Background
The Glass Slipper restaurant has operated in a resort community near a popular ski area of New Mexico and busiest
during the first 3 months of the year. The Glass slipper offered the ultimate dining experience with breathtaking views of the
surrounding mountains. James and Deena Weltee, the owner, place special attention in setting the perfect ambiance making dining
a truly magnificent gourment experience. The Glass Slipper has developed and maintained a reputation as one of the "must visit"
places in that region of New Mexico.
Objective
After careful analysis of their financial condition, the Weltee's decided to sell the Glass Slipper and open a bed and
breakfast on a beautiful beach in Mexico. Although not retired yet, this would put them in the retirement setting they have been
longing for many years. They would have to hire a manager that would allow them to begin a semi-retirement life in paradise. The
Glass Slipper for the right price. The price of the business would be based on the value of the property and equipment, as well as
projections of future income. A forecast of sales for the next year is needed to help in the determination of the calue of the
restaurant. Monthly sales for each of the past 3 years are provided below.
Monthly Revenue (in $1,000s)
Month
January
February
March
April
May
June
July
August
September
October
November
December
###
436
419
414
318
306
240
240
216
202
225
270
315
##
###
###
###
###
###
###
###
###
###
###
###
###
###
454
439
434
338
331
254
264
231
220
243
289
330
12-Month Moving Average
Enter
Enter the
the past
past demands
demands in
in the
the data
data area
area
Forecasting
Num pds
Data
Month
Jan-08
Feb-08
Mar-08
Apr-08
May-08
Jun-08
Jul-08
Aug-08
Sep-08
Oct-08
Nov-08
Dec-08
Jan-09
Feb-09
Mar-09
Apr-09
May-09
Jun-09
Jul-09
Aug-09
Sep-09
Oct-09
Nov-09
Dec-09
Jan-10
Feb-10
Mar-10
Apr-10
May-10
Jun-10
Simple Linear Regression
12
Demand
438
420
414
318
306
240
240
216
198
225
270
315
444
425
423
331
318
245
255
223
210
233
278
322
450
438
434
338
331
254
Forecasts and Error Analysis
Forecast
Error
Absolute
Squared
Abs Pct Err
Forec
500
400
300
Value
200
100
0
300.000
300.500
300.917
301.667
302.750
303.750
304.167
305.417
306.000
307.000
307.667
308.333
308.917
309.417
310.500
311.417
312.000
313.083
144.000
124.500
122.083
29.333
15.250
-58.750
-49.167
-82.417
-96.000
-74.000
-29.667
13.667
141.083
128.583
123.500
26.583
19.000
-59.083
144.000
124.500
122.083
29.333
15.250
58.750
49.167
82.417
96.000
74.000
29.667
13.667
141.083
128.583
123.500
26.583
19.000
59.083
20736.000
15500.250
14904.340
860.444
232.563
3451.563
2417.361
6792.507
9216.000
5476.000
880.111
186.778
19904.507
16533.674
15252.250
706.674
361.000
3490.840
0.324
0.293
0.289
0.089
0.048
0.240
0.193
0.370
0.457
0.318
0.107
0.042
0.314
0.294
0.285
0.079
0.057
0.233
Tim
Demand
The lines above the "forecast line" illustrate their
the "forecast line" show their offseason. The gra
sales as each New Year begins. Basically, the up
issue and the "Gray" is performance.
Jul-10
Aug-10
Sep-10
Oct-10
Nov-10
264
231
224
243
289
313.833
314.583
315.250
316.417
317.250
-49.833
-83.583
-91.250
-73.417
-28.250
49.833
83.583
91.250
73.417
28.250
Dec-10
335
318.167
16.833
129.000
5.375
16.833
1679.833
69.993
Total
Average
Bias
MAD
SE
Next period
319.25
2483.361
6986.174
8326.563
5390.007
798.063
283.361
161170.389
6715.433
MSE
MAPE
85.592
0.189
0.362
0.407
0.302
0.098
0.050
5.437
0.227
Forecasting
Time
Demand
Forecast
orecast line" illustrate their busiest months, while the lines below
ow their offseason. The graph also shows moderate increase in
ar begins. Basically, the up and down is more of a seasonality
s performance.
Regression Analysis
Refression Analysis
Forecasting
Simple linear regression
Data
Forecasts and Error Analysis
Month
Demand (y)
Period(x)
Forecast
Error
IfIf this
this isis trend
trend analysis
analysis then
then simply
simply enter
enterthe
the past
pastdemands
demands in
in the
thedemand
demand column.
column. IfIfthis
thisis
is caus
caus
then
then enter
enter the
the y,x
y,x pairs
pairswith
withyyfirst
firstand
and enter
enter aanew
new value
valueof
ofxx at
at the
thebottom
bottomin
in order
order to
toforecast
forecast y
Absolute
Squared
Abs Pct Err
Jan-08
438
1
329.727
108.273
108.273
11723.102
0.247
Feb-08
420
2
328.565
91.435
91.435
8360.439
0.218
Mar-08
414
3
327.402
86.598
86.598
7499.144
0.209
Apr-08
318
4
326.240
-8.240
8.240
67.902
0.026
May-08
306
5
325.078
-19.078
19.078
363.973
0.062
Jun-08
240
6
323.916
-83.916
83.916
7041.881
0.350
Jul-08
240
7
322.754
-82.754
82.754
6848.184
0.345
Aug-08
216
8
321.592
-105.592
105.592
11149.584
0.489
Sep-08
198
9
320.429
-122.429
122.429
14988.965
0.618
Oct-08
225
10
319.267
-94.267
94.267
8886.318
0.419
Nov-08
270
11
318.105
-48.105
48.105
2314.101
0.178
Dec-08
315
12
316.943
-1.943
1.943
3.775
0.006
Jan-09
444
13
315.781
128.219
128.219
16440.168
0.289
Feb-09
425
14
314.619
110.381
110.381
12184.049
0.260
Mar-09
423
15
313.456
109.544
109.544
11999.788
0.259
Apr-09
331
16
312.294
18.706
18.706
349.903
0.057
May-09
318
17
311.132
6.868
6.868
47.168
0.022
Jun-09
245
18
309.970
-64.970
64.970
4221.097
0.265
Jul-09
255
19
308.808
-53.808
53.808
2895.280
0.211
Aug-09
223
20
307.646
-84.646
84.646
7164.885
0.380
Sep-09
210
21
306.483
-96.483
96.483
9309.063
0.459
Oct-09
233
22
305.321
-72.321
72.321
5230.374
0.310
Nov-09
278
23
304.159
-26.159
26.159
684.302
0.094
Dec-09
322
24
302.997
19.003
19.003
361.114
0.059
Jan-10
450
25
301.835
148.165
148.165
21952.916
0.329
Feb-10
438
26
300.673
137.327
137.327
18858.795
0.314
Mar-10
434
27
299.511
134.489
134.489
18087.423
0.310
Apr-10
338
28
298.348
39.652
39.652
1572.253
0.117
May-10
331
29
297.186
33.814
33.814
1143.374
0.102
Regre
500
400
300
200
100
0
0
5
10
15
Column B
The seasonality is consistent but the slope is not. W
positive performance trend line, the regression plot
raw data is found to be: Y = 330.889 - 1.162X
The Slope of the trend line is negative which would i
seasonal index in Jan and Feb causes the trend line
negative slope.
Jun-10
254
30
296.024
-42.024
42.024
1766.019
0.165
Jul-10
264
31
294.862
-30.862
30.862
952.455
0.117
Aug-10
231
32
293.700
-62.700
62.700
3931.252
0.271
Sep-10
224
33
292.538
-68.538
68.538
4697.394
0.306
Oct-10
243
34
291.375
-48.375
48.375
2340.177
0.199
Nov-10
289
35
290.213
-1.213
1.213
1.472
0.004
Dec-10
335
36
289.051
45.949
45.949
2111.306
0.137
Total
0.000
2436.847
227549.393
8.204
Average
0.000
67.690
6320.816
0.228
Intercept
Slope
330.889
-1.162
Bias
MAD
SE
Forecast
287.88888889
MSE
MAPE
81.808
37
Correlation
-0.150
Coefficient of determination
0.023
nd column.
and
column. IfIfthis
thisis
is causal
causalregression
regression
tom in
ttom
in order
order to
toforecast
forecast y.
y.
10
Regression
15
Column B
20
25
30
35
40
Linear (Column B)
but the slope is not. While the 12-month moving average plotted a
ne, the regression plotted a negative trend line. A trend line based on the
30.889 - 1.162X
negative which would indicate that sales are declining over time. The high
causes the trend line on the unadjusted data to appear to have a
Multiplicative Decomposition
Enter
Enter past
past demands
demands in
in the
the data
data area.
area. Do
Do not
not change
change the
the time
time period
period
numbers!
numbers!
Forecasting Decomposition, multiplicative
12 seasons
Data
Forecasts and Error Analysis
Month
Demand (y)
Time (x)
Average
Ratio
Seasonal
Smoothed
Unadjusted
Adjusted
Jan-08
438
1
309.389
1.416
1.435
305.208
295.930
424.685
Feb-08
420
2
309.389
1.358
1.382
303.843
296.699
410.125
Mar-08
414
3
309.389
1.338
1.369
302.330
297.468
407.343
Apr-08
318
4
309.389
1.028
1.063
299.045
298.237
317.141
May-08
306
5
309.389
0.989
1.029
297.402
299.006
307.651
Jun-08
240
6
309.389
0.776
0.796
301.434
299.775
238.679
Jul-08
240
7
309.389
0.776
0.818
293.491
300.544
245.767
Aug-08
216
8
309.389
0.698
0.722
299.230
301.313
217.504
Sep-08
198
9
309.389
0.640
0.681
290.786
302.083
205.692
Oct-08
225
10
309.389
0.727
0.755
297.914
302.852
228.729
Nov-08
270
11
309.389
0.873
0.902
299.409
303.621
273.798
Dec-08
315
12
309.389
1.018
1.047
300.795
304.390
318.765
Jan-09
444
13
309.389
1.435
1.435
309.389
305.159
437.930
Feb-09
425
14
309.389
1.374
1.382
307.460
305.928
422.883
Mar-09
423
15
309.389
1.367
1.369
308.902
306.697
419.981
Apr-09
331
16
309.389
1.070
1.063
311.270
307.466
326.955
May-09
318
17
309.389
1.028
1.029
309.065
308.235
317.146
Jun-09
245
18
309.389
0.792
0.796
307.714
309.004
246.027
Jul-09
255
19
309.389
0.824
0.818
311.835
309.773
253.314
Aug-09
223
20
309.389
0.721
0.722
308.927
310.543
224.166
Sep-09
210
21
309.389
0.679
0.681
308.410
311.312
211.976
Oct-09
233
22
309.389
0.753
0.755
308.506
312.081
235.700
Nov-09
278
23
309.389
0.899
0.902
308.280
312.850
282.121
Dec-09
322
24
309.389
1.041
1.047
307.479
313.619
328.430
Jan-10
450
25
309.389
1.454
1.435
313.570
314.388
451.174
Feb-10
438
26
309.389
1.416
1.382
316.864
315.157
435.640
Mar-10
434
27
309.389
1.403
1.369
316.935
315.926
432.619
Apr-10
338
28
309.389
1.092
1.063
317.852
316.695
336.769
May-10
331
29
309.389
1.070
1.029
321.700
317.464
326.642
Jun-10
254
30
309.389
0.821
0.796
319.018
318.233
253.375
Jul-10
264
31
309.389
0.853
0.818
322.841
319.002
260.861
Aug-10
231
32
309.389
0.747
0.722
320.010
319.772
230.828
Sep-10
224
33
309.389
0.724
0.681
328.970
320.541
218.260
Oct-10
243
34
309.389
0.785
0.755
321.747
321.310
242.670
Nov-10
289
35
309.389
0.934
0.902
320.478
322.079
290.443
Dec-10
335
36
309.389
1.083
1.047
319.893
322.848
338.095
Total
Average
Intercept
295.161
Slope
0.769
Ratios
Season 1
Season 2
Season 3
Season 4
Season 5
Season 6
Season 7
Season 8
Season 9
1.416
1.358
1.338
1.028
0.989
0.776
0.776
0.698
0.640
1.435
1.374
1.367
1.070
1.028
0.792
0.824
0.721
0.679
1.454
1.416
1.403
1.092
1.070
0.821
0.853
0.747
0.724
1.435
1.382
1.369
1.063
1.029
0.796
0.818
0.722
0.681
Unadjusted
Seasonal
Adjusted
37
323.617
1.435
464.419
38
324.386
1.382
448.397
39
325.155
1.369
445.256
40
325.924
1.063
346.583
41
326.693
1.029
336.138
42
327.462
0.796
260.723
350
43
328.232
0.818
268.408
300
44
329.001
0.722
237.490
250
45
329.770
0.681
224.544
46
330.539
0.755
249.640
47
331.308
0.902
298.766
48
332.077
1.047
347.760
Average
Forecasts
Period
Forecasts
500
450
400
200
150
100
50
Forecasted sales for each month of the next year. The
gives the seasonal indices, the unadjusted forecasts
found using the trend line, and the final (adjusted)
forecasts for the next year
0
1
2
3
4
Period
5
6
Unadjusted
7
8
Seasonal
9
10
Adjusted
Forecasts and Error Analysis
Error
|Error|
Error^2
Abs Pct Err
13.315
13.315
177.285
0.030
9.875
9.875
97.507
0.024
6.657
6.657
44.320
0.016
0.859
0.859
0.737
0.003
-1.651
1.651
2.724
0.005
1.321
1.321
1.745
0.006
-5.767
5.767
33.264
0.024
-1.504
1.504
2.262
0.007
-7.692
7.692
59.162
0.039
-3.729
3.729
13.908
0.017
-3.798
3.798
14.428
0.014
-3.765
3.765
14.174
0.012
6.070
6.070
36.850
0.014
2.117
2.117
4.483
0.005
3.019
3.019
9.117
0.007
4.045
4.045
16.359
0.012
0.854
0.854
0.729
0.003
-1.027
1.027
1.055
0.004
1.686
1.686
2.841
0.007
-1.166
1.166
1.360
0.005
-1.976
1.976
3.904
0.009
-2.700
2.700
7.288
0.012
-4.121
4.121
16.982
0.015
-6.430
6.430
41.342
0.020
-1.174
1.174
1.378
0.003
2.360
2.360
5.570
0.005
1.381
1.381
1.908
0.003
1.231
1.231
1.514
0.004
4.358
4.358
18.990
0.013
0.625
0.625
0.390
0.002
3.139
3.139
9.851
0.012
0.172
0.172
0.030
0.001
5.740
5.740
32.947
0.026
0.330
0.330
0.109
0.001
-1.443
1.443
2.084
0.005
-3.095
3.095
9.577
0.009
18.114
120.190
688.175
0.393
0.503
3.339
19.116
0.011
Bias
MAD
MSE
MAPE
SE
5.593
Season 10
Season 11
Season 12
0.727
0.873
1.018
0.753
0.899
1.041
0.785
0.934
1.083
0.755
0.902
1.047
recasts
6
ted
7
8
Seasonal
9
10
Adjusted
11
12
Forecasting
12 seasons
Data
Period
Decomposition, multiplicative
Enter
Enter past
past demands
demands in
in the
the data
data area.
area. Do
Do not
not change
change the
the time
time
period
period numbers!
numbers!
Demand (y) Time (x)
Jan-08
438
Feb-08
420
Mar-08
414
Apr-08
318
May-08
306
Jun-08
240
Jul-08
240
Aug-08
216
Sep-08
198
Oct-08
225
Nov-08
270
Dec-08
315
Jan-09
444
Feb-09
425
Mar-09
423
Apr-09
331
May-09
318
Jun-09
245
Jul-09
255
Aug-09
223
Sep-09
210
Oct-09
233
Nov-09
278
Dec-09
322
Jan-10
450
Feb-10
438
Mar-10
434
Apr-10
338
May-10
331
Jun-10
254
Jul-10
264
Aug-10
231
Sep-10
224
Oct-10
243
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
Average Ratio
300
300.5
300.9167
301.6667
302.75
303.75
304.1667
305.4167
306
307
307.6667
308.3333
308.9167
309.4167
310.5
311.4167
312
313.0833
313.8333
314.5833
315.25
316.4167
317.25
318.1667
319.25
300.25
300.7083
301.2917
302.2083
303.25
303.9583
304.7917
305.7083
306.5
307.3333
308
308.625
309.1667
309.9583
310.9583
311.7083
312.5417
313.4583
314.2083
314.9167
315.8333
316.8333
317.7083
318.7083
Seasonal SmoothedUnadjuste
1.444452 303.2292 295.3847
1.390529 302.0433 296.2451
1.37712 300.6274 297.1056
1.071907 296.6676 297.966
1.037152 295.0388 298.8265
0.795405 301.733 299.687
0.799334 0.812066 295.5425 300.5474
0.718304 0.718878 300.4683 301.4079
0.657171 0.666251 297.1853 302.2684
0.74452 0.746007 301.6059 303.1288
0.890354 0.889918 303.3988 303.9893
1.036326 1.031788 305.2953 304.8497
1.456733 1.444452 307.3831 305.7102
1.390214 1.390529 305.6391 306.5707
1.380098 1.37712 307.1627 307.4311
1.077007 1.071907 308.7955 308.2916
1.032468 1.037152 306.6089 309.1521
0.793844 0.795405 308.0191 310.0125
0.824798 0.812066 314.0139 310.873
0.719452 0.718878 310.2057 311.7334
0.675332 0.666251 315.1965 312.5939
0.747494 0.746007 312.3297 313.4544
0.889481 0.889918 312.3884 314.3148
1.02725 1.031788 312.0796 315.1753
1.432171 1.444452 311.5369 316.0358
1.390844 1.390529 314.988 316.8962
1.374142 1.37712 315.1504 317.7567
1.066807 1.071907 315.3259 318.6171
1.041836 1.037152 319.1433 319.4776
0.796967 0.795405 319.3341 320.3381
0.812066 325.0968 321.1985
0.718878 321.3342 322.059
0.666251 336.2096 322.9195
0.746007 325.7344 323.7799
Nov-10
289
35
0.889918
Dec-10
335
36
1.031788 324.6791 325.5008
Average
324.749 324.6404
Intercept 294.5242
Slope
0.860463
Ratios
Season 1
Average
Season 2 Season 3 Season 4 Season 5 Season 6 Season 7 Season 8
0.799334 0.718304
1.4567327 1.390214 1.380098 1.077007 1.032468 0.793844 0.824798 0.719452
1.4321708 1.390844 1.374142 1.066807 1.041836 0.796967
1.4444518 1.390529 1.37712 1.071907 1.037152 0.795405 0.812066 0.718878
Forecasts
Period
Unadjusted Seasonal Adjusted
37 326.36131 1.444452 471.4132
38 327.22177 1.390529 455.0114
39 328.08224 1.37712 451.8087
40 328.9427 1.071907 352.5959
41 329.80316 1.037152 342.0559
42 330.66362 0.795405 263.0116
43 331.52409 0.812066 269.2194
44 332.38455 0.718878 238.9439
45 333.24501 0.666251 222.0248
46 334.10547 0.746007 249.2449
47 334.96594 0.889918 298.0922
48 335.8264 1.031788 346.5017
Forecasts and Error Analysis
Adjusted Error
|Error| Error^2 Abs Pct Err
426.6689 11.3311 11.3311 128.3939
02.59%
411.9375 8.062548 8.062548 65.00468
01.92%
409.1501 4.849903 4.849903 23.52156
01.17%
319.3918 -1.39181 1.39181 1.937136
00.44%
309.9285 -3.92845 3.928453 15.43274
01.28%
238.3726 1.627396 1.627396 2.648417
00.68%
244.0643 -4.06431 4.064313 16.51864
01.69%
216.6754 -0.67544 0.675438 0.456216
00.31%
201.3866 -3.38662 3.38662 11.4692
01.71%
226.1361 -1.1361 1.136096 1.290715
00.50%
270.5255 -0.52552 0.525521 0.276172
00.19%
314.5403 0.459686 0.459686 0.211311
00.15%
441.5837 2.416346 2.416346 5.838726
00.54%
426.2954 -1.29543 1.29543 1.678139
00.30%
423.3696 -0.36962 0.36962 0.136619
00.09%
330.4598 0.540163 0.540163 0.291776
00.16%
320.6376 -2.63762 2.637616 6.957016
00.83%
246.5856 -1.5856 1.585601 2.514132
00.65%
252.4493 2.55066 2.55066 6.505866
01.00%
224.0982 -1.09825 1.098246 1.206145
00.49%
208.266 1.733971 1.733971 3.006655
00.83%
233.839 -0.83902 0.839025 0.703962
00.36%
279.7144 -1.71441 1.714413 2.939213
00.62%
325.1941 -3.19409 3.194093 10.20223
00.99%
456.4984 -6.49841 6.498414 42.22938
01.44%
440.6534 -2.65341 2.653407 7.040571
00.61%
437.5891 -3.58914 3.589144 12.88195
00.83%
341.5279 -3.52786 3.527864 12.44582
01.04%
331.3468 -0.34678 0.346779 0.120255
00.10%
254.7986 -0.7986 0.798599 0.63776
00.31%
260.8344 3.165633 3.165633 10.02123
01.20%
231.5211 -0.52105 0.521055 0.271498
00.23%
215.1454 8.854562 8.854562 78.40327
03.95%
241.542 1.458047 1.458047 2.125901
00.60%
288.9033 0.096694 0.096694
0.00935
00.03%
335.8479 -0.84787 0.847872 0.718887
00.25%
Total
0.521283 93.77214 476.0471
30.11%
0.01448 2.604782 13.22353
00.84%
Bias
MAD
MSE
MAPE
SE
4.651721
Season 9 Season 10Season 11Season 12
0.657171 0.74452 0.890354 1.036326
0.675332 0.747494 0.889481 1.02725
0.666251 0.746007 0.889918 1.031788
Forecasting
Trend adjusted exponential smoothing
Enter
Enter alpha
alpha and
and beta
beta (between
(between 00 and
and 1),
1), enter
enter the
the past
past demands
demands in
in the
the shaded
shaded column
column then
then enter
enter
aa starting
starting forecast.
forecast. IfIf the
the starting
starting forecast
forecast isis not
not in
in the
the first
first period
period then
then delete
delete the
the error
error analysis
analysis for
for
all
all rows
rows above
above the
the starting
starting forecast.
forecast.
Alpha
Beta
Data
Period
Period 1
Period 2
Period 3
Period 4
Period 5
Period 6
Period 7
Forecasts and Error Analysis
Demand
Period 8
Next period
Smoothed
Forecast
Forecast, Smoothed Including
Ft
Trend, Tt Trend, FITt Error
0
0
0
0
0
0
0
0
0
0
0
0
0
Absolute Squared
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
Total
Average
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
Bias
MAD
SE
MSE
0
Abs Pct
Err
#DIV/0!
Forecasting
1
#DIV/0!
#DIV/0!
MAPE
0.9
0.8
0.7
0.6
Value
0.5
0.4
0.3
0.2
0.1
0
1
2
3
4
5
6
Time
Demand
Smoothed Forecast, Ft
7
8
KATE WALSH ASSOCIATES
Forecasting
Exponential smoothing
Enter
Enter alpha
alpha (between
(between 00 and
and 1),
1), enter
enter the
the past
past demands
demands in
in the
the shaded
shaded column
column then
then enter
enter aa starting
starting
forecast.
forecast. IfIf the
the starting
starting forecast
forecast isis not
not in
in the
the first
first period
period then
then delete
delete the
the error
error analysis
analysis for
for all
all rows
rows
above
above the
the starting
starting forecast.
forecast.
Alpha
Data
Period
Period 1
Period 2
Period 3
Period 4
Period 5
Period 6
0.1
Demand
70
68.5
64.8
71.7
71.3
72.8
Forecasts and Error Analysis
Forecast Error
Absolute Squared Abs Pct Err
65
5
5
25
07.14%
65.5
3
3
9
04.38%
65.8
-1
1
1
01.54%
65.7
6
6
36
08.37%
66.3
5
5
25
07.01%
66.8
Total
Average
6
6
24
26
4 4.333333
Bias
MAD
36 0.0824175824
132
36.69%
22
06.11%
MSE MAPE
SE 5.744563
Next period
DISCUSSION
67.4
5.29: Using exponential smoothing forecast for August's income is $67,400.
Forecasting
74
72
70
68
Value
66
64
62
60
1
2
3
Time
70
65
4
5
KATE WALSH ASSOCIATES
Forecasting
Alpha
Data
Period
MONTH 1
MONTH 2
MONTH 3
MONTH 4
MONTH 5
MONTH 6
Exponential smoothing
0.3
Demand
70
68.5
64.8
71.7
71.3
72.8
Forecasts and Error Analysis
Forecast Error
Absolute Squared Abs Pct Err
65
5
5
25
07.14%
66.5
2
2
4
02.92%
67.1
-2.3
2.3
5.29
03.55%
66.41
5.29
5.29 27.9841
07.38%
67.997
3.303
3.303 10.90981
04.63%
68.9879
Total
Average
3.8121
3.8121 14.53211 0.052364011
17.1051 21.7051 87.71602
30.86%
2.85085 3.617517 14.61934
05.14%
Bias
MAD
MSE MAPE
SE 4.682841
Next period
DISCUSSION
70.13153
5.30: Using alpha of 0.1 ,MAD value is 4.333 while using alpha of 0.3 ,MAD value is 3.618. Based
on this using alpha of 0.3 provides a better forecast since it has a lower MAD value.
Enter
Enter alpha
alpha (between
(between 00 an
a
forecast.
forecast. IfIf the
the starting
starting fore
for
above
above the
the starting
starting forecas
forecas
Forecasting
74
72
70
68
Value
66
64
62
60
1
2
3
Time
70
65
4
5
Enter
Enter alpha
alpha (between
(between 00 and
and 1),
1), enter
enter the
the past
past demands
demands in
in the
the shaded
shaded column
column then
then enter
enter aa starting
starting
forecast.
forecast. IfIf the
the starting
starting forecast
forecast is
is not
not in
in the
the first
first period
period then
then delete
delete the
the error
error analysis
analysis for
for all
all rows
rows
above
above the
the starting
starting forecast.
forecast.