tqm concept and the 8 tools of tqm
TRANSCRIPT
The Total Quality Management (TQM)
Customer-Focused Principles
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Where are You on this scale ?
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What’s TQM
I’ve heard about TQM
I’ve read about TQM
I’ve attended some TQM training
I’ve used some TQM tools
I have lots of experience with TQM
I taught Dr. Deming everything he knows about TQM
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Industrial Paradigm
Quality
Cost
VarietyResponsiveness
1913 1960 1980 2000
“Mass” “Lean” “Flexible” “Reconfigurable”
Production :
Objective :
“Interchangeable Parts”
Production Management
Computerization
“Knowledge Science”
Approach:
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Competition Strategy
Cost Quality Delivery Flexibility/Responsiveness
Innovation
1800 1960 1970 1980 1990 2000
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evolusi
1900 1918 1937 1960 1980
operator
foremen
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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Process improvement Product simplification Training costs
Inspection costs
Warranty cost Product liability
Scrap Rework
Internal Failure Costs
External Failure Costs
Appraisal Costs
Prevention Costs
Little or no defective work
No dissatisfiedcustomers
Very little inspection
An ounce of prevention is worth
a pound of cure
Decline
Decline
Decline
Increase
Decline
IncreaseCo
nti
nu
al Im
pro
vem
ent
Quality Costs
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Co
st
pe
r g
oo
d u
nit
of
pro
du
ct
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. DIAGRAM PARETO
4. DIAGRAM SEBAB AKIBAT
5. DIAGRAM TEBAR
6. STRATIFIKASI
7. PETA KONTROL
8. QUALITY FUNCTION DEPLOYMENT (QFD)
EIGHT TQM TOOLSEIGHT TQM TOOLS
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SAMURAI WITH SEVEN PORTABLE WEAPONS + 1 INTENTION
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Gusoku (armor)
Hoyo (Hood) Kabuto (Helmet)
Tachi (long sword)
Katana (sword)
Ya (arrow)
Yumi (bow)
7+1
7+1
AN INTENTION
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Fungsi
Menyajikan data yang berhubungan dgn :
• Distribusi proses produksi
• Defective item
• Defective location
• Defective cause
• Check up confirmation
CHECK SHEETCHECK SHEET
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CHECK SHEET
Product : Plant :
Usage : Dept. :
Specification :
Inspector :
Inspection number :
Lot No. :
Lot Size :
Supplier :
Measurement unit :
Weight (g) Tally Frequency
Total
Date :
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1. Guna : menyajikan data secara visual sehingga lebih mudah dilihat oleh pelaksanan
2. Mekanisme :
1. Kumpulkan data pengamatan (N)
data : minimum rumus statistik tentukan
2. Pilih harga maksimum & minimum
a) Susun data dalam baris & kolom
b) Pilih angka max. Tiap baris
c) Pilih angka min. tiap baris
d) Tentukan max & min dari keseluruhan
3. Hitung range ( R ) = max min
4. Tentukan jumlah kelas ( K )
K = f(R) = 1 + 3.3 log R
Atau
K =
atau
K = 10 ~ tentukan
N
HISTOGRAM
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5. Tentukan kelas interval ( KI)
KI = R/K
6. Tentukan batas bawah KI terendah
BB = min – KI/2
7. Tentukan BB, batas atas dan setiap nilai kelas
8. Kelompok data setiap kelas = f(data) nyatakan “tally – mark”
9. Hitung f ( frekwensi )
X (minus, 0, plus)
10. Hitung rata-rata & tandar deviasi
KI
K ?
NTmaxmin
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Gambarkan histogram dari data berikut ini :
Data max min
12 11 12.5 14 13.5 14 11
11 11.5 12 18 19 19 11.5
11 12 12 11.5 13 13 11
14 15 12 11 18 18 11
13 12 14.5 13.5 14.5 14.5 11.5
9 10.5 9.5 10.5 11 11 9
10 11 12 13 14 14 10
14 13.5 15 16 17 17 13.5
11 12 12 11.5 10 12 10
• R = max min = 19 9 (19 – 9 = 10)
• K = = 50/7 7,…. 8
• KI = R/K = 10/7 = 13/7 1.5
• BB = 9 – 1.5/2 = 8.25
• BA = 9.75 dst untuk setiap kelas.
N
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Batas Kelas NT Tallies
8.25 – 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
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8.25 20.25
5
11
22 1
17
10
2
X
10
f X
= 12.78 , SD = 2.31
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~ Petunjuk hierarki kepentingan persoalan cacat produk~ Mekanisme
1. Buat klasifikasi cacat2. Tentukan absis~ordinat3. Buat diagram % jumlah cacat
~ manfaat• membuat orang mau bekerja sama• dampak perbaikan besar• identifikasi tujuan terpilih
a b c d e
Kumulatif % cacat
DIAGRAM PARETODIAGRAM PARETO
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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 : Jumlah yang diinspeksi N = 2160Catatan produk cacat
Hd Bd Ld Md Cd
Jum
lah
c ac a
t
(jumlah) 200 100 (%)
75
50
25
00
100
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~ MANFAAT :• mengarahkan diskusi faktor sebab domonan• petunjuk pengumpulan dan pencatatan data• menunjukkan kemampuan pekerja
Menggambarkan hubungan sebab~akibat
C.E. DIAGRAMC.E. DIAGRAMC.E. DIAGRAMC.E. DIAGRAM
~ GUNA
• menganalisa kondisi aktual perbaikan mutu
efisiensi sumber daya
biaya • eliminasi kondisi ~ cacat / keluhan konsumen
• standarisasi
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TAHAPAN
1. Kelompok analisa masalah
2. Anak panah
3. “tulang” penyebab
sebelah kanan masalah mutu
4. Identifikasi
5. evaluasi
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Fishbone Chart Airline Customer Service
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~ MANFAAT :
• mengarahkan diskusi faktor sebab dominan• petunjuk pengumpulan dan pencatatan data• menunjukkan kemampuan pekerja
SCATTERED DIAGRAM
Melihat hubungan antar faktor
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No. Reaction Temperature Yield Y (%)
1 72.5 91.1
2 73.8 90.8
3 73.2 90.8
4 72.1 91.3
5 73.0 91.0
6 73.0 90.6
7 74.3 90.6
8 71.5 91.6
9 72.9 91.2
10 73.6 90.8
11 74.2 90.8
12 74.9 90.3
13 73.7 91.0
14 72.1 91.5
15 74.3 90.5
16 74.5 90.7
17 72.8 91.4
18 73.7 90.5
19 72.8 90.9
20 72.0 91.3
21 73.1 91.4
22 73.5 91.2
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X
X
X
X
X
X
X
Yie
ld
Reaction Temperature
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~ MANFAAT :
• mencari penyebab utama faktor kualitas• memisahkan data (kategorisasi) sesuai dengan
kelompok datanya• memudahkan pengambilan keputusan peta kontrol• mempelajari secara menyeluruh masalah yang dihadapi
STRATIFIKASI
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Grade A
Grade B
Grade C
Grade D
Stratifikasi kategorisasi
Mencari faktor penyebab utama
Ilustrasi :
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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 chart
Determine 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 customer requirements
Monitor times, customer satisfaction
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Service Quality Examples Hospitals
–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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~ MANFAAT :• mengendalikan proses• kecenderungan proses• identifikasi kebutuhan konsumen
pH
t
PETA KONTROL
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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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Produce GoodProvide Service
Stop Process
Yes
NoAssign.
Causes?Take Sample
Inspect Sample
Find Out WhyCreate
Control Chart
Start
Flow Diagram:Statistical Process Control Steps
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Constructing a Control Chart
Decide what to measure or count Collect the sample data Plot the samples on a control chart Calculate and plot the control limits on the control
chart 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 average
About an equal # points are above & below the centerline
Points appear randomly distributed
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99.74 %
The Normal Distribution
95 %
= 0 1 2 3-1-2-3
Area under the curve = 1.0
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Control Chart Z Values
Smaller Z values make more sensitive charts
Z = 3.00 is standard
Compromise between sensitivity and errors
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Control Charts and the Normal Distribution
Mean
UCL
LCL
+ 3
- 3
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Types Of Data
Attribute data (p-charts, c-charts)Product characteristics evaluated with a
discrete choice (Good/bad, yes/no, count)
Variable data (X-bar and R charts)Product characteristics that can be measured
(Length, size, weight, height, time, velocity)
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Control Charts For Attributes
p Charts–Calculate percent defectives in a sample;–an item is either good or bad
c Charts–Count number of defects in an item
p - Charts
Based on the binomial distribution
– p = number defective / sample size, n
– p = total no. of defectives
total no. of sample observations
UCLp = p + 3 p(1-p)/n
LCLp = p - 3 p(1-p)/n
p-Chart Calculations
Proportion Sample Defect Defective
1 6 .06 2 0 .00 3 4 .04
. . .
. . .20 18 .18 200 1.00
= 0.10
=
total defectives total sample observations 200 20 (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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. .
0
0.02
0.04
0.06
0.08
0.1
0.12
0.14
0.16
0.18
0.2
0 2 4 6 8
10 12 14 16 18 20
Prop
ortio
n de
fect
ive
Sample number
c - Charts
Count the number of defects in an item
Based on the Poisson distribution
– c = number of defects in an item
– c = total number of defects– number of samples
– UCLc = c + 3 c
LCLc = c - 3 c
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c - Chart Calculations
Count # of defects per roll in 15 rolls of denim fabric
Sample Defects1 122 83 16. .
. .15 15
190
c = 190/15 = 12.67
UCL = c + z c = 12.67 + 3 12.67 = 23.35
LCL = c - z c = 12.67 - 3 12.67 = 1.99
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Example c - Chart
.
0
3
6
9
12
15
18
21
24
0 2 4 6 8
10
12
14
Sample number
Nu
mb
er
of
de
fect
s
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Control Charts For Variables
Mean chart (X-Bar Chart)–Measures central tendency of a sample
Range chart (R-Chart)–Measures amount of dispersion in a sample
Each chart measures the process differently. Both the process average and process variability must be in control for the process to be in control.
Example: Control Charts for Variable Data Slip Ring Diameter (cm)Sample 1 2 3 4 5 X R
1 5.02 5.01 4.94 4.99 4.96 4.98 0.082 5.01 5.03 5.07 4.95 4.96 5.00 0.123 4.99 5.00 4.93 4.92 4.99 4.97 0.084 5.03 4.91 5.01 4.98 4.89 4.96 0.145 4.95 4.92 5.03 5.05 5.01 4.99 0.136 4.97 5.06 5.06 4.96 5.03 5.01 0.107 5.05 5.01 5.10 4.96 4.99 5.02 0.148 5.09 5.10 5.00 4.99 5.08 5.05 0.119 5.14 5.10 4.99 5.08 5.09 5.08 0.15
10 5.01 4.98 5.08 5.07 4.99 5.03 0.10 50.09 1.15
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Constructing an Range Chart
UCLR = D4 R = (2.11) (.115) = 2.43
LCLR = D3 R = (0) (.115) = 0
where R = R / k = 1.15 / 10 = .115 k = number of samples = 10 R = range = (largest -
smallest)
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3 Control Chart Factors
Sample size X-chart R-chartn A2 D3 D4
2 1.88 0 3.27
3 1.02 0 2.57
4 0.73 0 2.28
5 0.58 0 2.11
6 0.48 0 2.00
7 0.42 0.08 1.92
8 0.37 0.14 1.86
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0
0,05
0,1
0,15
0,2
0,25
0,3
1 2 3 4 5 6 7 8 9 10
Sample number
Ra
ng
e
Example R-Chart
UCL
R
LCL
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Constructing A Mean Chart
UCLX = X + A2 R = 5.01 + (0.58) (.115) = 5.08
LCLX = X - A2 R = 5.01 - (0.58) (.115) = 4.94
where X = average of sample means = X / n = 50.09 / 10 = 5.01
R = average range = R / k = 1.15 / 10 = .115
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4,92
4,94
4,96
4,98
5,00
5,02
5,04
5,06
5,08
5,10
1 2 3 4 5 6 7 8 9 10
Sample number
Sa
mp
le a
vera
ge
Example X-bar Chart
UCL
X
LCL
Penyebaran Fungsi Kualitas
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Needs
User
Developer
Build to Requirements
Specification
Requirements
1. Produsen mengharapkan konsumen mengubah keinginan menjadi permintaan yang dapat dimengerti
2. Konsumen menyatakan dengan baik keinginan mereka tetapi produsen tidak memuaskan keinginan mereka
Needs
Requirements
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Alat utama QFDTECHNICAL
INFORMATION
CUSTOMER INFORMATION
Rumah Kualitas
• Suatu proses perencanaan
• Input: keinginan dan kebutuhan konsumen
• Penggunaan matriks untuk mencatat informasi penting
• Memungkinkan analisa dan penentuan isu-isu utama
• Out put : isu-isu tindakan kunci untuk memperbaiaki kepuasan konsumen berdasarkan input konsumen
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Identifikasi keinginan pelanggan
Mempelajari ketentuan teknis dalam menghasilkan barang atau jasa
Hubungan antara keinginan dan ketentuan teknis
Perbandingan kinerja dengan pesaing
Evaluasi Pelanggan
Trade off
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MATRIKS HUBUNGAN
Trade off
Karakteristik proses
HARAPANPERBANDINGAN KINERJA PERUSAHAAN
NILAI RELATIF
TINGKAT KEPENTINGAN
EVALUASI PELANGGAN
Lakukan analisa
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Penentuan konsumen ahli
Judgement Sampling
Wawancara dengan konsumen ahli
Hasil wawancara : Atribut kualitas
Pembobotan dengan metode perbandingan berpasanganCara perhitungan pembobotan:
Mendengarkan Suara Konsumen (Voice of Customer) untuk menentukan harapan pelanggan
Caranya:
Membuat skala perbandingan yang disebt skala fundamental yang diturunkan berdasarkan riset psikologis atas kemampuan individu dalam membuat suatu perbandingan secara berpasangan terhadap beberapa elemen yang diperbandingkan. Skala perbandingan tersbut adalah ;1: sama penting, 2: sedikit lebih penting, 5: lebih penting, 7: jauh lebih penting, 9: sangat lebih penting, angka 2, 4,6,8 adalah nilai atara
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Cara Penentuan Hubungan Keterkaitan dan Trade Roof
1. Hubungan Keterkaitan
Hubungan antara harapan pelanggan dan karakteristik proses dapat dinyatakan dengan menggunakan lambang-lambang tertentu untuk menyatakan hubungan. Lambang dan nilai yang umum digunakan adalah sebagai berikut.
= 10 = Hubungan Kuat
= 5 = Hubungan sedang
= 1 = Hubungan lemah
Penentuan kuat, sedang dan lemah dikerjakan dengan membuat pertanyaan apakah dengan mengerjakan karakteristik proses ini maka dapat memuasakan harapan pelanggan.
Harus diterapkan setiap kolom antara satu karakteristik proses dengan harapan pelanggan
Brainstorming dengan manager ahli yang mengetahui secara mendalam mengenai proses
produksi
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0.1120.2060.1840.1350.0490.2060.108Nilai Relatif
4544445PT A
1252302051505523012 0NILAI (Tingkat kepentingan)
5555445PT B
PT SM 5555445
4,14; 3; 41Bentuk standar
4,14; 3; 42Ukuran seragam
4,14; 4; 43Daya tahanproduk
4,14; 3; 44Warna
4,13; 3; 35Keamanan pangan
5,15; 4; 5 5Kebersihan
5,15; 4; 5 6Kesegaran
Harapan pelanggan
VIITarge;Rasio
PT SM; PT A; PTB
VIIIVIVIVII IIII
+
+
++
++
++
++
++
+
+ ++
++
++
++
++
+
++
++
++
++
++
++
+++
++
: kuat (10): sedang (5): lemah (1)
Keterangan :
I. BOBOT KONVERSI
II. PENGADAAN BAHAN BAKU
III. PENANGANAN BAHAN BAKU
IV. PENERIMAAN
V. SORTASI
VI. PENGEMASAN
VII. PENYIMPANAN
VIII. DISTRIBUSI
Gambar Rumah Kualitas Sayuran Segar PT. SM
69
+
21,912,616,514,915,2Nilai Relatif
44
4
4 5PT. A
140801059597NILAI (Tingkat kepentingan)
55
44
5
PT.B
PT .X 34443
1
2
4; 14; 4; 4; 3
3
Harga
4; 14; 4; 3; 3
4
Desain
5; 1,663; 5; 4; 4Kenyamanan
5; 2,52; 5; 4; 5
5
Kekuatan
4; 1 4; 4; 4; 4 Keselamatan
Harapan pelanggan
Target ; Rasio
PT X;A;B;C
VIVIVII IIII
Atribut Pelanggan
: kuat (10): sedang (5): lemah (1)
PT.C
3
4
4
5 2
++
+ ++
+ ++
++
Keterangan :I Bobot KonversiII PencampuranIII PelapisanIV PenggabunganV PenyemprotanVI PemasakanVII Pengujian
Proses Produksi
VII
4
4
4
5
120
18,8
+
+
++
++
Rumah Kualitas Ban PT. G.Y +
70
•Marimin, 2004, Teknik dan Aplikasi
Pengambilan Keputusan Kriteria Majemuk,
Grassindo.
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• Pilih kasus industri yang anda paling kuasai
• Susun salah satu alur proses produksi atau sistem pengelolaannya
• Rancang QFD-nya
• Diskusikan matrik QFD yang telah anda rancang tersebut.
•Kendalikan respon teknik dominan/kritis dengan Statistical process control.