Analisis Pengendalian Persediaan Bahan Baku dalam Meminimumkan Biaya Persediaan dengan Metode EOQ (Studi Kasus: PT. Sinar Sosro Medan)

52

Lampiran 1. Tabel Z

Universitas Sumatera Utara

53

Universitas Sumatera Utara

54

Lampiran 3 : Tabel Z

Universitas Sumatera Utara

55

Universitas Sumatera Utara

56


Lampiran 4. Tabel D
Kolmogorov-Smirnov Test
(If calculated ratio is greater than value shown, then reject the null hypothesis at the
chosen level of confidence.)
SAMPLE
SIZE
(N)
1
2
3
4
5
6
7
8
9
10
11
12

13
14
15
16
17
18
19
20
25
30
35
OVER 35

LEVEL OF SIGNIFICANCE FOR D
F0(X) - Sn(X) ]
.20
.15
.10
.900
.925

.950
.684
.726
.776
.565
.597
.642
.494
.525
.564
.446
.474
.510
.410
.436
.470
.381
.405
.438
.358

.381
.411
.339
.360
.388
.322
.342
.368
.307
.326
.352
.295
.313
.338
.284
.302
.325
.274
.292
.314

.266
.283
.304
.258
.274
.295
.250
.266
.286
.244
.259
.278
.237
.252
.272
.231
.246
.264
.210
.220

.240
.190
.200
.220
.180
.190
.210
1.07
1.14
1.22
___
___
___
N
N
N

= MAXIMUM [
.05
.975

.842
.708
.624
.565
.521
.486
.457
.432
.410
.391
.375
.361
.349
.338
.328
.318
.309
.301
.294
.270

.240
.230
1.36
___
N

.01
.995
.929
.828
.733
.669
.618
.577
.543
.514
.490
.468
.450
.433

.418
.404
.392
.381
.371
.363
.356
.320
.290
.270
1.63
___
N

Lampiran 5. Tabel perbandingan MAPE, MSE dan MAD

Universitas Sumatera Utara

57


Trend Analysis Plot for NILAI
Linear Trend Model
Yt = 314541 - 534,692*t
Variable
A ctual
Fits
Forecasts

350000

A ccuracy Measures
MA PE
10
MA D
28725
MSD
1064231186

NILAI


325000

300000

275000

250000
1

5

10

15

20

25
Index

30

35

40

45

Trend Analysis for NILAI
Data
Length
NMissing

NILAI
36
0

Fitted Trend Equation
Yt = 314541 - 534,692*t

Accuracy Measures
MAPE
MAD
MSE

10
28725
1064231186

Forecasts
Period
37
38
39
40
41
42
43
44
45
46
47
48

Forecast
294757
294222
293688
293153
292618
292083
291549
291014
290479
289945
289410
288875

Universitas Sumatera Utara

58

Trend Analysis Plot for NILAI
Quadratic Trend Model
Yt = 323496 - 1949*t + 38,2*t**2
Variable
A ctual
Fits
Forecasts

350000

A ccuracy Measures
MA PE
10
MA D
28763
MSD
1050655176

NILAI

325000

300000

275000

250000
1

5

10

15

20

25
Index

30

35

40

45

Trend Analysis for NILAI
Data
Length
NMissing

NILAI
36
0

Fitted Trend Equation
Yt = 323496 - 1949*t + 38,2*t**2

Accuracy Measures
MAPE
MAD
MSE

10
28763
1050655176

Forecasts
Period
37
38
39
40
41
42
43
44
45
46
47
48

Forecast
303712
304630
305624
306694
307841
309065
310364
311740
313193
314722
316328
318009

Universitas Sumatera Utara

59

DEKOMPOSISI
Additive Model
Variable
A ctual
Fits
Trend
Forecasts

350000

NILAI

325000

A ccuracy Measures
MA PE
4
MA D
12165
MSD
322028275

300000

275000

250000

1

5

10

15

20

25
Index

30

35

40

45

Time Series Decomposition for NILAI
Additive Model

Data
Length
NMissing

NILAI
36
0

Fitted Trend Equation
Yt = 314438 - 529,141*t

Seasonal Indices
Period
1
2
3
4
5
6
7
8
9
10
11
12

Index
-2760,5
-38515,1
32110,1
-35043,8
29764,4
28829,9
36848,1
-51213,6
10210,0
17471,2
-12224,4
-15476,3

Accuracy Measures
MAPE
MAD
MSE

4
12165
322028275

Universitas Sumatera Utara

60

Forecasts
Period
37
38
39
40
41
42
43
44
45
46
47
48

Forecast
292099
255815
325911
258228
322508
321044
328533
239942
300837
307569
277344
273563

DEKOMPOSISI
Multiplicative Model
Variable
A ctual
Fits
Trend
Forecasts

350000

NILAI

325000

A ccuracy Measures
MA PE
4
MA D
12003
MSD
325280227

300000

275000

250000

1

5

10

15

20

25
Index

30

35

40

45

Time Series Decomposition for NILAI
Multiplicative Model

Data
Length
NMissing

NILAI
36
0

Fitted Trend Equation
Yt = 315191 - 556,659*t

Seasonal Indices
Period
1
2
3

Index
0,99052
0,87035
1,10726

Universitas Sumatera Utara

61

4
5
6
7
8
9
10
11
12

0,88299
1,09909
1,09643
1,12195
0,83183
1,03469
1,05766
0,95897
0,94826

Accuracy Measures
MAPE
MAD
MSE

4
12003
325280227

Forecasts
Period
37
38
39
40
41
42
43
44
45
46
47
48

Forecast
291801
255917
324960
258649
321338
319951
326773
241810
300205
306281
277169
273547

Moving Average Plot for NILAI
375000

Variable
A ctual
Fits
Forecasts
95,0% PI

350000

Mov ing A v erage
Length 12

NILAI

325000

A ccuracy Measures
MA PE
10
MA D
29052
MSD
1119107155

300000

275000

250000

1

5

10

15

20

25
Index

30

35

40

45

Moving Average for NILAI
Data

NILAI

Universitas Sumatera Utara

62

Length
NMissing

36
0

Moving Average
Length

12

Accuracy Measures
MAPE
MAD
MSE

10
29052
1119107155

Forecasts
Period
37
38
39
40
41
42
43
44
45
46
47
48

Forecast
302073
302073
302073
302073
302073
302073
302073
302073
302073
302073
302073
302073

Lower
236507
236507
236507
236507
236507
236507
236507
236507
236507
236507
236507
236507

Upper
367640
367640
367640
367640
367640
367640
367640
367640
367640
367640
367640
367640

Smoothing Plot for NILAI
Single Exponential Method
380000

Variable
A ctual
Fits
Forecasts
95,0% PI

360000
340000

Smoothing Constant
A lpha
0,0401788

NILAI

320000

A ccuracy Measures
MA PE
10
MA D
29356
MSD
1140365919

300000
280000
260000
240000
220000
1

5

10

15

20

25
Index

30

35

40

45

Single Exponential Smoothing for NILAI

Universitas Sumatera Utara

63

Data
Length

NILAI
36

Smoothing Constant
Alpha

0,0401788

Accuracy Measures
MAPE
MAD
MSE

10
29356
1140365919

Forecasts
Period
37
38
39
40
41
42
43
44
45
46
47
48

Forecast
302337
302337
302337
302337
302337
302337
302337
302337
302337
302337
302337
302337

Lower
230415
230415
230415
230415
230415
230415
230415
230415
230415
230415
230415
230415

Upper
374258
374258
374258
374258
374258
374258
374258
374258
374258
374258
374258
374258

Smoothing Plot for NILAI
Double Exponential Method
500000

Variable
A ctual
Fits
Forecasts
95,0% PI

NILAI

400000

Smoothing Constants
A lpha (lev el)
0,425018
Gamma (trend)
0,075163

300000

A ccuracy Measures
MA PE
11
MA D
31726
MSD
1588100761

200000

100000
1

5

10

15

20

25 30
Index

35

40

45

Double Exponential Smoothing for NILAI

Data
Length

NILAI
36

Universitas Sumatera Utara

64

Smoothing Constants
Alpha (level)
Gamma (trend)

0,425018
0,075163

Accuracy Measures
MAPE
MAD
MSE

11
31726
1588100761

Forecasts
Period
37
38
39
40
41
42
43
44
45
46
47
48

Forecast
287015
285698
284380
283062
281745
280427
279109
277792
276474
275156
273839
272521

Lower
209288
200477
190875
180675
170018
159011
147730
136234
124566
112759
100837
88821

Upper
364743
370919
377885
385450
393471
401843
410488
419349
428382
437554
446840
456221

Winters' Method Plot for NILAI
Multiplicative Method

NILAI

400000

350000

Variable
A ctual
Fits
Forecasts
95,0% PI

300000

Smoothing Constants
A lpha (lev el)
0,2
Gamma (trend)
0,2
Delta (seasonal)
0,2
A ccuracy Measures
MA PE
5
MA D
15788
MSD
367019059

250000

200000
1

5

10

15

20

25
30
Index

35

40

45

Winters' Method for NILAI
Multiplicative Method

Data

NILAI

Universitas Sumatera Utara

65

Length

36

Smoothing Constants
Alpha (level)
Gamma (trend)
Delta (seasonal)

0,2
0,2
0,2

Accuracy Measures
MAPE
MAD
MSE

5
15788
367019059

Forecasts
Period
37
38
39
40
41
42
43
44
45
46
47
48

Forecast
294487
261484
336734
288042
335006
333013
336735
257031
325670
317660
285978
279725

Lower
255807
222198
296771
247338
293497
290640
293443
212768
280387
271311
238521
231120

Upper
333168
300771
376696
328746
376515
375386
380027
301294
370953
364009
333435
328330

Universitas Sumatera Utara