Useful for controlling the process

1. SPC
Statistical process Control
Contents:
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1. Introduction
2. SPC Principles
3. Causes of Variations
4. STATISTICAL CONTROL
5. NORMAL DISTRIBUTION
6. PROCESS CAPABILITY & INDICES
7. CONTROL CHART STEPS
8. CONTROL CHART TYPES
9. FLOW CHART SPC PROCEDURE
10.OPERATOR ROLE IN SPC
11.PRECAUTIONS IN SPC
Data Information
SPC
TOOLS

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1. INTRODUCTION:
There are three types of process controls:
TYPE I : PREVENTION OF CAUSES
In this type of control causes are prevented before it start affecting the process.
e.g. Mistake Proofing. To achieve Zero defect these type of controls are required.
TYPE II : DETECTION OF CAUSES & LEAD TO CORRECTIVE ACTION
In this type of control causes are prevented from occurring instead its presence is identified
and Demands actions to eliminate the cause.
e.g. Statistical process control, Visual Control.
TYPE III : DETECTION OF DEFECTS
In this type of control Defects are detected not prevented
e.g. 100% Inspection, Sampling Inspection.
Statistical process control is an economical way of controlling the process
Statistical Process Control: - The use of Statistical techniques such as control charts to analyze
a process or it’s outputs so as to take appropriate actions to achieve and maintain a state of
statistical control and to improve the process capability.

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VARIATIONS ARE OF TWO TYPES:
2. SPC Principles:
- VARIATION IS INEVITABLE
- VARIATION IS PREDICTABLE
- VARIATION IS MEASURABLE
-For Implementation of SPC the 10% of Statistics & 90% of
Process / Product knowledge is required
- RANDOM VARIATION ( Individual readings are not predictable) RM section
- NON RANDOM VARIATION due to operator

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3. Causes of Variations:

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4. STATISTICAL CONTROL:
Any process free from special causes is called process under statistical control.
STABILITY:
Being in statistical control w.r.t time
Tools for Statistical control:

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5.NORMAL DISTRIBUTION:
Sample of 100 nos. Taken
Histogram drawn and bell shape is witnessed and found process is under statistical
control
Assume mean is --- 50
Standard deviation is ----1.5
As per Normal distribution theory,
Between 48.50 to 51.5 ( i.e +/- 1 Sigma )—there should be 68.20% of readings shall
lie
Between 47.00 to 53.0 ( i.e +/- 2 Sigma )—there should be 95.4% of readings shall lie
Between 45.50 to 54.5 ( i.e +/- 3 Sigma )—there should be 99.73% of readings shall
lie
i.e From this we can understand approximately 100% of the readings are represented
by +/- 3 sigma. Hence this is utilized for calculating Total variation of Process.
PROCESS CAPABILITY
Total variation ( 6 sigma) of process when process is under statistical control is called
process capability
PROCESS CAPABILITY INDICES
It is not possible for the customer to specify the total variation acceptable to him for
each parameter; hence there was a need for developing indices for relating tolerances
and the total variation

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PROCESS CAPABILITY
Total variation ( 6 sigma) of process when process is under statistical control
is called process capability
PROCESS CAPABILITY INDICES
It is not possible for the customer to specify the total variation acceptable to
him for each parameter; hence there was a need for developing indices for
relating tolerances and the total variation
6. PROCESS CAPABILITY & INDICES:

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7. CONTROL CHART STEPS:
1. Gather data & initial study:
1.1 Identify the parameter as per contractual agreement with customer
1.2 Group size 4 to 5 consecutive pieces
1.3 Frequency has to be fixed, continuous, 1/2hr,1hr….depends on the
stability of the process ( To identify special causes)
2. Calculate control limits:
2.1 UCL(Upper control limit) and LCL(Lower control limit) to be calculated

9. 3. Interpret for process control :
3.1 If not in control then identify special causes & eliminate.
3.2 Recalculate UCL & LCL
4. Interpret for capability (if in control):
4.1 Calculate standard deviation (sigma) = R bar / d 2.
4.2 Calculate 6 sigma, Cp & Cpk, If Cpk >1.33 then proceed for step 5
4.3 Else initiate corrective action plan to improve Cpk and carryout 100%
inspection.
7. CONTROL CHART STEPS:

10. 5.Ongoing control:
5.1 Provide UCL & LCL calculated for Ongoing control, specify the date of
calculation of UCL / LCL on the chart to be used for ongoing control
5.2 Prepare reaction plan & link it into control plan
5.3 Train the operator on his / her role
5.4 Review periodically for improvement calculate Cp / Cpk & reviewUCL / LCL
if improvement is observed
- Repeat steps 5.1-5.4
7. CONTROL CHART STEPS:

11. 8.1 For Variable data
8.1.1 If N(Samples)<10 then x̄-R chart is the flagship of the control
charts. It is called variable chart because the data to be plotted
result from measurement on a variable or continuous scale
8.1.2 If N(Samples)>10 then x̄-S chart is another variable control
chart. In this chart standard deviation s is used to indicate
dispersion instead of the range.
8.1.3 The ImR ( Individual and moving range)chart is used when
data are expensive or occur at a rate too slow to form rational
subgroups contd…
8. CONTROL CHART TYPES:

12. 8.2 For Attribute data:
8.2.1 If sample size varies then p chart is used
8.2.2 If sample is fixed then np chart is used here full batch is taken into
consideration
8.2.3 If sample is fixed but a part of data is used then c chart is used.
Example: From loan applications approval everyday a fixed no.is cross
checked.
8. CONTROL CHART TYPES:

13. 9. FLOW CHART SPC PROCEDURE:

14. 10. OPERATOR ROLE IN SPC :

15. 11. PRECAUTIONS IN SPC :
NOTE :
1.After Corrective action taken, the Immediate subgroup shall be measured
and plotted.
2.Record in detail the causes, corrective action and disposition action taken for
every out of control condition.
Concept of over adjustment:
- Over adjustment is the practice of treating each deviation from the target as
if it were the result of the action of a special cause of variation of the process.
- If a stable process is adjusted based on the each measurement made, then
the adjustment becomes an additional source of variation.
- Adjust the process only based on the indications of control chart and as
directed by corrective action plan

16. Engineering Engineers