charts For Attributes

Control Charts For Attributes

Control Charts For Attributes

 

What is a Control Chart?

  • A trend chart with control limits
  • Graphical representation of process performance, where data is collected at regular time sequence of production
  • Valuable tool for differentiating between common cause and special cause variation
  • Evaluating whether a process is or is not in a state of statistical control
  • It lets the data ‘talk’ by itself & basis for data-driven decisions

Control Limits
A typical control chart consists of three lines :

control chart

control chart

CL: The average (measure of location) process performance when the process is in-control

UCL & LCL: The range of ‘usual’ process performance when the process is stable.  Lines drawn 3 standard deviations (3 sigma) on each side of the center line.

What is Statistical Process Control?

Statistical Process Control Explained
Statistical Process Control (SPC) is a set of tools designed to help reduce variation by identifying the source of variation, taking appropriate corrective action, and verifying the results.

  • Dr. Walter Shewhart of Bell Labs developed a theory of Statistical Process Control in 1924.
  • During the ’20’s Dr. Shewhart presented his theories in a series of lectures that were published in a book , Economic Control of Quality Manufactured Product (1931).
  • SPC came into wide use during the 1940’s as a result of war production efforts.