statistic process control
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Statistic Process Control
Week 3
Ananda Sabil Hussein, SE, MCom
Latar Belakang
Pertengahan tahun 80 an pangsa pasar pager Motorola di rebut oleh produk-produk Jepang seperti halnya NEC, TOSHIBA dan Hitachi.
Motorola melakukan perubahan radikal dengan memperbaiki mutu, pengembangan produk dan penurunan biaya yang berbasis statistik
Statistical Process Control
Teknik statistik yang secara luas digunakan untuk memastikan bahwa proses yang sedang berjalan telah memenuhi standar.
StartProduce Good
Provide Service
Stop Process
Yes
NoAssign.
Causes?Take Sample
Inspect Sample
Find Out WhyCreate
Control Chart
Variasi Alami dan Khusus
Variasi alami adalah sumber-sumber variasi dalam proses yang secara statistik berada dalam batas kendali
Variasi Khusus/dapat dihilangkan yaitu variasi yang muncul disebabkan karena peralatan yang tidak sesuai, karyawan yang lelah atau kurang terlatih serta bahan baku baru.
Diagram Pengendalian
Plot of Sample Data Over Time
0
20
40
60
80
1 5 9 13 17 21
Time
Sam
ple
Va
lue
SampleValueUCL
Average
LCL
17 = UCL17 = UCL
15 = LCL15 = LCL
16 = Mean16 = Mean
Sample numberSample number
|| || || || || || || || || || || ||11 22 33 44 55 66 77 88 99 1010 1111 1212
Konsep Rata-rata dan Jarak
Rata-rata
Menentukan Batas Diagram Rata-rata
Batas Kendali Atas (UCL) = Batas Kendali Bawah (LCL) = = rata-rata dari sampel =
xZX xZX
x
Z
X
x
Z
X
= Standar deviasi = 2 (95.5%) 3(99.7%)
= Standar deviasi rata-rata sampel
nx
Cara Lain
RAX 2
RAX 2Batas Kendali Atas =
Batas Kendali Bawah
Dimana :
x
A
R
2
= rentangan rata-rata sampel
= Nilai batas kendali
= rata-rata dari sampel rata-rata
Batas Bagan Rentangan
RDLCL
RDUCL
R
R
3
4
Bagan Rata-rata
(a)(a)
These These sampling sampling distributions distributions result in the result in the charts belowcharts below
(Sampling mean is (Sampling mean is shifting upward but shifting upward but range is consistent)range is consistent)
R-chartR-chart(R-chart does not (R-chart does not detect change in detect change in mean)mean)
UCLUCL
LCLLCL
x-chartx-chart(x-chart detects (x-chart detects shift in central shift in central tendency)tendency)
UCLUCL
LCLLCL
R-chartR-chart(R-chart detects (R-chart detects increase in increase in dispersion)dispersion)
UCLUCL
LCLLCL
Figure S6.5Figure S6.5
(b)(b)
These These sampling sampling distributions distributions result in the result in the charts belowcharts below
(Sampling mean (Sampling mean is constant but is constant but dispersion is dispersion is increasing)increasing)
x-chartx-chart(x-chart does not (x-chart does not detect the increase detect the increase in dispersion)in dispersion)
UCLUCL
LCLLCL
Bagan Jarak
Bagan Kendali Atribut
Mengukur persentase penolakan dalam sebuah sampel, bagan-p
Menghitung jumlah penolakan, bagan-c
For variables that are categoricalFor variables that are categorical
Good/bad, yes/no, Good/bad, yes/no, acceptable/unacceptableacceptable/unacceptable
Measurement is typically counting defectivesMeasurement is typically counting defectives
Charts may measureCharts may measure
Percent defective (p-chart)Percent defective (p-chart)
Number of defects (c-chart)Number of defects (c-chart)
Control Charts for Attributes
Control Limits for p-Charts
Population will be a binomial distribution, but Population will be a binomial distribution, but applying the Central Limit Theorem allows us to applying the Central Limit Theorem allows us to
assume a normal distribution for the sample assume a normal distribution for the sample statisticsstatistics
UCLUCLpp = p + z = p + zpp^̂
LCLLCLpp = p - z = p - zpp^̂
wherewhere pp ==mean fraction defective in the samplemean fraction defective in the samplezz ==number of standard deviationsnumber of standard deviationspp ==standard deviation of the sampling distributionstandard deviation of the sampling distribution
nn ==sample sizesample size
^̂
pp(1 -(1 - p p))nn
pp = =^̂
Contoh Soal
Jam Rata2 Jam Rata2 Jam Rata2
1 17.1 5 16.5 9 16.3
2 18.8 6 16.4 10 16.5
3 14.5 7 15.2 11 14.2
4 14.8 8 16.4 12 17.3
Ditanyakan : Batas kendali proses 9 boks yang mencakup 99.7%
Jawab :xZX UCLx = = 16 + 3
9
1
LCLx = xZX = 16 - 3
9
1
Setting Control Limits
Process average x Process average x = 16.01= 16.01 ounces ouncesAverage range R Average range R = .25= .25Sample size n Sample size n = 5= 5
Setting Control Limits
UCLUCLxx = x + A= x + A22RR
= 16.01 + (.577)(.25)= 16.01 + (.577)(.25)= 16.01 + .144= 16.01 + .144= 16.154 = 16.154 ouncesounces
Process average x Process average x = 16.01= 16.01 ounces ouncesAverage range R Average range R = .25= .25Sample size n Sample size n = 5= 5
From From Table S6.1Table S6.1
Setting Control Limits
UCLUCLxx = x + A= x + A22RR
= 16.01 + (.577)(.25)= 16.01 + (.577)(.25)= 16.01 + .144= 16.01 + .144= 16.154 = 16.154 ouncesounces
LCLLCLxx = x - A= x - A22RR
= 16.01 - .144= 16.01 - .144= 15.866 = 15.866 ouncesounces
Process average x Process average x = 16.01= 16.01 ounces ouncesAverage range R Average range R = .25= .25Sample size n Sample size n = 5= 5
UCL = 16.154UCL = 16.154
Mean = 16.01Mean = 16.01
LCL = 15.866LCL = 15.866
Contoh SoalSampleSample NumberNumber FractionFraction SampleSample NumberNumber FractionFractionNumberNumber of Errorsof Errors DefectiveDefective NumberNumber of Errorsof Errors DefectiveDefective
11 66 .06.06 1111 66 .06.0622 55 .05.05 1212 11 .01.0133 00 .00.00 1313 88 .08.0844 11 .01.01 1414 77 .07.0755 44 .04.04 1515 55 .05.0566 22 .02.02 1616 44 .04.0477 55 .05.05 1717 1111 .11.1188 33 .03.03 1818 33 .03.0399 33 .03.03 1919 00 .00.00
1010 22 .02.02 2020 44 .04.04
Total Total = 80= 80
(.04)(1 - .04)(.04)(1 - .04)
100100pp = = = .02= .02^̂p p = = .04= = .04
8080
(100)(20)(100)(20)
.11 .11 –
.10 .10 –
.09 .09 –
.08 .08 –
.07 .07 –
.06 .06 –
.05 .05 –
.04 .04 –
.03 .03 –
.02 .02 –
.01 .01 –
.00 .00 –
Sample numberSample number
Fra
ctio
n d
efec
tive
Fra
ctio
n d
efec
tive
| | | | | | | | | |
22 44 66 88 1010 1212 1414 1616 1818 2020
p-Chart for Data Entry
UCLUCLpp = p + z = p + zpp = .04 + 3(.02) = .10= .04 + 3(.02) = .10^̂
LCLLCLpp = p - z = p - zpp = .04 - 3(.02) = 0 = .04 - 3(.02) = 0^̂
UCLUCLpp = 0.10= 0.10
LCLLCLpp = 0.00= 0.00
p p = 0.04= 0.04
.11 .11 –
.10 .10 –
.09 .09 –
.08 .08 –
.07 .07 –
.06 .06 –
.05 .05 –
.04 .04 –
.03 .03 –
.02 .02 –
.01 .01 –
.00 .00 –
Sample numberSample number
Fra
ctio
n d
efec
tive
Fra
ctio
n d
efec
tive
| | | | | | | | | |
22 44 66 88 1010 1212 1414 1616 1818 2020
UCLUCLpp = p + z = p + zpp = .04 + 3(.02) = .10= .04 + 3(.02) = .10^̂
LCLLCLpp = p - z = p - zpp = .04 - 3(.02) = 0 = .04 - 3(.02) = 0^̂
UCLUCLpp = 0.10= 0.10
LCLLCLpp = 0.00= 0.00
p p = 0.04= 0.04
p-Chart for Data Entry
Possible assignable
causes present
Control Limits for c-Charts
Population will be a Poisson distribution, Population will be a Poisson distribution, but applying the Central Limit Theorem but applying the Central Limit Theorem
allows us to assume a normal distribution allows us to assume a normal distribution for the sample statisticsfor the sample statistics
wherewhere cc ==mean number defective in the samplemean number defective in the sample
UCLUCLcc = c + = c + 33 c c LCLLCLcc = c - = c - 33 c c
c-Chart for Cab Company
c c = 54= 54 complaints complaints/9/9 days days = 6 = 6 complaintscomplaints//dayday
|1
|2
|3
|4
|5
|6
|7
|8
|9
DayDay
Nu
mb
er d
efec
tive
Nu
mb
er d
efec
tive14 14 –
12 12 –
10 10 –
8 8 –
6 6 –
4 –
2 –
0 0 –
UCLUCLcc = c + = c + 33 c c
= 6 + 3 6= 6 + 3 6= 13.35= 13.35
LCLLCLcc = c - = c - 33 c c
= 3 - 3 6= 3 - 3 6= 0= 0
UCLUCLcc = 13.35= 13.35
LCLLCLcc = 0= 0
c c = 6= 6
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