procon engineering private limited
DESCRIPTION
Statistical Quality ReviewTRANSCRIPT
PROCON ENGINEERING
PRIVATE LIMITED ____________
The Statistical quality review was conducted by Saad Sarfraz
A Statistical Quality Management Review
Company Profile
Master Group of Industries
Establishment
Manufacturing Facility
Standards ISO 9002 Certification QS 9000 Series Certification
Company Profile (contd.)
Automobile seats Sports car seats for export Reclining mechanism Automotive fabric Roof headlining Door trim Steering wheels Rear package tray Sun visors Fender liner Engine under coversheet Armrest Cargo deck Chassis frame
Suzuki Toyota Honda Nissan Hyundai Kia Motors Daihatsu Hino Nissan Diesel Volvo Yamaha Motorcycle Millat Tractor Al-Ghazi Tractor
Products Clients
Statistical Quality Control Systems
Categories Descriptive Statistics Statistical Process Control (SPC) Acceptance Sampling
PROCON SQCS Failure Mode and Effect Analysis (FMEA) Checks Sheets Control Plans Process Flow Charts Process Quality Control Tables Cause and Effect Diagram Pareto Charts Kaizen
Application of Management Strategies
Total Quality Management (TQM) Problem History Charts Scrap and Rework Log Quality vs. Price
HACPL Quarterly Presentations Operational Manuals Diagrams Team Work Encouraged vs. Fear of Management Kaizen Target Monitoring Ergonomics
Eight Dimensions of Quality
Performance Inspection right off assembly Features Seat recliners and comforters Reliability Performance tests (pressure/temperature) Conformance Design diagram during production Durability Process capability Serviceability Rework Redesign Aesthetics Style, color, shape, tactile characteristics Perceived Quality 100% inspection on outgoing products
Descriptive Statistics
Objective
Summarize data to be used for inference Pattern and Distribution
Discrete Binomial Distribution Variable Normal Distribution
Visual Representation
Examples
Stem and Leaf Display
Histogram
631.0630.5630.0629.5629.0
7
6
5
4
3
2
1
0
Length of Frame (mm)
Frequency
Mean 630StDev 0.5N 30
Histogram of Length of Frame (mm)
14.1014.0514.0013.9513.90
10
8
6
4
2
0
Diameter of Rivet (mm)
Frequency
Mean 14StDev 0.05N 30
Histogram of Diameter of Rivet (mm)
35343332313029
7
6
5
4
3
2
1
0
Torque (nm)
Frequency
Mean 32StDev 1.5N 30
Histogram of Torque (nm)
522521520519518
7
6
5
4
3
2
1
0
Height of Seat (mm)
Frequency
Mean 520StDev 1N 30
Histogram of Height of Seat (mm)
Box and Whisker Plot
631.0630.5630.0629.5629.0Length of Frame (mm)
Boxplot of Length of Frame (mm)
14.1514.1014.0514.0013.9513.90Diameter of Rivet (mm)
Boxplot of Diameter of Rivet (mm)
343332313029Torque (nm)
Boxplot of Torque (nm)
522521520519518Height of Seat (mm)
Boxplot of Height of Seat (mm)
Magnificent Seven (Statistical Process Control)
Major Tools Histogram or stem-and-leaf diagram Check sheet-- Pareto chart Cause-and-effect diagram Defect concentration diagram Scatter diagram Control chart
Frequency 28 18 5 5Percent 50.0 32.1 8.9 8.9Cum % 50.0 82.1 91.1 100.0
Category Scratch on PVCGuide GapWrinklesDirty Marks
60
50
40
30
20
10
0
100
80
60
40
20
0
Frequency
Perc
ent
Pareto Chart of 4-W Cushioning
Frequency 2 27 10 8 5Percent 54.0 20.0 16.0 10.0Cum % 54.0 74.0 90.0 100.0
2-W Cushioning Mold OpenScreening ProblemNotching MissCushioning Fault
50
40
30
20
10
0
100
80
60
40
20
0
Frequency
Perc
ent
Pareto Chart of 2-W Cushioning
Frequency 3 32 18 10 9Percent 46.4 26.1 14.5 13.0Cum % 46.4 72.5 87.0 100.0
Cutting & Stiching
70
60
50
40
30
20
10
0
100
80
60
40
20
0
Frequency
Perc
ent
Pareto Chart of Cutting & Stiching
Chance and Assignable Causes of Variation
Definition
Impact/Implication on Quality
Identification Failure Modes and Effects Analysis (FMEA)
Control Charts for Variables
Definition
Purpose and Usefulness
X-Bar Chart and R-Chart
191715131197531
1212
1210
1208
1206
1204
1202
1200
1198
1196
Sample
Sam
ple
Mean
__X=1203.78
UCL=1210.40
LCL=1197.15
X-bar Chart of Distance 1
191715131197531
1212
1210
1208
1206
1204
1202
1200
1198
1196
Sample
Sam
ple
Mean
__X=1203.78
UCL=1210.40
LCL=1197.15
X-bar Chart of Distance 1
191715131197531
25
20
15
10
5
0
Sample
Sam
ple
Range
_R=11.34
UCL=23.99
LCL=0
R Chart of Distance 1
191715131197531
35
30
25
20
15
10
5
0
Sample
Sam
ple
Range
_R=14.73
UCL=31.14
LCL=0
R Chart of Distance 2
Control Chart for Attributes
Definition
Purpose and Usefulness
P- Chart and C- Chart
191715131197531
0.35
0.30
0.25
0.20
0.15
0.10
0.05
0.00
Sample
Pro
port
ion
_P=0.11
UCL=0.3199
LCL=0
P Chart of Defective Seats
191715131197531
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6
5
4
3
2
1
0
Sample
Sam
ple
Count
_C=2.2
UCL=6.650
LCL=0
C Chart of Defective Seats
Process and Measurement System Capability Analysis
Process capability involves evaluating process variability relative to preset product specifications in order to determine whether the process is capable of producing an acceptable product
To produce an acceptable product, the process must be capable and in control before production begins
Process and Measurement System Capability Analysis
The Indices of Process Capability
The processes and measurement systems considered
Evaluation Criteria
Results
7.87.57.26.96.66.36.0
LSL Target USL
LSL 6Target 7USL 8Sample Mean 7.01977Sample N 30StDev(Within) 0.117908StDev(Overall) 0.118755
Process Data
Cp 2.83CPL 2.88CPU 2.77Cpk 2.77
Pp 2.81PPL 2.86PPU 2.75Ppk 2.75Cpm 2.79
Overall Capability
Potential (Within) Capability
PPM < LSL 0.00PPM > USL 0.00PPM Total 0.00
Observed PerformancePPM < LSL 0.00PPM > USL 0.00PPM Total 0.00
Exp. Within PerformancePPM < LSL 0.00PPM > USL 0.00PPM Total 0.00
Exp. Overall Performance
WithinOverall
Process Capability of Nugget Size
14.4014.2514.1013.9513.8013.6513.50
LSL Target USL
LSL 13.5Target 14USL 14.5Sample Mean 13.9837Sample N 30StDev(Within) 0.0703106StDev(Overall) 0.0594953
Process Data
Cp 2.37CPL 2.29CPU 2.45Cpk 2.29
Pp 2.80PPL 2.71PPU 2.89Ppk 2.71Cpm 2.72
Overall Capability
Potential (Within) Capability
PPM < LSL 0.00PPM > USL 0.00PPM Total 0.00
Observed PerformancePPM < LSL 0.00PPM > USL 0.00PPM Total 0.00
Exp. Within PerformancePPM < LSL 0.00PPM > USL 0.00PPM Total 0.00
Exp. Overall Performance
WithinOverall
Process Capability of Riveting Diameter
2.942.802.662.522.382.242.10
LSL Target USL
LSL 2Target 2.5USL 3Sample Mean 2.50067Sample N 30StDev(Within) 0.0501345StDev(Overall) 0.0445555
Process Data
Cp 3.32CPL 3.33CPU 3.32Cpk 3.32
Pp 3.74PPL 3.75PPU 3.74Ppk 3.74Cpm 3.77
Overall Capability
Potential (Within) Capability
PPM < LSL 0.00PPM > USL 0.00PPM Total 0.00
Observed PerformancePPM < LSL 0.00PPM > USL 0.00PPM Total 0.00
Exp. Within PerformancePPM < LSL 0.00PPM > USL 0.00PPM Total 0.00
Exp. Overall Performance
WithinOverall
Process Capability of Riveting Height
328324320316312308304300
LSL Target USL
LSL 300Target 315USL 330Sample Mean 314.967Sample N 30StDev(Within) 2.50673StDev(Overall) 2.208
Process Data
Cp 1.99CPL 1.99CPU 2.00Cpk 1.99
Pp 2.26PPL 2.26PPU 2.27Ppk 2.26Cpm 2.28
Overall Capability
Potential (Within) Capability
PPM < LSL 0.00PPM > USL 0.00PPM Total 0.00
Observed PerformancePPM < LSL 0.00PPM > USL 0.00PPM Total 0.00
Exp. Within PerformancePPM < LSL 0.00PPM > USL 0.00PPM Total 0.00
Exp. Overall Performance
WithinOverall
Process Capability of ILD Front Seat
Acceptance Sampling for Attributes and Variables
Acceptance sampling refers to the process of randomly inspecting a certain number of items from a lot or batch in order to decide whether to accept or reject the entire batch
A sampling plan is required for acceptance sampling that precisely specifies the parameters of the sampling process and the acceptance/rejection criteria
Acceptance Sampling for Attributes and Variables
Sampling plan for attributes Lot size (N) = 100,000 Inspection level = Normal Acceptance quality level (AQL) = 2% Sample size code letter = N Sample size (n) = 500 Acceptance number (c) = 18
Acceptance Sampling for Attributes and Variables
Sampling plan for variables Lot size (N) = 100,000 Inspection level = Normal Acceptance quality level (AQL) = 2% Sample size code letter = O Sample size (n) = 100 Acceptance number (k) = 1.765
0.40.30.20.10.0
1.0
0.8
0.6
0.4
0.2
0.0
Lot Defects Per Unit
Pro
babili
ty o
f A
ccepta
nce
Operating Characteristic (OC) Curve for Attributes
Sample Size = 20, Acceptance Number = 1Produer Risk = 6%, Consumer Risk = 7%
0.40.30.20.10.0
0.04
0.03
0.02
0.01
0.00
Incoming Lot Defects Per Unit
AO
Q (
Defe
cts
Per
Unit
)Average Outgoing Quality (AOQ) Curve for Attributes
Sample Size = 20, Acceptance Number = 1AOQL = 4%
325320315310305
1.0
0.8
0.6
0.4
0.2
0.0
MU
P(A
ccept)
Operating Characteristics (OC) curve for VariablesSample Mean = 315, Standard Error = 2.2
Acceptance Criteria = Mean<=315
ISO Standardization
ISO develops international standards of quality for all industrial standards
ISO has gained popularity and recognition because of the facts that it controls quality, it saves money, customers expect it and competitors use it
ISO 9000 and ISO 14000
ISO Standardization
PROCON strictly follows the ISO standards of quality and its QMS is based entirely on the ISO 9000 family
The compatible standard that applies to PROCON is ISO T/S 16949 quality specification for automotive industry supplier
Application of Six Sigma Approach
Six Sigma Quality is defined as a high level of quality associated with approximately 3.4 defective parts per million
Six Sigma simply means a measure of quality that strives for near perfection
A Six Sigma defect is defined as anything outside of customer specifications
Application of Six Sigma Approach
There are two aspects to implementing the Six Sigma concept: DMAIC and DMADV
The first is the use of technical tools to identify and eliminate causes of quality problems
The second aspect of Six Sigma implementation is people involvement
Critique of the Quality Control System
The objective is to study, evaluate and critically analyze the quality control system of the company
The quality control measures for the most part conform to the standard operating procedure
One drawback of the quality control system of PROCON is its cost
Overall, the quality control system is highly efficient and effective at establishing and maintaining quality