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Specialty Packaging Corporation Part B Case Study

Price established by the subcontractors will certainly influence the decision about the amount of private warehousing space that should be leased. This is because the company only has money to invest into each project depending on the subcontractor’s rates as well as the amount of work to be done thus the price will vary. Thus, if it appears that the subcontractor is charging more than expected, the rates of private warehousing space to be leased may have to decrease. However, this is not the only item Julie needs to consider. Julie must also consider how much space the company should lease. If she errs too much in either direction, she can hurt the company by either paying too much, or by not providing enough space; neither error will be helpful to the goals of the company. So, the needs of the company, as well as the pulls of the overall market will need to be considered when making this decision. Julie will need to examine trends of the company’s current market and current needs in order to assist her with making this decision .l certainly come into play. ...
For instance, does the need seem to be increasing or decreasing at this time Determining what direction the need of the company is heading can hopefully help her reduce any margin for error in one direction or the other.
The Aggregate production planning refers to the planning method that involves planning the production output levels of product lines that should be produced by a firm. The plans are coordinated through

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Extruders
Thermoforming Presses
total
N.o. of particulars
14
25
39
Processing capacity of each particular
3000 pounds per hour
2000 pounds per hour

Capacity loss
5%


Effective processing capacity
2850 per hours


Workers
6
25
31
Salary
$15
$15
$30
Overtime
150%(15)
150%(15)
45
Limit of overtime per quarter
60
60
120
Fixed cost of each particular per quarter
80,000
80,000
160,000
Training cost per worker
3000
$3,000
$6,000
Laying off workers
$2,000
2500
$4,500
Training cost for Re-use of idle extruders
$2,000

$2,000



$172,765

The demand forecast for the company for the next three years is;


Forecast











Black plastic forecast
Cum.demand forecast
Normal Prod. days
Prod. rate units/day






2007
I
6650
6650



ii
4576
11226



iii
6293
17519



iv
13777
31296





31296
63
496.7619048






2008
I
7509
7509



ii
5149
12658



iii
7056
19714



iv
15399
35113





35113
63
557.3492063






2009
I
8367
8367



ii
5721
14088



iii
7819
21907



iv
17021
38928





38928
63
617.9047619
















Clear plast. forecast
Cum. Dd. forecast
Normal Prod. days
Prod. Hrs /employee
Prod. rate per

SPC Year Quarter 2005 I 2250 3200 II 1737 7658 p = 4 (even) III 2412 4420 IV 7269 2384 2006 I 3514 3654 II 2143 8680 III 3459 5695 IV 7056 1953 2007 I 4120 4742 II 2766 13673 III 2556 6640 IV 8253 2737 2008 I 5491 3486 II 4382 13186 III 4315 5448 IV 12035 3485 2009 I 5648 7728 II 3696 16591 III 4843 8236 IV 13097 3316 Black Plastics Quarter period Forecast bias MSE MAD MAPE TS Deseasonalized Demand Regression I 1 2250 2820 0.80 2536 286 286 286 81573 286 13 13 1.00 II 2 1737 3046 0.57 1824 87 87 373 44610 187 5 9 2.00 SUMMARY OUTPUT III 3 2412 3575 3273 0.74 2283-129 129 244 35256 167 5 8 1.46 IV 4 7269 3784 3500 2.08 6301-968 968-723 260542 367 13 9-1.97 Regression Statistics I 5 3514 3965 3727 0.94 3352-162 162-886 213706 326 5 8-2.71 Multiple R 0.9554126993 II 6 2143 4070 3954 0.54 2368 225 225-661 186517 309 10 9-2.14 R Square 0.9128134259 III 7 3459 4119 4181 0.83 2916-543 543-1203 201938 343 16 10-3.51 Adjusted R Square 0.9065858135 IV 8 7056 4272 4408 1.60 7935 879 879-324 273264 410 12 10-0.79 Standard Error 345.5089345887 I 9 4120 4237 4634 0.89 4168 48 48-277 243154 370 1 9-0.75 Observations 16 II 10 2766 4274 4861 0.57 2911 145 145-131 220950 347 5 9-0.38 III 11 2556 4595 5088 0.50 3549 993 993 862 290567 406 39 11 2.12 ANOVA IV 12 8253 4969 5315 1.55 9569 1316 1316 2177 410581 482 16 12 4.52 df SS MS F Significance F I 13 5491 5390 5542 0.99 4984-507 507 1670 398795 484 9 12 3.45 Regression 1 17497621.06176 17497621 146.57518 8.347E-009 II 14 4382 6083 5769 0.76 3455-927 927 743 431723 515 21 12 1.44 Residual 14 1671269.934329 119376.42 III 15 4315 6575 5996 0.72 4182-133 133 610 404115 490 3 12 1.25 Total 15 19168890.99609 IV 16 12035 6509 6222 1.93 11202-833 833-223 422205 511 7 11-0.44 I 17 5648 6490 6449 0.88 5800 152 152-71 398724 490 3 11-0.14 Coefficients Standard Error t Stat P-value Lower 95% Upper 95%Lower 95.0%Upper 95.0% II 18 3696 6688 6676 0.55 3998 302 302 231 381645 480 8 11 0.48 Intercept 2592.747610294 214.8734219756 12.066395 8.714E-009 2131.8895 3053.6057 2131.8895 3053.6057 III 19 4843 6903 0.70 4815-28 28 204 361599 456 1 10 0.45 X Variable 1 226.8558823529 18.7378528402 12.106824 8.347E-009 186.66715 267.04462 186.66715 267.04462 IV 20 13097 7130 1.84 12836-261 261-57 346930 446 2 10-0.13 I 21 6616 Estimate of standard deviation of forecast error: 557.6798 II 22 4542 III 23 5448 IV 24 14469 Seasonal Factors I 25 7432 I 0.899 II 26 7636 II 0.599 III 27 7840 III 0.698 IV 28 8044 IV 1.800 I 29 8248 II 30 8452 III 31 8656 IV 32 8860 Clear Plastics Quarter period Forecast bias MSE MAD MAPE TS Deseasonalized Demand Regression I 1 3200 3876 0.83 2952-248 248-248 61682 248 8 8-1.00 II 2 7658 4140 1.85 7862 204 204-44 51747 226 3 5-0.19 SUMMARY OUTPUT III 3 4420 4472 4404 1.00 4175-245 245-289 54508 233 6 5-1.24 IV 4 2384 4657 4668 0.51 1936-448 448-737 91129 287 19 9-2.57 Regression Statistics I 5 3654 4944 4932 0.74 3756 102 102-636 74969 250 3 8-2.55 Multiple R 0.931048614 II 6 8680 5049 5196 1.67 9868 1188 1188 552 297539 406 14 9 1.36 R Square 0.8668515216 III 7 5695 5132 5460 1.04 5176-519 519 33 293529 422 9 9 0.08 Adjusted R Square 0.857340916 IV 8 1953 5892 5724 0.34 2373 420 420 453 278940 422 22 10 1.07 Standard Error 509.7729917265 I 9 4742 6634 5987 0.79 4560-182 182 271 251641 395 4 10 0.69 Observations 16 II 10 13673 6850 6251 2.19 11873-1800 1800-1529 550581 536 13 10-2.85 III 11 6640 6791 6515 1.02 6177-463 463-1992 520032 529 7 10-3.77 ANOVA IV 12 2737 6573 6779 0.40 2811 74 74-1918 477157 491 3 9-3.90 df SS MS F Significance F I 13 3486 6363 7043 0.49 5364 1878 1878-40 711645 598 54 12-0.07 Regression 1 23685916.21352 23685916 91.145775 1.654E-007 II 14 13186 6308 7307 1.80 13878 692 692 651 695001 605 5 12 1.08 Residual 14 3638159.043313 259868.5 III 15 5448 6932 7571 0.72 7178 1730 1730 2381 848128 680 32 13 3.50 Total 15 27324075.25684 IV 16 3485 7887 7835 0.44 3249-236 236 2145 798597 652 7 13 3.29 I 17 7728 8662 8099 0.95 6168-1560 1560 585 894841 705 20 13 0.83 Coefficients Standard Error t Stat P-value Lower 95% Upper 95%Lower 95.0%Upper 95.0% II 18 16591 8989 8363 1.98 15883-708 708-123 872980 705 4 13-0.17 Intercept 3611.978860294 317.0299121016 11.39318 1.815E-008 2932.0167 4291.941 2932.0167 4291.941 III 19 8236 8627 0.95 8179-57 57-181 827207 671 1 12-0.27 X Variable 1 263.9402573529 27.6463221197 9.5470297 1.654E-007 204.64474 323.23577 204.64474 323.23577 IV 20 3316 8891 0.37 3687 371 371 190 792727 656 11 12 0.29 I 21 6972 Estimate of standard deviation of forecast error: 820.4349 II 22 17888 III 23 9180 Quarter Average - Seasonal Factor St IV 24 4125 I 0.76 I 25 7776 II 1.90 II 26 19893 III 0.95 III 27 10180 IV 0.41 IV 28 4563 I 29 8580 II 30 21898 III 31 11181 IV 32 5000 Black Plastic Demand ('000 lbs) Clear Plastic Demand ('000 lbs) Demand D t Deseasonal ized Demand D t D t (based on regression) Seasonal Factor S t E t A t Percent Error Demand D t Deseasonal ized Demand D t D t (based on regression) Seasonal Factor S t E t A t Percent Error 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 210 2000 4000 6000 8000 10000 12000 14000 16000 18000 Quarterly Historical Demand for Clear and Black Plastic Containers

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