The document outlines a training program on Statistical Process Control (SPC), covering topics such as process control, variation, causes of variation, statistical control tools, and control chart methodologies. It emphasizes the significance of predicting process stability and managing variation through statistical techniques to enhance productivity and reduce inspection costs. Furthermore, it describes various control charts and process capability indices, including CP and CPK, and the importance of monitoring and improving process capability in manufacturing.

1. WELCOME
TRAINING PROGRAMME ON
STATISTICAL PROCESS
CONTROL

2. STATISTICAL PROCESS
CONTROL
CONTENT
1.INTRODUCTION
2.PROCESS CONTROL
3.VARIATION
4.CAUSES
5.STATISITICAL CONTROL

3. STATISTICAL PROCESS
CONTROL
CONTENT
6.TOOLS FOR STATISTICAL CONTROL
7.PROCESS CAPABILITY
8.PROCESS CAPABILITY INDICES
9.CONTROL CHART TYPES
10 CONTROL CHART MEHODOLOGY
EVALUATION

4. Mistake Proofing 100% Inspection Statistical Process Control
In this
method more
advanced and
modern
techniques are
used which
require
substantial
investment
during its
installation and
maintenance.
As it is detection type
of technique it
can’t avoid failure
but rejects
defective products.
Requires more
inspectors, more
inspection times
and in turn more
cost.
For this technique investment
is very less and process is
controlled on each
workstation therefore
defective components is not
forwarded to next
operation. Predictability
reduces frequent
adjustments & in turn
increases productivity,
reduces inspection cost at
station & at final inspection

5. SPC
The Use of Statistical Techniques such as
Control charts to analyze the process or its
output so as to take appropriate action to
achieve and maintain a state of statistical
Control and to Improve the process
Stability.

6. QUALITY OF CONFORMANCE
Vs
CONFORMANCE TO
SPECIFICATION

7. PROCESS CONTROL
THREE TYPES OF PROCESS CONTROL
PREVENTION OF DEFECTS
* MISTAKE PROOFING
DETECTION OF CAUSES AND LEAD TO
CORRECTIVE ACTION
* VISUAL CONTROL, SPC
DETECTION OF DEFECTS
* INSPECTION

8. VARIATION
PRINCIPLES OF SPC
 VARIATION IS INEVITABLE
 VARIATION IS MEASURABLE

9. STATISTICAL PROCESS
CONTROL
STATISTICAL PROCESS CONTROL
 10 % IS STATISTICS
 90 % IS PRODUCT & PROCESS
KNOWLEDGE

10. STATISTICS
DETERMINE WHICH SAMPLE IS GOOD ?
SAMPLE –1
15,15,14,15,16,15,15
SAMPLE –2
14,15,20,15,16,10,15
SAMPLE - 3
19,20,15,14,11,10,16

11. STATISTICS
HENCE WE NEED TO CAPTURE
 AVERAGE
 RANGE
 STANDARD DEVIATION
TO EVALUATE SAMPLES

12. VARIATION & CAUSES
TYPES OF VARIATION
RANDOM VARIATION
NON RANDOM VARIATION
CAUSES OF VARIATION
COMMON OR CHANCE CAUSES
 SPECIAL OR ASSIGNABLE CAUSES

13. CAUSES
COMMON CAUSES SPL./ASSIGNABLE
FEW IN NOS. PLENTY IN NOS.
VARIATION IS LOW VARIATION IS HIGH
PART OF THE
PROCESS
VISITOR TO THE
PROCESS
CONSTANT
VARIATION
FLUCTUATING
VARIATION

14. CAUSES
COMMON CAUSES SPL./ASSIGNABLE
STATISTICS
APPLICABLE
STATISTICS CANNOT
APPLY
MANAGEMENT
CONTROLLABLE
OPERATOR
CONTROLLABLE
e.g Pressure variation,
Environment variation
e.g Wrong setting, wrong
master
REDUCTION LEAD
TO IMPROVEMENT
ELIMINATION LEAD
TO MAINTENANCE

15. TOOLS FOR CONTROL
HISTOGRAM ----------- BELL SHAPE
CONTROL CHART-- NO OUT OF
CONTROL

16. NORMAL DISTRIBUTION
TAKE A SAMPLE OF 100
ASSUME MEAN = 50.00
S. D. = 1.5
48.5 -------------51.5 (1sigma) 68.20%
47.0--------------53.0 (2Sigma) 95.4%
45.5--------------54.5 (3Sigma) 99.73%

17. NORMAL DISTRIBUTION
 WHEN AVERAGE, MEDIAN & MODE
IS SAME
AVERAGE OF ALL VARIABLE DATA
FOLLOWS NORMAL DISTRIBUTION
ELONGATION,UTS,TWIST,OVALITY,
ETC., LIKE ONE SIDED DOES NOT
FOLLOW NORMAL DISTRIBUTION

18. STATISTICAL CONTROL &
PROCESS CAPABILITY
A PROCESS FREE FROM ASSIGNABLE
CAUSES (PREDICTABLE PROCESS)
PROCESS CAPABILITY
IS
A MEASURE OF INHERENT
VARIATION
(MANAGEMENT CONTROLLABLE)

19. PROCESS CAPABILITY
Cp=Potential Process Capability Index
Cp = TOLERANCE
----------------------
TOTAL VARIATION
(6 SIGMA)

20. PROCESS CAPABILITY
INDICES
TV Vs TOL PPM LEVELS
6 SIGMA > TOL
125 > 100
Cp = 0.8
HIGHER REJECTION
6 SIGMA = TOL
100 = 100
Cp = 1.0
2700 PPM
6 SIGMA < TOL
75 < 100
Cp = 1.33
64 PPM
6 SIGMA < TOL
60 < 100
Cp = 1.67
4 PPM

21. PROCESS CAPABILITY INDICES
 Cp DOES NOT SPECIFY WHERE THE PROCESS
IS CENTERED.
 HENCE WE NEED TO HAVE ONE MORE INDEX
TO MEASURE ACTUAL PROCESS CAPABILITY
 Cpk = ACTUAL PROCESS CAPABILITY INDEX
 Cpk = Min ( USL – AVG / 3 SIGMA, AVG – LSL/ 3
SIGMA)
 USE Cp AND Cpk TOGETHER
 Cpk CANNOT EXCEED Cp

22. PROCESS CAPABILITY INDICES
Pp & Ppk Cp &Cpk
PROCESS
PERFORMANCE INDEX
PROCESS CAPABILITY
INDEX
USED DURING INITIAL
PROCESS STUDY
DURING PPAP
ONGOING PROCESS
CAPABILITY STUDY
CAN BE CAPTURED FOR
STABLE AND
CHRONICALLY
UNSTABLE PROCESSES
USED ONLY FOR
STABLE PROCESSES

23. PROCESS CAPABILITY INDICES
Pp & Ppk Cp &Cpk
CAPTURES VARIATION
DUE TO BOTH
COMMON & SPECIAL
CAUSES
CAPTURES VARIATION
DUE TO COMMON
CAUSES ONLY
SIGMA IS CALCULATED
USING n-1 FORMULA
USING ALL INDIVIDUAL
READINGS
SIGMA IS CALCULATED
USING R bar / d2
FORMULA
Ppk > 1.67 Cpk > 1.33

24. CONTROL CHARTS
OBJECTIVES OF CONTROL CHART
TO DETECT SPECIAL/ASSIGNABLE
CAUSES
TO MAINTAIN THE ACHIEVED
PROCESS CAPABILITY
TO IDENTIFY THE OPPORTUNITY
FOR IMPROVEMENT

25. CONTROL CHART STEPS
1. GATHER DATA
2. INITIAL STUDY
3. CALCULATE CONTROL LIMITS
4. ESTABLISH ONGOING CONTROL
CHART
5. MONITOR, REVIEW AND IMPROVE
PROCESS CAPABILITY

26. 1.GATHER DATA
ELIMINATE OBVIOUS DEFICIENCIES
IDENDTIFY THE FACTORS AFFECTING
AVG. & RANGE
UNDERSTAND PROCESS THROUGH
MASTER CAUSE & WHY WHY ANALYSIS
PLAN SAMPLE SIZE, FREQ, CONTROL
CHART,NO. OF SUBGROUPS,ETC.,

27. SELECTION OF CHARTS
X BAR & R CHART
MOST SENSITIVE CHART
MEDIAN & R CHART
Used to compare output of several process e.g.
same parts from two supplier.
X BAR & S CHART
SUBGROUP IS > 9,
Process is stable & in control and the objective is
to reduce variation.

28. SELECTION OF CHARTS
X & MR CHART
WHERE NOT SUITABLE FOR SUBGROUP
SAMPLING
 INSPECTION IS COSTLY/ DESTRUCTIVE
IN NATURE , long time inspection .

29. GATHER DATA
RECORD PROCESS EVENTS WHILE
COLLECTION OF DATA

30. INTERPRETATION FOR
CONTROL
FOCUS ON RANGE CHART
ALL 4 CONDITIONS ARE CHECKED
AND APPROPRIATE ANALYSIS
MADE AND CAUSES IDENTIFIED FOR
ALL 4 OUT OF CONTROL
CONDITIONS

31. INTERPRETATION FOR
CAPABILITY
CALCULATE SIGMA, 6 SIGMA, Cp,Cpk
IF Cpk < 1.33 INITIATE CA TO IMPROVE
THE VALUE
INITIATE CA PLAN FOR PROCESSES NOT
UNDER STATISTICAL CONTROL
IF Cpk >1.33 ESATBLISH ONGOING
CONTROL

32. EFFECTIVENESS OF SPC
MEASURED BY
IMPROVED PROCESS KNOWLEDGE
REVIEW AND REVISION OF UCL/LCL
REDUCTION IN REJECTION ( ACTUAL Vs
ESTIMATED)
IMPROVED PRODUCTIVITY
LESS INSPECTION AND ADJUSTMENT
CUSTOMER SATISFACTION DUE TO LESS
VARIATION

33. ATTRIBUTE CHART
P-CHART
- PROPORTION OF UNIT NONCONFORMING
- SAMPLE SIZE NEED NOT BE EQUAL
np-CHART
- NUMBER OF UNITS NONCONFORMING
- SAMPLE SIZE MUST BE EQUAL

34. ATTRIBUTE CHART
C – CHART
- NUMBER OF NONCONFORMITIES
- SAMPLE SIZE MUST BE EQUAL
U – CHART
- NUMBER OF NONCONFORMITIES PER UNIT
- SAMPLE SIZE NEED NOT BE EQUAL

35. OPERATOR’s ROLE FOR ONE SUBGROUP
PLOTTING
1. CHECK AS PER CONTROL PLAN
2. CALCULATE AVG/RANGE/INDIVIDUALS
3. PLOT THE SAME IN CONTROL CHART
4. CHECK THE PLOTTED POINT IS IN CONTROL ( REFER
4 CONDITIONS IN CONTROL CHART)
5. IF IT IS IN CONTROL, CONTINUE THE PROCESS
6. ELSE, STOP THE PROCESS, TAKE CORRECTIVE
ACTION & DISPOSITION ACTION AS PER REACTION
PLAN
7. RECORD THE PROCESS EVENTS INCLUDING OUT OF
CONTROL CONDITIONS

36. EFFECT OF OVER
ADJUSTMENT
IF A STABLE PROCESS IS ADJUSTED ON THE
BASIS OF EACH MEASUREMENT MADE, THEN
THE ADJUSTMENT BECOMES AN ADDITIONAL
SOURCE OF VARIATION
 OVER ADJUSTMENT WILL INCREASE THE
VARIATION

37. Thank you