Hp Deskjet Case Vancouver
Autor: Maryam • June 3, 2018 • 2,384 Words (10 Pages) • 541 Views
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some product has excess inventory while others have less.
Analysis
Main Target:
The company’s main target is to carry as little inventory as possible, yet maintain a high level of availability to consumers.
Current Problem:
Due to fluctuations in demand it has become quite difficult to get the right balance of inventory for the various production options.
Root of the problem:
The unreliable forecasting system is the root cause of the problem.
To solve the root cause of the problem, first the demand should be forecasted properly. The demand for various product versions is calculated using simple linear regression method.
Reasons for choosing simple linear regression method
This method combines economic theory with statistical tools.
The estimates are more reliable.
It is a statistical method used to forecast the demand for longer periods and the estimations are dependent upon relationship between dependent and independent variables.
It is the most common method used to forecast the demand for a product.
The forecasted demand using simple linear regression for different product versions is shown below.
Table 1: Forecasted Demand for product version A
Forecasted Demand for product version A
Period Month Demand Forecasted Demand
1 Nov 80 45
2 Dec 0 45
3 Jan 60 44
4 Feb 90 44
5 Mar 21 43
6 Apr 48 43
7 May 0 42
8 Jun 9 42
9 Jul 20 41
10 Aug 54 41
11 Sep 84 40
12 Oct 42 40
Intercept 45.46969697
Slope -0.482517483
Table 2: Forecasted Demand for product version AA
Forecasted Demand for product version AA
Period Month Demand Forecasted Demand
1 Nov 400 361
2 Dec 255 372
3 Jan 408 383
4 Feb 645 393
5 Mar 210 404
6 Apr 87 415
7 May 432 426
8 Jun 816 436
9 Jul 430 447
10 Aug 630 458
11 Sep 456 468
12 Oct 273 479
Intercept 350.7575758
Slope 10.67832168
Table 3: Forecasted Demand for product version AB
Forecasted Demand for product version AB
Period Month Demand Forecasted Demand
1 Nov 20572 19410
2 Dec 20895 18759
3 Jan 19252 18108
4 Feb 11052 17457
5 Mar 19864 16806
6 Apr 20316 16155
7 May 13336 15505
8 Jun 10578 14854
9 Jul 6096 14203
10 Aug 14496 13552
11 Sep 23712 12901
12 Oct 9792 12251
Intercept 20060.4697
Slope -650.8286713
Table 4: Forecasted Demand for product version AQ
Forecasted Demand for product version AQ
Period Month Demand Forecasted Demand
1 Nov 4008 2963
2 Dec 2196 2843
3 Jan 4761 2722
4 Feb 1953 2602
5 Mar 1008 2482
6 Apr 2358 2361
7 May 1676 2241
8 Jun 540 2121
9 Jul 2310 2000
10 Aug 2046 1880
11 Sep 1797 1760
12 Oct 2961 1639
Intercept 3083.30303
Slope -120.3286713
Table 5: Forecasted Demand for product version AU
Forecasted Demand for product version AU
Period Month Demand Forecasted Demand
1 Nov 4564 4072
2 Dec 3207 4097
3 Jan 7485 4121
4 Feb 4908 4146
5 Mar 5295 4171
6 Apr 90 4196
7 May 0 4220
8 Jun 5004 4245
9 Jul 4385 4270
10 Aug 5103 4295
11 Sep 4302 4319
12 Oct 6153 4344
Intercept 4047.045455
Slope 24.76223776
Table 6: Forecasted Demand for product version AY
Forecasted Demand for product version AY
Period Month Demand Forecasted
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