week-8-tqm concept and tools

8/13/2019 Week-8-TQM Concept and Tools

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Where are You on this scale ?

1

2

3

4

56

7

Whats TQM

Ive heard about TQM

Ive read about TQM

Ive attended some TQM training

Ive used some TQM tools

I have lots of experience with TQM

I taught Dr. Deming everything he knows about TQM


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ndustrial Paradigm

1913 1960 1980 2000Mass Lean Flexible Reconfigurable

Production :

Objective :

Interchangeable Parts

Production Management

Computerization

Knowledge Science

Approach:


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ompetition Strategy

Cost Quality Delivery Flexibility/Responsiveness Innovation

1800 1960 1970 1980 1990 2000


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evolusi

1900 1918 1937 1960 1980

operatorforemen

inspection

Quality Assurance

TQM


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QUALITY

Quality Control Quality Assurance

Total Quality Control

Total Quality Management


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Quality of design

Quality of conformance

1. Performance

2. Feature

3. Reliability

4. Conformance

5. Durability

6. Serviceability

7. Aesthetic

8. Perceived quality


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Quality Assurance

Quality

Control

PDCA

Quality Circle


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Top Management Commitment

Customer Focus

Performance measurement

Participative Management

Continuous Improvement


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BASIC QUESTIONS IN THE TQM IMPLEMENTATION

Who are my customer ? What are the products/services I provide to my customers ?

What are their expectations of my product/service ?

Does my product/service consistently meet or exceed

their expectations ?

What tells me my product/service is improving ?

How do my work activities add value to the process ?

What actions are needed to improve my process ?


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C o s

t p e r g o o

d u n

i t o

f p r o

d u c

t

0 100%Quality level (q)Optimum

quality level

TotalqualitycostsInternal

and externalfailurecosts

Minimumtotal cost

Preventionand appraisalcosts

Quality Cost: Traditional View


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1. CHECK SHEET

2. HISTOGRAM

3. PARETO DIAGRAM

4. CAUSE and EFFECT DIAGRAM

5. SCATTER DIAGRAM

6. CLUSTERING

7. CONTROL CHART

8. QUALITY FUNCTION DEPLOYMENT(QFD)

EIGHT TQM TOOLS


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CHECK SHEET

Product : Plant :Usage : Dept. :Specification : Inspector :Inspectionnumber :

Lot No. :

Lot Size :

Supplier :Measurementunit :

Weight (g) Tally Frequency

Total

Date :


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Interval Mid Value Tallies8.25 9.75 9 II 2

9.75 11.25 10.5 IIII IIII 10

11.25 12.75 12 17

12.75 14.25 13.5 11

14.25 15.75 15 5

15.75 17.25 16,5 2

17.25 18.75 18 2

18.75 20.25 19.5 1

50

8.25 20.25

5

11

22 1

17

10

2

X

f X

= 12.78 , SD = 2.31

Histogram


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Pareto Diagram

Defective Item Number of Defectives Per cent Defective Per cent of Compodition

Head defective (Hd) 99 4.6 % 47.4 %

Material defectives (Md) 13 0.6 % 6.2 %

Bolt defectives (Bd) 52 2.4 % 24.9 %

Corner defectives (Cd) 9 0.4 % 4.3 %

Length defectives (Ld) 36 1.7 % 17.2 %

209 9.7 % 99.9 %

Date : Number of Inspection, N = 2160

Hd Bd Ld Md Cd

200 100 (%)

75

50

25

00

100


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X

X

X

X X

X

X

Reaction Temperature

Scatter Diagram


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Fishbone ChartAirline Customer Service


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Quality Control Approaches

Statistical process control (SPC) Monitors the production process to prevent poor quality

Acceptance sampling Inspects a random sample of the product to determine if a lot is acceptable


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Statistical Process Control

Take periodic samples from a process

Plot the sample points on a control chartDetermine if the process is within limits

Correct the process before defects occur


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Types Of Data

Attribute data Product characteristic evaluated with a discrete

choice Good/bad, yes/no

Variable data Product characteristic that can be measured

Length, size, weight, height, time, velocity


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SPC Applied To Services

Nature of defect is different in services

Service defect is a failure to meet customerrequirements

Monitor times, customer satisfaction


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Service Quality ExamplesHospitals

timeliness, responsiveness, accuracy

Grocery Stores Check-out time, stocking, cleanliness

Airlines luggage handling, waiting times, courtesy

Fast food restaurants waiting times, food quality, cleanliness


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Process Control Chart

1 2 3 4 5 6 7 8 9 10

Sample number

Uppercontrollimit

Processaverage

Lowercontrollimit


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Patterns to Look for in Control Charts


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Constructing a Control Chart

Decide what to measure or countCollect the sample data

Plot the samples on a control chartCalculate and plot the control limits on the controlchart

Determine if the data is in-control If non-random variation is present, discard the data(fix the problem) and recalculate the control limits


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A Process Is In Control If

No sample points are outside control limits

Most points are near the process averageAbout an equal # points are above & belowthe centerline

Points appear randomly distributed


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99.74

%

The Normal Distribution

95 %

m = 0 1 s 2 s 3 s-1 s-2 s-3 s

Area under the curve = 1.0


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Control Chart Z Values

Smaller Z values make more sensitivecharts

Z = 3.00 is standard (99.74% pass);= 2.00 is Medium (95.00% pass);= 1.00 is Tight (66.67% pass)

Compromise between sensitivity anderrors


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Control Charts and the Normal Distribution

Mean

UCL

LCL

+ 3 s

- 3 s

UCL: Upper Control LimitLCL: Lower Control Limit


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Control Charts For Attributes

p Charts Calculate percent defectives in a sample; an item is either good or bad


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p - Charts

Based on the binomial distribution p = number defective / sample size, n

p = total no. of defectivestotal no. of sample observations

UCL p = p + 3 p(1-p)/n

LCL p = p - 3 p(1-p)/n


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p-Chart Calculations

ProportionSample Defect Defective

1 6 .062 0 .003 4 .04. . .. . .20 18 .18

200 1.00

= 0.10=

total defectivestotal sample observations

20020 (100)

p =

100 jeans in each sample

LCL = p - 3 p(1-p) /n

= 0.10 + 3 0.10 (1-0.10) /100

= 0.010

UCL = p + 3 p(1-p) /n

= 0.10 + 3 0.10 (1-0.10) /100

= 0.190


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Control Charts For Variables

Mean chart (X-Bar Chart) Measures central tendency of a sample

Each chart measures the process differently.Both the process average and process variabilitymust be in control for the process to be incontrol.


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Mean (X) - Charts

Based on the normal distribution Calculate the mean (X = average) Calculate the standard deviation (SD) Assign the control level (Suppose standard)

UCL = X + 3 (SD)

LCL = X - 3 (SD)


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Mean (X) - Charts

Based on the normal distribution Volume of antibiotic: 10, 8, 9, 7, 8 ml The average = 42/5 = 8.4 ml Calculate the standard deviation (SD) = Assign the control level (Suppose tight)

UCL = X + 3 (SD) = 8.4 +

LCL = X - 3 (SD) = 8.4 -


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Process Control Chart

1 2 3 4 5

Sample number

UppercontrolLimit (

Process Average(8.4)

LowercontrolLimit (


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Needs

User

Developer

Build toRequirements

Specification

Requirements

Needs

Requirements


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RELATIONS MATRIX

Trade off

Process Characteristics

NEE

DS

PERFORMANCE EVALUATION

RELATIVE VALUE

ABSOLUTE VALUE

CUSTOMEREVALUATION

ANALYSIS


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21,912,616,514,915,2Relative Value

44

4

4 5PT. A

140801059597Absolute Value

55

44

5

PT.B

PT .X 34443

2

2

4,14; 4; 4; 3

3

Price

4,14; 4; 3; 3

4

Trend

5; 1,663; 5; 4; 4Comport

5; 2,52; 5; 4; 5

5

Model

4; 14; 4; 4; 4Color

CustomerRequire-ment

Target &RasioPTX;A;B;CVIVIVII IIII

ConsumerAttribute

: Strong (10): Medium (5): Weak (1)

PT.C

3

4

4

5 2

Keterangan :I Att. WeightII SizingIII CuttingIV SewingV Finishing

VI PackagingVII Inspection

Production ProcessVII

4

4

4

5

120

18,8

Quality Function Deployment ofGarment Factory (PT X)


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Reference

Akao, Y. 2004. Quality Function Deployment:Integrating Customer Requirements IntoProduct. Productivity Press.

Marimin. 2004. Application and Technique of

Multiple Criteria Decision Making. Grasindo.


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