Register now to access 7 million high quality study materials (What's Course Hero?) Course Hero is the premier provider of high quality online educational resources. With millions of study documents, online tutors, digital flashcards and free courseware, Course Hero is helping students learn more efficiently and effectively. Whether you're interested in exploring new subjects or mastering key topics for your next exam, Course Hero has the tools you need to achieve your goals.
5 Pages
Sunum4
Course: SIMULATION 20091, Spring 2009School: Istanbul Technical...
Rating:
Word Count: 1188
Document Preview
Test: Sezgisel Kutu Grafii Gzlenen 1 2 3 4 5 6 7 8 9 10 11 Uygunluk Testleri Gzlemlerin dalm fonksiyonu F olan bir dalmndan alnm bamsz veriler olup olmadna ilikin hipotez testleridir. H 0 : X iler dalm fonksiyonu F olan IID rastsal deikendirler. Az veri says iin farkllklara duyarszdr. Veri says bydke H 0 hipotezi her zaman ret edilir. 1 2 Teorik Kantil U deer Oktil Kartil Medyan Kartil Oktil U deer Yzde 0.000...
Unformatted Document Excerpt
Coursehero >> Other International >> Istanbul Technical University >> SIMULATION 20091Course Hero has millions of student submitted documents similar to the one
below including study guides, practice problems, reference materials, practice exams, textbook help and tutor support.
Course Hero has millions of student submitted documents similar to the one
below including study guides, practice problems, reference materials, practice exams, textbook help and tutor support.
Test: Sezgisel Kutu Grafii
Gzlenen
1 2 3 4 5 6 7 8 9 10 11
Uygunluk Testleri
Gzlemlerin dalm fonksiyonu F olan bir dalmndan alnm bamsz veriler olup olmadna ilikin hipotez testleridir. H 0 : X iler dalm fonksiyonu F olan IID rastsal deikendirler. Az veri says iin farkllklara duyarszdr. Veri says bydke H 0 hipotezi her zaman ret edilir.
1 2
Teorik
Kantil U deer Oktil Kartil Medyan Kartil Oktil U deer Yzde 0.000 0.125 0.250 0.500 0.750 0.825 1.000 Gzlenen Deer 0.08 0.57 1.13 2.89 6.21 7.36 11.54 Teorik Deer 0.00 0.49 1.05 2.53 5.06 6.36 -
Teorik dalmn birikimli olaslk fonksiyonu kullanlarak kantil deerleri bulunur.
Uygunluk Testleri
1. Ki-Kare testi 2. Kolmogorov-Smirnov testi 3. Anderson-Darling testi
rnek 1
Aadaki veriler toplanm olsun:
No 1 2 3 4 5 6 7 8 9 10 Deer 4.78 5.35 3.84 3.47 3.27 3.78 3.33 3.73 4.35 1.92 No 11 12 13 14 15 16 17 18 19 20 Deer 2.25 4.58 4.51 3.24 3.33 3.69 4.75 4.48 6.02 4.00
3
4
rnek 1 - Snflar
Snf Alt Limit st Limit 1.915 2.745 3.575 4.405 5.235 2.745 3.575 4.405 5.235 6.065 Toplam Gzlenen Sklk 2 5 6 5 2 20
rnek 1 - Histogram
Teorik ve Gzlenen S kl k 7 6 5 4 3 2 1 0 2.33 3.16 3.99 Snf Orta De eri 4.82 5.65
x min = 1.92 x max = 6.02 n = 20 sn = 5 gs = 6.02 1.92 = 0.82 0.83 5
5
6
1
rnek 1 Q-Q Grafii
Q-Q Grafii No 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 Gzlenen Olas lk 0.025 0.075 0.125 0.175 0.225 0.275 0.325 0.375 0.425 0.475 0.525 0.575 0.625 0.675 0.725 0.775 0.825 0.875 0.925 0.975 Deer 1.92 2.25 3.24 3.27 3.33 3.33 3.47 3.69 3.73 3.78 3.84 4.00 4.35 4.48 4.51 4.58 4.75 4.78 5.35 6.02 z Deeri -1.96 -1.44 -1.15 -0.93 -0.76 -0.60 -0.45 -0.32 -0.19 -0.06 0.06 0.19 0.32 0.45 0.60 0.76 0.93 1.15 1.44 1.96 Teorik De er 2.03 2.53 2.81 3.03 3.19 3.35 3.49 3.62 3.75 3.87 3.99 4.11 4.24 4.37 4.51 4.67 4.83 5.05 5.33 5.83
rnek 1 P-P Grafii
P-P Grafii No De er 1.92 2.25 3.24 3.27 3.33 3.33 3.47 3.69 3.73 3.78 3.84 4.00 4.35 4.48 4.51 4.58 4.75 4.78 5.35 6.02 Gzlenen Teorik Olaslk Olaslk 0.025 0.075 0.125 0.175 0.225 0.275 0.325 0.375 0.425 0.475 0.525 0.575 0.625 0.675 0.725 0.775 0.825 0.875 0.925 0.975 0.019 0.042 0.238 0.248 0.268 0.268 0.318 0.402 0.418 0.439 0.463 0.529 0.667 0.715 0.725 0.749 0.801 0.810 0.928 0.984
Q-Q Grafii
5.9 5.4 4.9 4.4 3.9 3.4 2.9 2.4 1.9 1.9 2.9 3.9 4.9 5.9
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
P-P Grafii
1.0 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0.0 0.0 0.2 0.4 0.6 0.8 1.0
7
8
rnek 1 Kutu Grafii
rnek 1 Ki-Kare Testi
Snf Alt Limit st Limit 1.915 2.745 3.575 4.405 5.235 Teorik Gzlenen z Birikimli Olas lk Sklk Deeri Olas lk 2.745 2 -1.22 0.111 0.111 3.575 5 -0.37 0.356 0.245 4.405 6 0.49 0.688 0.332 5.235 5 1.35 0.912 0.224 6.065 2 2.20 1.000 0.088 Toplam 20 1 Sklk 2.22 4.90 6.64 4.48 1.76 20.00
1
2
3
4
5
6
Kantil U deer Oktil Kartil Medyan Kartil Oktil U deer
Yer 1 3 5.5 10.5 15.5 18 20
Yzde Deer 0.000 1.92 0.125 3.24 0.250 3.33 0.500 3.81 0.750 4.55 0.825 4.78 1.000 6.02
9
Snf Alt Limit st Limit 3.575 4.405 Gzlenen Teorik Ki-Kare Sklk Sklk 3.575 7 7.12 0.002 4.405 6 6.64 0.062 7 6.24 0.093 Toplam 20 20 0.157
sd = s k 1 = 3 2 1 = 0
Serbestlik derecesi 0 olamayaca iin 1 alnacaktr.
= 0.05 12, 0.05 = 3.84
10
rnek 1 Kolmogorov-Smirnov
Snf Alt Limit st Limit 1.915 2.745 3.575 4.405 5.235 2.745 3.575 4.405 5.235 6.065 Teorik z De eri -1.22 -0.37 0.49 1.35 2.20 Birikimli Olas lk 0.111 0.356 0.688 0.912 1.000 Gzlenen Sklk 2 5 6 5 2 Birikimli Olas lk 0.10 0.35 0.65 0.90 1.00 En Byk Fark 0.011 0.006 0.038 0.012 0.000 0.038
rnek 1 Ki-Kare (Eit)
Snf Alt Limit 3.27574 3.93000 4.58426 st Limit 3.27574 3.93000 4.58426 Teorik Birikimli z Olaslk Deeri 0.25 -0.675 0.50 0.000 0.75 0.675 1.00 Gzlenen Ki-Kare Sklk 4 0.2 7 0.8 5 0.0 4 0.2 Toplam 1.2
Sklk 5 5 5 5
sd = n = 20 = 0.05 D20, 0.05 = 0.294
11
sd = s k 1 = 4 2 1 = 1
= 0.05 12, 0.05 = 3.84
12
2
rnek 1 KS (Alt-st Sapma)
Gzlenen Olaslk No 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 De er 1.92 2.25 3.24 3.27 3.33 3.33 3.47 3.69 3.73 3.78 3.84 4.00 4.35 4.48 4.51 4.58 4.75 4.78 5.35 6.02 Alt st Kestirim Kestirim 0.00 0.05 0.10 0.15 0.20 0.25 0.30 0.35 0.40 0.45 0.50 0.55 0.60 0.65 0.70 0.75 0.80 0.85 0.90 0.95 0.05 0.10 0.15 0.20 0.25 0.30 0.35 0.40 0.45 0.50 0.55 0.60 0.65 0.70 0.75 0.80 0.85 0.90 0.95 1.00 Teorik Birikimli Olaslk 0.019 0.042 0.238 0.248 0.268 0.268 0.318 0.402 0.418 0.439 0.463 0.529 0.667 0.715 0.725 0.749 0.801 0.810 0.928 0.984 Alt Sapma 0.019 0.008 0.138 0.098 0.068 0.018 0.018 0.052 0.018 0.011 0.037 0.021 0.067 0.065 0.025 0.001 0.001 0.040 0.028 0.034 st Sapma 0.031 0.058 0.088 0.048 0.018 0.032 0.032 0.002 0.032 0.061 0.087 0.071 0.017 0.015 0.025 0.051 0.049 0.090 0.022 0.016 En Byk Byk Sapma 0.031 0.058 0.138 0.098 0.068 0.032 0.032 0.052 0.032 0.061 0.087 0.071 0.067 0.065 0.025 0.051 0.049 0.090 0.028 0.034 0.138
K-S Testi (Dzeltilmi)
Durum Dzeltilmi Test statistii
0.11 n + 0.12 + Dn n 0.85 n 0.01 + Dn n 0.2 0.5 Dn n + 0.26 + n n
0.850 1.138 0.775 0.926
0.900 1.224 0.819 0.990
1- 0.950 1.358 0.895 1.094 1-
0.975 1.480 0.955 1.190
0.990 1.628 1.035 1.308
sd = n = 20 = 0.05 D20, 0.05 = 0.294
Parametreler biliniyor Normal stel
Weibull
n Dn
n 10 20 50
0.900 0.760 0.779 0.790 0.803
0.950 0.819 0.843 0.856 0.874
0.975 0.880 0.907 0.922 0.939
0.990 0.944 0.973 0.988 1.007
13
14
rnek 1 Anderson Darling
No 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 Deer 1.92 2.25 3.24 3.27 3.33 3.33 3.47 3.69 3.73 3.78 3.84 4.00 4.35 4.48 4.51 4.58 4.75 5.35 4.78 6.02 Kkten Bye F(x i) 0.019 0.042 0.238 0.248 0.268 0.268 0.318 0.402 0.418 0.439 0.463 0.529 0.667 0.715 0.725 0.749 0.801 0.81 0.928 0.984 Bykten Ke F(xn-i-1) 0.984 0.928 0.81 0.801 0.749 0.725 0.715 0.667 0.529 0.463 0.439 0.418 0.402 0.318 0.268 0.268 0.248 0.238 0.042 0.019 ln(F(x i)) -3.963 -3.17 -1.435 -1.394 -1.317 -1.317 -1.146 -0.911 -0.872 -0.823 -0.77 -0.637 -0.405 -0.335 -0.322 -0.289 -0.222 -0.211 -0.075 -0.016 ln(1-F(xn-i-1)) -4.135 -2.631 -1.661 -1.614 -1.382 -1.291 -1.255 -1.1 -0.753 -0.622 -0.578 -0.541 -0.514 -0.383 -0.312 -0.312 -0.285 -0.272 -0.043 -0.019 Toplam A
2
A-D Testi (Dzeltilmi)
Durum Parametreler biliniyor Normal stel Weibull Dzeltilmi Test statistii An2 n5
4 25 2 1 + 2 An n n 0.6 2 1 + An n 0.2 2 1 + An n
-8.098 -17.403 -15.48 -21.056 -24.291 -28.688 -31.213 -30.165 -27.625 -27.455 -28.308 -27.094 -22.975 -19.386 -18.386 -18.631 -16.731 -16.905 -4.366 -1.365 0.281
Zi = F(X(i) )
1 n A2 = (2i 1){ln( i ) +ln( Zni+1)} n Z 1 n i=1
Normal dalm iin
1- 0.900 1.933 0.632 1.070 0.637 0.950 2.492 0.751 1.326 0.757 0.975 3.070 0.870 1.587 0.877 0.990 3.857 1.029 1.943 1.038
Dzeltilmi
Dzeltilmi
4 25 A 2 = 1 + 2 A 2 n n
4 25 A 2 = 1 + 2 0.281 = 0.320 20 20
sd = n = 20
= 0.05
2 A20, 0.05 = 0.751
15 15
16
rnek 2
Aadaki veriler toplanm olsun:
No 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 Deer 6.80 0.20 14.67 0.97 5.01 1.73 0.17 8.26 1.49 0.31 0.35 8.43 4.89 2.85 0.67
17 17
rnek 2 - Snflar
Snf Alt Limit st Limit 0.165 5.005 9.845 Gzlenen Sklk 5.005 11 9.845 3 14.685 1 Toplam 15
xmin = 0.17 xmax = 14.67 n = 15 sn = 3 gs = 14.67 0.17 = 4.83 4.84 3
18
3
rnek 2 - Histogram
Teorik ve Gzlenen S kl k 12 10 8 6 4 2 0 2.585 7.425 S nf Orta De eri 12.265
rnek 2 Q-Q Grafii
Q-Q Grafii No 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
19
Gzlenen Olaslk 0.033 0.100 0.167 0.233 0.300 0.367 0.433 0.500 0.567 0.633 0.700 0.767 0.833 0.900 0.967
Deer 0.17 0.20 0.31 0.35 0.67 0.97 1.49 1.73 2.85 4.89 5.01 6.80 8.26 8.43 14.67
Teorik Deer 0.13 0.40 0.69 1.01 1.35 1.73 2.15 2.63 3.17 3.80 4.56 5.52 6.79 8.73 12.89
Q-Q Grafii
14.00 12.00 10.00 8.00 6.00 4.00 2.00 0.00 0.00 2.00 4.00 6.00 8.00 10.00 12.00 14.00
20
rnek 2 P-P Grafii
P-P Grafii No 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 Deer 0.17 0.20 0.31 0.35 0.67 0.97 1.49 1.73 2.85 4.89 5.01 6.80 8.26 8.43 14.67 Gzlenen Teorik Olaslk Olaslk 0.033 0.044 0.100 0.051 0.167 0.079 0.233 0.088 0.300 0.162 0.367 0.226 0.433 0.325 0.500 0.366 0.567 0.529 0.633 0.725 0.700 0.733 0.767 0.834 0.833 0.887 0.900 0.892 0.967 0.979
rnek 2 Kutu Grafii
P-P Grafii
1.0 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0.0 0.0 0.2 0.4 0.6 0.8 1.0
0
4
8
12
16
Kantil U deer Oktil Kartil Medyan Kartil Oktil U deer
21
Yer 1 2.5 4.5 8.0 11.5 13.5 15
Yzde Deer 0.000 0.17 0.125 0.26 0.250 0.51 0.500 1.73 0.750 5.91 0.825 8.35 1.000 14.67
22
rnek 2 Ki-Kare Testi
Snf Alt Limit st Limit 0.165 5.005 9.845 Gzlenen Birikimli Sklk Olaslk 5.005 11 0.733 9.845 3 0.926 14.685 1 1.000 Toplam 15 Teorik Olaslk 0.733 0.193 0.074 1 Sklk 11.00 2.90 1.10 15
rnek 2 Kolmogorov-Smirnov
Snf Teorik Alt st Birikimli Limit Limit Olaslk 0.165 5.005 0.733 5.005 9.845 0.926 9.845 14.685 1.000 Gzlenen Birikimli Olaslk 11 0.733 3 0.933 1 1.000 En Byk
Sklk
Fark 0.000 0.007 0.000 0.007
Snf Alt Limit st Limit 5.005 5.005 Toplam Gzlenen Sklk 11 4 15 Teorik Sklk 11 4 15 Ki-Kare 0.00 0.00 0.00
sd = s k 1 = 2 1 1 = 0
Serbestlik derecesi 0 olamayaca iin 1 alnacaktr.
sd = n = 15
= 0.05
= 0.05
D15, 0.05 = 0.338
12, 0.05 = 3.84
23
24
4
rnek 2 Ki-Kare (Eit)
Teorik Birikimli Alt Limit st Limit Sklk Olaslk 1.53671 0.33 5 1.53671 4.16374 0.67 5 4.16374 1.00 5 Snf Gzlenen Sklk 7 2 6 Toplam
rnek 2 KS (Alt-st Sapma)
No De er Gzlenen Olaslk Teorik Alt st Alt Birikimli Kestirim Kestirim Sapma Olas lk 0.17 0.00 0.07 0.044 0.044 0.20 0.07 0.13 0.051 0.016 0.31 0.13 0.20 0.079 0.054 0.35 0.20 0.27 0.088 0.112 0.67 0.27 0.33 0.162 0.105 0.97 0.33 0.40 0.226 0.107 1.49 0.40 0.47 0.325 0.075 1.73 0.47 0.53 0.366 0.101 2.85 0.53 0.60 0.529 0.004 4.89 0.60 0.67 0.725 0.125 5.01 0.67 0.73 0.733 0.066 6.80 0.73 0.80 0.834 0.101 8.26 0.80 0.87 0.887 0.087 8.43 0.87 0.93 0.892 0.025 14.67 0.93 1.00 0.979 0.046 st Sapma 0.023 0.082 0.121 0.179 0.171 0.174 0.142 0.167 0.071 0.058 0.000 0.034 0.020 0.041 0.021 En By k Byk Sapma 0.044 0.082 0.121 0.179 0.171 0.174 0.142 0.167 0.071 0.125 0.066 0.101 0.087 0.041 0.046 0.179
Ki-Kare 0.8 1.8 0.2 2.8
sd = s k 1 = 3 1 1 = 1
= 0.05 12,0.05 = 3.84
25
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
sd = n = 15
= 0.05
D15, 0.05 = 0.338
26
rnek 2 Anderson Darling
No Deer 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 0.17 0.20 0.31 0.35 0.67 0.97 1.49 1.73 2.85 4.89 5.01 6.80 8.26 8.43 14.67 Birikimli Olaslk Kkten Bykten Ke Bye Fn-i+1(xi) Fi(x i) 0.044 0.979 0.051 0.892 0.079 0.887 0.088 0.834 0.162 0.733 0.226 0.725 0.325 0.529 0.366 0.366 0.529 0.325 0.725 0.226 0.733 0.162 0.834 0.088 0.887 0.079 0.892 0.051 0.979 0.044 ln(Fi(xi)) ln(1-Fn-i+1(x i)) -3.124 -2.976 -2.538 -2.43 -1.82 -1.487 -1.124 -1.005 -0.637 -0.322 -0.311 -0.182 -0.12 -0.114 -0.021 -3.863 -2.226 -2.18 -1.796 -1.321 -1.291 -0.753 -0.456 -0.393 -0.256 -0.177 -0.092 -0.082 -0.052 -0.045 Toplam A
2
-6.987 -15.606 -23.59 -29.582 -28.269 -30.558 -24.401 -21.915 -17.51 -10.982 -10.248 -6.302 -5.05 -4.482 -1.914 0.826
stel dalm iin
Dzeltilmi
0.6 2 A2 = 1 + A n
Dzeltilmi
0 .6 A 2 = 1 + 0.826 = 0.860 15
sd = n = 15
= 0.05
2 A15, 0.05 = 1.326
27
5
Find millions of documents on Course Hero - Study Guides, Lecture Notes, Reference Materials, Practice Exams and more.
Course Hero has millions of course specific materials providing students with the best way to expand
their education.
Below is a small sample set of documents:
Below is a small sample set of documents:
Istanbul Technical University - LOGISTICS - 20091
Winter 2009 Prof. Dr. Fsun lenginProf.Dr. Fsun lengin, 20091What Is a Supply Chain?Flow of products and services from: Raw materials manufacturers Intermediate products manufacturers End product manufacturers Wholesalers and distributors and Retaile
Istanbul Technical University - LOGISTICS - 20091
Prof.Dr. Fsun lengin, 20091Logistics Strategy The 4 problem areas of supply chain planningCustomer ser vice levels Facility location Inventory decisions Transportation decisionsRecall the logistics strategy triangle When to plan?No distribution net
Istanbul Technical University - LOGISTICS - 20091
Prof.Dr. Fsun lengin, 20091The Logistics/Supply Chain ProductLogistics/Supply Chain managers are owners of the product-flow process from raw material sources to final consumers, not activity administrators.Prof.Dr. Fsun lengin, 20092Product in the P
Istanbul Technical University - LOGISTICS - 20091
Prof. Dr. Fsun lenginFusun Ulengin, 20091Customer Service in Planning TriangleInventory Strategy Forecasting Inventory decisions Purchasing and supply scheduling decisions Storage fundamentals Storage decisionsTransport StrategyORGANIZING The prod
Istanbul Technical University - LOGISTICS - 20091
Data Collection in Logistics ActivitiesProf. Dr. Fsun lenginData collectionData needed in a typical network configuration problem:Location of customers, retailers, existing warehouses and distribution centers, manufacturing facilities and suppliers Al
Istanbul Technical University - LOGISTICS - 20091
Transport Mode Selection ProblemFsun lengin, 20091Transport Fundamentals in Planning TriangleInventory StrategyORGANIZING The product Logistics service Ord . proc. & info. sys.Location Strategy Location decisions The network planning processFsun
Istanbul Technical University - LOGISTICS - 20091
Prof.Dr. Fsun lenginFusun Ulengin1Vehicle Routing: Separate single origin and destination: Once we have selected transport mode and have goods that need to go from point A to point B, we must decide how to route a vehicle (or vehicles) from point A
Istanbul Technical University - LOGISTICS - 20091
Prof. Dr. Fsun lenginFsun lengin, 20091The Savings Method for VRPStop dA,0 A 0 Depot dB,0 d0,B d0,A B 0 Depot dB,0 Bd0,AA dA,BStop (a) Initial routing (b) Combining two stops on a route Route distance = d +dA,0 +d0,B + d Route distance = d0,A +dA,B
Istanbul Technical University - LOGISTICS - 20091
SINGLE FACILITY LOCATION MODELSProf.Dr. Fsun lengin, 20091Facility Location Decisions Classifying location decisions Driving force (critical factor - traffic, labor rates, emergency facilities, obnoxious facilities) Number of facilities Discrete vs. c
Istanbul Technical University - LOGISTICS - 20091
Drawbacks of Single Facility Location Models Demand is assumed to be concentrated at a single point, when in fact demand is generated from a number of retail stores or individual companies. The market center of gravity is often used as the demand focal p
Istanbul Technical University - SIMULATION - 20091
T ENDSTR MHENDSL BLM SSTEM SMLASYONU YARIYIL SINAV SORU YANITLARI 29/03/2005 Soru 1. nce elle simlasyonda kullanacamz ters dnm denklemlerini bulmamz gerekmektedir. Dier dalmlar ak bir ekilde belirtilmiken gerek sre katsaysnn verileri verilmi ve gen dalma
Istanbul Technical University - LOGISTICS - 20091
TALEP ve ARZ PLANLAMAProf. Dr. Mehmet TANYA mehmet.tanyas@okan.edu.trKasm 2007, stanbul RKETLERDE TZY YAPILANMASITEDARK ZNCR YNETCSLOJSTK YNETM PAKETLEME DEPO YNETM TALEP ve TALEP ARZ PLANLAMA ARZ SPAR YNETM ve ve SEVKIYAT SEVKIYAT DI TCARET CARET OP
Istanbul Technical University - LOGISTICS - 20091
TRANSPORTASYON YNET MMehmet TANYATRANSPORTASYON YNET M Transportasyon/Tamaclk, yk ve/veya insanlarn bir yerden baka bir yere fiziksel hareketidir. Yk tamacl, lojistiin temel ve en nemli ksmdr. Uluslararas lojistikte, Transportasyon fonksiyonu daha da n
Istanbul Technical University - LOGISTICS - 20091
ULUSLARARASI LOJ ST KMehmet TANYAULUSLARARASI T CARET ve LOJ ST KD piyasaya nasl girileceinin karar, lojistie olan talep zerinde nemli bir etkiye sahiptir.hracat : En az sorumluluk ykleyen metot olup, rn bir kurumsal mteriye ihracat yoluyla gnderilir.
Istanbul Technical University - LOGISTICS - 20091
Geleneksel DatmBAA ADepoBAA BB Tedarikiler Kabul Yerletirme Depolama kmal Toplama SevkyatMaazalarapraz SevkyatBAAapraz apraz Sevkyat Tesisi TesisiBAABB Tedarikiler Kabul lem (<24hr) SevkyatMaazalarCrossdocking Crossdocking
Istanbul Technical University - LOGISTICS - 20091
Depo Ynetiminin AmalarDEPO YNET MProf. Dr. Mehmet TANYAEn Az Alan/Hacm En Fazla Depolama Talepleri Hzla Karlamak En Az Fire Etkin Gvenlik Veri Gvenilirlii En Az Hatal Sevkiyat1 2Depo levleriTEMEL LEVLERDEPO S STEM ELEMANLARISabitlerAra says Perso
Istanbul Technical University - LOGISTICS - 20091
DI T CARET NED R?Bir lkedeki mevcut ticari kurulularn, dier bir lkedeki ticari kurulular ile mevcut kanun ve kurallar erevesinde yapm olduklar ticari alveriin btnne D Ticaret denir. Gmrk Mstearl, lkemizde bu kurallar ile ilgili mevzuat uygulamakla ykmldr
Istanbul Technical University - LOGISTICS - 20091
GZET M RKET NED R? GZETGzetim irketi; uluslararas kurallarn gerei olarak, mevzuat, sat akdi veya akreditifler gereince tart ve analiz yaplmasn zorunlu bulunan hallerde, Ticarete konu olan malda (mamul/yar mamul) aranan zelliklerle ilgili kalite kontrol v
Istanbul Technical University - LOGISTICS - 20091
T.C. BABAKANLIK GMRK MSTEARLIIGMRK MEVZUATI UYGULAMALARIGMRK KANUNUGenel Hkmler (Madde 1-14) Gmrk Vergilerinin ve Ticaret Politikalarnn Uygulanmasnda Esas Alnacak Unsurlar (Madde 15-32) Tatlarn Kontrol ve Gmrk Blgesine Getirilen Eyaya Uygulanacak Hkmle
Istanbul Technical University - LOGISTICS - 20091
LOJ ST K VE TEDAR K Z NC R YNET M NDE B LG TEKNOLOJ LERProf.Dr. Mehmet TANYA Okan niversitesi Uluslararas Lojistik Blm Bakan ve Lojistik Dernei Ynetim Kurulu Bakan Nisan 2008, stanbul A Tasarm Talep Planlama Mteri Hizmet Dzeyi-Stok Optimizasyonu retim i
Istanbul Technical University - LOGISTICS - 20091
LOJ ST K YNET M YNETProf. Dr. Mehmet TANYA OKAN niversitesi Uluslararas OKAN Lojistik Blm Bakan ve Lojistik ve Lojistik Dernei(LODER) Dernei(LODER) Ynetim Kurulu yesi ubat 2008 stanbulSUNUM ZET SUNUMLojistikte Verimlilik Nakliye Nedir? Lojistikte Nakli
東京大学 - BUSINESS A - 139
Y 1 2 3 4 5 6 7 8 9 10 11 12 5100 5300 5650 6300 6400 6700 7035 7000 6200 6100 5600 5900X 75 78 80 92 98 108 118 112 95 90 85 90Y 1 2 3 4 5 6 7 8 9 10 11 12 5100 5300 5650 6300 6400 6700 7035 7000 6200 6100 5600 5900X1 75 78 80 92 98 108 118 112 95 90
東京大学 - BUSINESS A - 139
CHAPTER 2 Basic Cost Management Concepts and Accounting for Mass Customization OperationsANSWERS TO REVIEW QUESTIONS2-1 Product costs are costs that are associated with manufactured goods until the time period during which the products are sold, when th
東京大学 - BUSINESS A - 139
CHAPTER 3 Product Costing and Cost Accumulation in a Batch Production EnvironmentANSWERS TO REVIEW QUESTIONS3-1 (a) Use in financial accounting: In financial accounting, product costs are needed to determine the value of inventory on the balance sheet a
東京大学 - BUSINESS A - 139
CHAPTER 4 Process Costing and Hybrid Product-Costing SystemsANSWERS TO REVIEW QUESTIONS4-1 In a job-order costing system, costs are assigned to batches or job orders of production. Job-order costing is used by firms that produce relatively small numbers
東京大学 - BUSINESS A - 139
CHAPTER 5 Activity-Based CostingANSWERS TO REVIEW QUESTIONS5-1 In a traditional, volume-based product-costing system, only a single predetermined overhead rate is used. All manufacturingoverhead costs are combined into one cost pool, and they are applie
東京大学 - BUSINESS A - 139
CHAPTER 6 Activity-Based Management and Cost Management ToolsANSWERS TO REVIEW QUESTIONS6-1 The two-dimensional activity-based costing model provides one way of picturing the relationship between ABC and ABM. The vertical dimension of the model depicts
東京大学 - BUSINESS A - 139
CHAPTER 7 Activity Analysis, Cost Behavior, and Cost EstimationANSWERS TO REVIEW QUESTIONS7-1 Cost behavior patterns are important in the process of making cost predictions. Cost predictions are used in planning, control, and decision making. For exampl
東京大学 - BUSINESS A - 139
Ch05 1 2 341 1. Determinants of Retailer Success 1. Location 2. Nature and quality of assortment 3. Price 4. Advertising and promotion 5. Sales personnel 6. Service offer
東京大学 - BUSINESS A - 139
CHAPTER 6 Post-Purchase Processes: Consumption and PostConsumption Evaluations : 12 Consumption Behavior 3 41Consumption BehaviorWhen does Consumption Occur? How much time passes between purchase and consumption? What time of day is produc
東京大学 - BUSINESS A - 139
Analyzing and Predicting Consumer Behavior CHAPTER 7Demographics Personality Values LifestylesDemographics, Psychographics, Values, and Personality 89 65 8.62% 7% =(65 /0-14 )*100 80 24.79%89 40.85% =(0-14 +65)/ 15-64 *100 80 48.96%89 42.32%
東京大学 - BUSINESS A - 139
Consumer Motivation CHAPTER 8Represents the drive to satisfy both physiological and psychological needs through product purchase and consumption Consumer Motivation Roger D. Blackwell, Paul W. Miniard, and James F. Engel, Consumer Behavior , Ninth Edi
東京大学 - BUSINESS A - 139
CH09 12 3 41 : Recall : which brands can be retrieved from memory Top-of-the-mind awareness : the particular brand that is remembered first (Product image): (symbols) (Image analysis):
東京大学 - BUSINESS A - 139
CHAPTER 12The Importance of Families and Households on Consumer Behavior Many products are purchased by a family unitFamily and Household Influences Individual buying decisions s may be heavily influenced by other family members Families and Househol
東京大学 - BUSINESS A - 139
CHAPTER 3The Consumer Decision ProcessRogerD.Blackwell,PaulW.Miniard,andJamesF.Engel,ConsumerBehavior,NinthEdition Copyright2001byHarcourt,Inc.Allrightsreserved.The Consumer Decision ProcessBlackwell,Miniard,andEngel,ConsumerBehavior,NinthEdition,Copy
東京大学 - BUSINESS A - 139
-- 1 * ) N (Item Data) (Work Center) (Routing)- MPS - Production(Bills Of Material)- MPS - Production2( hY p) hB)*Q+ MPS BOMs * MPS h )BQ+N)*( *)B h D@ B + D@ B + MPS * (JB +Resource3(Item Data) PathManufacturing/Item Control/Item Data/Ma
東京大学 - BUSINESS A - 139
Market Informationu:1Customers Sales FCST2uuuCustomer Data Sales Quotations RCCPuuuSales ContractBOMs Routing2.1 2.2Item Data uSales OrderMPS BOMs u MPS Routing MPSuDaily Production Order AA b CRP2.35 4Material RequisitionsWork Sta
東京大学 - BUSINESS A - 139
Market Informationu:1Customers Sales FCST2uuuCustomer Data Sales Quotations RCCPuuuSales ContractBOMs Routing2.1 2.2Item Data uSales OrderMPS BOMs u MPS Routing MPSuDaily Production Order ( b CRP2.35 4Material RequisitionsWork Stat
東京大学 - BUSINESS A - 139
LEARNING MANUFACTURING RESOURCES PLANNING INTRODUCTION Manufacturing resources planning (MRP II) involves various time-phased calculations such as master production scheduling (MPS), material requirement planning (MRP), lot sizing, and capacity requiremen
東京大学 - BUSINESS A - 139
ERP E User Account :- User name : group + E ex : group1 e group2 - password : group + E ex : group1 e group21ERP h f*2
東京大学 - BUSINESS A - 139
CHAPTER 9 Profit Planning, Activity-Based Budgeting, and e-BudgetingANSWERS TO REVIEW QUESTIONS9-1 A budget facilitates communication and coordination by making each manager throughout the organization aware of the plans made by other managers. The budg
東京大学 - BUSINESS A - 139
CHAPTER 10 Standard Costing and Performance Measures for Todays Manufacturing EnvironmentANSWERS TO REVIEW QUESTIONS10-1 Any control system has three basic parts: a predetermined or standard performance level, a measure of actual performance, and a comp
東京大学 - BUSINESS A - 139
東京大学 - BUSINESS A - 139
CHAPTER 12 Responsibility Accounting and Total Quality ManagementANSWERS TO REVIEW QUESTIONS12-1 Goal congruence results when the managers of subunits throughout an organization strive to achieve objectives that are consistent with the goals set by top
東京大学 - BUSINESS A - 139
$ A / @/^ $ $ * + * *^+(^+ * h @/^ + (^+ (^+ < ^+ ^+ ^+ p&( Q $ * ,^+ ,^+ p & ,^+ (^+ 4F *p4F p4F @ p4F $ 4F ^4 4 4 4F 4F 7 4F @ A / ^ + ^ + * * @ h k F * h k F *1 p> @/^ +
東京大学 - BUSINESS A - 139
2 *h 2 *h 2 *h(Secondary Data) *2* * * * *+ 2 +p2d 0 2 +p2d 0 2 +p2d 0 2 +p2d 0 2 +p2d 0 S * 2 8 * 2 8 2 8 * s " x p$M p$M s " *x X#M + * 2 8 2 8 * 2 8 ] 2 8 2 8 * 2 8 * 2 8 * 2 8 (single source service) * 2 8 S x2* x2*
東京大学 - BUSINESS A - 139
*x xSTP + *ug+g h p * * P @ 7 @E @ 7 @E P g+pU* `g+pU*h `* * + *ug+g h p * @ 7 @E g+*p `g+ @ @E7 @ 7 @E @ 7 @E + g h *ug+p * 0 0 ( (() )) )( 0 () STP * ` g+ * p @E 7@ STP @ @E @ @ @E @ @E % Kg+ g+ 7@ pEpE STP Por
東京大学 - BUSINESS A - 139
* @ f f f f f * * * * *' ' ' ' ' + + + ++ + + + + + 2* x2* x (FocusGroup) f * f * + ++ * @ K < E 7 @ ! 7 @ @ 7 @ @ + @ * s + @K<E @K<E X2 * `
東京大学 - BUSINESS A - 139
*X X (SurveyMethod) . .+ + % T *c P * + c . q [ P . % cT + .+ * P. % T c* + .+ * [.+ q * ` * 71 * B + . P *%T.+c [ .+B*q [ p .+ p D D X I I * * (ComputerAssistedTelephone Interview,CATI) PT . c +. * % @
東京大学 - BUSINESS A - 139
* * * E~ k p @kE&N @ yu E~ k p @kE&N @ @ @kE7 E~ k p @kE&N @ G k 7@ y k @ +jE *B+ , * ( *B+ , * (Sampling) + * * + * F 8* F 8 cfw_( u (cfw_u + p y + p + (samplingframe) E~ k p @kE%N @ H k 7@ @kE + + p* + p* + , , 8,x + p* , /
東京大学 - BUSINESS A - 139
東京大学 - BUSINESS A - 139
東京大学 - BUSINESS A - 139
東京大学 - BUSINESS A - 139
東京大学 - BUSINESS A - 139
東京大学 - BUSINESS A - 139
東京大学 - BUSINESS A - 139
東京大学 - BUSINESS A - 139