FROM:
DATE: 29 October 2014
SUBJECT: Quarterly sales analysis
Following an analysis of sales data between 1995 and 2012, it is recommended that that Deere and Co. incorporate the seasonal indicator variables model to forecast future sales. In the regression analysis for this model it was determined that it was able to explain approximately
90% of the variability in past sales. Adjustments to this forecasting model can be made by incorporating overall economic climate projections, long-term weather forecasts, and commodity market futures.
As evidenced in the sales plot, from January 31, 1995 through July 31, 2014 John Deere has enjoyed steady sales growth with seasonal peaks recorded at end of …show more content…
SMOOTHING
THREE MONTHS ENDED
YEAR
DEERE SALES
3-PERIOD
4-PERIOD
0.6
January 31st
1999
2,459
3,046
3,126
2,836
April 30th
1999
3,468
2,988
2,991
3,215
July 31st
1999
3,036
3,097
2,923
3,108
October 31st
1999
2,788
2,721
2,948
2,916
January 31st
2000
2,339
2,972
3,063
2,570
April 30th
2000
3,790
3,254
3,211
3,302
July 31st
2000
3,632
3,599
3,330
3,500
October 31st
2000
3,376
3,238
3,378
3,426
January 31st
2001
2,705
3,297
3,379
2,993
April 30th
2001
3,809
3,377
3,350
3,483
July 31st
2001
3,618
3,529
3,300
3,564
October 31st
2001
3,161
3,100
3,300
3,322
January 31st
2002
2,522
3,223
3,366
2,842
April 30th
2002
3,987
3,493
3,448
3,529
July 31st
2002
3,969
3,808
3,521
3,793
October 31st
2002
3,469
3,411
3,606
3,599
January 31st
2003
2,794
3,554
3,712
3,116
April 30th
2003
4,400
3,865
3,825
3,886
July 31st
2003
4,402
4,247
3,970
4,196
October 31st
2003
3,939
3,942
4,241
4,042
January 31st
2004
3,484
4,433
4,553
3,707
April 30th
2004
5,877
4,926
4,838
5,009
July 31st
2004
5,418
5,501
5,077
5,254
October 31st
2004
5,207
4,917
5,250
5,226
January 31st
2005
4,127
5,318
5,417
4,567
April 30th
2005
6,621
5,584
5,486