Procon Engineering Private Limited

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


Histogram of Diameter of Rivet (mm)

35343332313029

7

6

5

4

3

2

1

0

Torque (nm)

Frequency


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

7

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






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






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






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


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


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

Procon Engineering Private Limited

Documents

rework log quality

durability process capability

conformancedesign diagram

automobile seats

nissan diesel

sports car seats

series certification

perceived quality100