I need help with the solution to the Chapter 1 Performance Lawn Equipment case s
ID: 449580 • Letter: I
Question
I need help with the solution to the Chapter 1 Performance Lawn Equipment case study in Business Analytics text by James R. Evans. The problem statement is:
"An important part of planning manufacturing capacity is having a good forecast of sales. Elizabeth Burke is interested in forecasting sales of mowers in NA, Eur and Pacific regions as well as industry mower sales to assess future changes in market share. She also wants to forecast future increases in market share. She also wants to forecast future increases in production costs. Develop forecasting models for these data and prepare a formal report of your result with appropriate charts and outputs.
Can you assist me with how to go about figuring 1st, what model to use and the initial steps needed to solve these problem.
This is the information from the mower unit sales:
Month
NA
SA
Europe
Pacific
China
World
Jan-10
6000
200
720
100
0
7020
Feb-10
7950
220
990
120
0
9280
Mar-10
8100
250
1320
110
0
9780
Apr-10
9050
280
1650
120
0
11100
May-10
9900
310
1590
130
0
11930
Jun-10
10200
300
1620
120
0
12240
Jul-10
8730
280
1590
140
0
10740
Aug-10
8140
250
1560
130
0
10080
Sep-10
6480
230
1590
130
0
8430
Oct-10
5990
220
1320
120
0
7650
Nov-10
5320
210
990
130
0
6650
Dec-10
4640
180
660
140
0
5620
Jan-11
5980
210
690
140
0
7020
Feb-11
7620
240
1020
150
0
9030
Mar-11
8370
250
1290
140
0
10050
Apr-11
8830
290
1620
150
0
10890
May-11
9310
330
1650
130
0
11420
Jun-11
10230
310
1590
140
0
12270
Jul-11
8720
290
1560
150
0
10720
Aug-11
7710
270
1530
140
0
9650
Sep-11
6320
250
1590
150
0
8310
Oct-11
5840
250
1260
160
0
7510
Nov-11
4960
240
900
150
0
6250
Dec-11
4350
210
660
150
0
5370
Jan-12
6020
220
570
160
0
6970
Feb-12
7920
250
840
150
0
9160
Mar-12
8430
270
1110
160
0
9970
Apr-12
9040
310
1500
170
0
11020
May-12
9820
360
1440
160
0
11780
Jun-12
10370
330
1410
170
0
12280
Jul-12
9050
310
1440
160
0
10960
Aug-12
7620
300
1410
170
0
9500
Sep-12
6420
280
1350
180
0
8230
Oct-12
5890
270
1080
180
0
7420
Nov-12
5340
260
840
190
0
6630
Dec-12
4430
230
510
180
0
5350
Jan-13
6100
250
480
200
0
7030
Feb-13
8010
270
750
190
0
9220
Mar-13
8430
280
1140
200
0
10050
Apr-13
9110
320
1410
210
0
11050
May-13
9730
380
1340
190
0
11640
Jun-13
10120
360
1360
200
0
12040
Jul-13
9080
320
1410
200
0
11010
Aug-13
7820
310
1490
210
0
9830
Sep-13
6540
300
1310
220
0
8370
Oct-13
6010
290
980
210
0
7490
Nov-13
5270
270
770
220
0
6530
Dec-13
5380
260
430
230
0
6300
Jan-14
6210
270
400
200
0
7080
Feb-14
8030
280
750
190
0
9250
Mar-14
8540
300
970
210
0
10020
Apr-14
9120
340
1310
220
5
10995
May-14
9570
390
1260
200
16
11436
Jun-14
10230
380
1240
210
22
12082
Jul-14
9580
350
1300
230
26
11486
Aug-14
7680
340
1250
220
14
9504
Sep-14
6870
320
1210
220
15
8635
Oct-14
5930
310
970
230
11
7451
Nov-14
5260
300
650
240
3
6453
Dec-14
4830
290
300
230
1
5651
Month
NA
SA
Europe
Pacific
China
World
Jan-10
6000
200
720
100
0
7020
Feb-10
7950
220
990
120
0
9280
Mar-10
8100
250
1320
110
0
9780
Apr-10
9050
280
1650
120
0
11100
May-10
9900
310
1590
130
0
11930
Jun-10
10200
300
1620
120
0
12240
Jul-10
8730
280
1590
140
0
10740
Aug-10
8140
250
1560
130
0
10080
Sep-10
6480
230
1590
130
0
8430
Oct-10
5990
220
1320
120
0
7650
Nov-10
5320
210
990
130
0
6650
Dec-10
4640
180
660
140
0
5620
Jan-11
5980
210
690
140
0
7020
Feb-11
7620
240
1020
150
0
9030
Mar-11
8370
250
1290
140
0
10050
Apr-11
8830
290
1620
150
0
10890
May-11
9310
330
1650
130
0
11420
Jun-11
10230
310
1590
140
0
12270
Jul-11
8720
290
1560
150
0
10720
Aug-11
7710
270
1530
140
0
9650
Sep-11
6320
250
1590
150
0
8310
Oct-11
5840
250
1260
160
0
7510
Nov-11
4960
240
900
150
0
6250
Dec-11
4350
210
660
150
0
5370
Jan-12
6020
220
570
160
0
6970
Feb-12
7920
250
840
150
0
9160
Mar-12
8430
270
1110
160
0
9970
Apr-12
9040
310
1500
170
0
11020
May-12
9820
360
1440
160
0
11780
Jun-12
10370
330
1410
170
0
12280
Jul-12
9050
310
1440
160
0
10960
Aug-12
7620
300
1410
170
0
9500
Sep-12
6420
280
1350
180
0
8230
Oct-12
5890
270
1080
180
0
7420
Nov-12
5340
260
840
190
0
6630
Dec-12
4430
230
510
180
0
5350
Jan-13
6100
250
480
200
0
7030
Feb-13
8010
270
750
190
0
9220
Mar-13
8430
280
1140
200
0
10050
Apr-13
9110
320
1410
210
0
11050
May-13
9730
380
1340
190
0
11640
Jun-13
10120
360
1360
200
0
12040
Jul-13
9080
320
1410
200
0
11010
Aug-13
7820
310
1490
210
0
9830
Sep-13
6540
300
1310
220
0
8370
Oct-13
6010
290
980
210
0
7490
Nov-13
5270
270
770
220
0
6530
Dec-13
5380
260
430
230
0
6300
Jan-14
6210
270
400
200
0
7080
Feb-14
8030
280
750
190
0
9250
Mar-14
8540
300
970
210
0
10020
Apr-14
9120
340
1310
220
5
10995
May-14
9570
390
1260
200
16
11436
Jun-14
10230
380
1240
210
22
12082
Jul-14
9580
350
1300
230
26
11486
Aug-14
7680
340
1250
220
14
9504
Sep-14
6870
320
1210
220
15
8635
Oct-14
5930
310
970
230
11
7451
Nov-14
5260
300
650
240
3
6453
Dec-14
4830
290
300
230
1
5651
Explanation / Answer
It is typical that costs, demand, or different variables will fluctuate—that is, go up and down in a apparently random method—over time in keeping with a massive number of motives. On the other hand, some alterations over time tend to show a consistent trend—that is, even though costs appear to vary, they are going to tend to move up over time.
In this very particular case the time series analysis is best applicable as several yearss of data are available also the realtionships and trends looks clear and stable.