DeskJ et Dem Demand and Data Data from Europe
Europe Options A AB AU
NOV 80
DEC
JAN
-
FEB
MAR
APR
MAY
60
90
21
48
-
20,572
20,895
19,252
11,052
19,864
20,316
4,564
3,207
7,485
4,908
5,295
90
-
AA
400
255
408
645
210
87
432
AQ
4,008
2,196
4,761
1,953
1,008
2,358
1,676
AY
248
450
378
306
219
204
248
29,872
27,003
32,344
18,954
26,617
23,103
15,692
TOTAL
13,336
By calculating the co-ef of variance, the degree of variance of the demand with r determined. The models with less co-ef of v ariance are comparitively having st others.
Europe Options A AB AU
NOV 80
DEC
JAN
-
FEB
MAR
APR
MAY
60
90
21
48
-
20,572
20,895
19,252
11,052
19,864
20,316
4,564
3,207
7,485
4,908
5,295
90
-
AA
400
255
408
645
210
87
432
AQ
4,008
2,196
4,761
1,953
1,008
2,358
1,676
AY
248
450
378
306
219
204
248
29,872
27,003
32,344
18,954
26,617
23,103
15,692
TOTAL
13,336
From the above caculated probablities, it is cle ar that with the existing system the chances out are high for all the models. The need for more saftey stock is the re.
1) Considering the probability as 98% for all the models, we will now find out the safety st requirements. Europe Options A AB AU
NOV 80
DEC -
JAN
FEB
MAR
APR
MAY
60
90
21
48
-
20,572
20,895
19,252
11,052
19,864
20,316
13,336
4,564
3,207
7,485
4,908
5,295
90
-
AA
400
255
408
645
210
87
432
AQ
4,008
2,196
4,761
1,953
1,008
2,358
1,676
AY
248
450
378
306
219
204
248
2) Considering the probability as 98% for models with high co-ef of variance( AU,AQ,AA) a models with low co-ef of variance (A,AY,AB).
Europe Options A AB AU
NOV 80
DEC
JAN
-
FEB
MAR
APR
MAY
60
90
21
48
-
20,572
20,895
19,252
11,052
19,864
20,316
13,336
4,564
3,207
7,485
4,908
5,295
90
-
AA
400
255
408
645
210
87
432
AQ
4,008
2,196
4,761
1,953
1,008
2,358
1,676
AY
248
450
378
306
219
204
248
3) If localization is done at the European DC, then generic printers can be kept as inventor be distributued. Considering the variation in overall demand, co-ef of variance for the total 0.27 from the first table. So, lets assume 85% as the probability of stock out.
Europe Options A AB AU
NOV 80
DEC -
JAN
FEB
MAR
APR
MAY
60
90
21
48
-
20,572
20,895
19,252
11,052
19,864
20,316
4,564
3,207
7,485
4,908
5,295
90
-
AA
400
255
408
645
210
87
432
AQ
4,008
2,196
4,761
1,953
1,008
2,358
1,676
AY
248
450
378
306
219
204
248
29,872
27,003
32,344
18,954
26,617
23,103
15,692
TOTAL
13,336
JUN
JUL
AUG
SEP
OCT
9
20
54
84
42
10,578
6,096
14,496
23,712
9,792
5,004
4,385
5,103
4,302
6,153
816
430
630
456
273
540
2,310
2,046
1,797
2,961
484
164
363
384
234
17,431
13,405
22,692
30,735
19,455
espect to mean is ble demand than the
Mean
Std. Dev 42 15830 4208 420 2301 307 23109
32.4 5624.6 2204.6 203.9 1168.5 103.1 6244.0
In the existing model, the target inventory level is equal to the mean. So, w When we compare the obtained R w ith the SS included, then the Z value c
JUN
JUL
AUG
SEP
OCT
9
20
54
84
42
10,578
6,096
14,496
23,712
9,792
5,004
4,385
5,103
4,302
6,153
816
430
630
456
273
540
2,310
2,046
1,797
2,961
484
164
363
384
234
17,431
13,405
22,692
30,735
19,455
for stock
Mean
Std. Dev 42 15830 4208 420 2301 307 23109
32.4 5624.6 2204.6 203.9 1168.5 103.1 6244.0
Now as we now that there are high chances of stock out, we will find out the s the FIXED-QUANTITY MODEL.
ock
JUN
JUL
AUG
SEP
OCT
9
20
54
84
42
10,578
6,096
14,496
23,712
9,792
5,004
4,385
5,103
4,302
6,153
816
430
630
456
273
540
2,310
2,046
1,797
2,961
Mean
Std. Dev 42 15830 4208 420 2301
32.4 5624.6 2204.6 203.9 1168.5
484
164
363
384
307
234
103.1
nd 80% for
JUN
JUL
AUG
SEP
OCT
9
20
54
84
42
10,578
6,096
14,496
23,712
9,792
5,004
4,385
5,103
4,302
6,153
816
430
630
456
273
540
2,310
2,046
1,797
2,961
484
164
363
384
234
Mean
Std. Dev 42 15830 4208 420 2301 307
32.4 5624.6 2204.6 203.9 1168.5 103.1
y and can demand is
JUN
JUL
AUG
SEP
OCT
9
20
54
84
42
10,578
6,096
14,496
23,712
9,792
5,004
4,385
5,103
4,302
6,153
816
430
630
456
273
540
2,310
2,046
1,797
2,961
484
164
363
384
234
17,431
13,405
22,692
30,735
19,455
Mean Std. Dev co-ef of variance std.dev for lead time R SS Average Inventory Holding cost
23109 6244.0 0.270196 7350.076 42160.58 7497.077 24828.83 1551802
co-ef of variance
std.dev for lead time
0.771739378 0.355311346 0.523902832 0.485540069 0.507819794 0.335905225 0.270196034
38.15501494 6620.97072 2595.124242 240.0523947 1375.491529 121.3910901 7350.0755
e can calculate the Z values of respective models. R=d*L . n be found out.
Z=(R-d*L)/std.dev for L
probability G(Z)
-0.647887116 -1.407216533 -0.954375447 -1.029781129 -0.984601243 -1.488515102 -1.850508285
26% 8% 17.10% 15.50% 16.20% 6% 3%
aftey stocks with different prbabiilties using
std.dev for lead time 38.15501494 6620.97072 2595.124242 240.0523947 1375.491529
R 141.2177806 37317.98998 11632.0047 1122.107409 6271.257635
SS 78.21778 13572.99 5320.005 492.1074 2819.758
Average Inventory 109.7177806 25445.48998 8476.004696 807.1074092 4545.507635
121.3910901
709.3517348 248.8517
479.1017348 Total cost
std.dev for lead time 38.15501494 6620.97072 2595.124242 240.0523947 1375.491529 121.3910901
R 95.4317627 29372.82511 11632.0047 1122.107409 6271.257635 563.6824266
SS
Average Inventory
32.43176 5627.825 5320.005 492.1074 2819.758 103.1824
63.9317627 17500.32511 8476.004696 807.1074092 4545.507635 333.4324266 Total cost
Holding Cost 6857.361289 1590343.124 529750.2935 50444.21307 284094.2272
29943.85842 2491433.077
Holding Cost 3995.735169 1093770.32 529750.2935 50444.21307 284094.2272 20839.52666 1982894.315
