LIPSCOMB’S WAREHOUSE Case Solution & Answer

LIPSCOMB’S WAREHOUSE Case Solution

Conclusion

From the following analysis, it is concluded that the particular model is used to calculate the accuracy of the results through applying regression as well as Monte Carlo model. This also indicates that the certain results would require probabilities that are applied in the suggested model. These probabilities would be the key factors to assess the confidential interval as along with the standardized error. Moreover, the following results show that the mean is different in both the cases (Pre-Christmas and Long-term peak) due to the various demands of station required according to the seasonal level of product sale. This shows that the long-term peak is better than the Pre-Christmas because it has high confidence interval and the accurate MSE as compared to the counter result. Therefore, in both the cases, the project would be achieved by simulating the model to minimize errors and to implement results less than and near to 1 in order to achieve the desired outcome and to maintain the position over the number of selected period according to the projected requirement of the related project of the company.

Exhibits

Christmas Peak
	Number of Decant Stations Required	Number of AOC Stations Required	Number of AS/RS aisles needed	Number of Dolly Stackers needed	Number of Sorting OSR aisles needed
Average	27.612	23.177	7.899	1.315	2.917
Standard Deviation	8.330	6.847	2.285	0.397	0.895
min	-3.334	-1.553	-0.063	0.040	-0.734
max	57.306	48.993	18.321	2.656	5.676
MSE	0.167	0.137	0.046	0.008	0.018
Confidence at 95%	0.326546225	0.268415557	0.089563431	0.015555134	0.035095401
CI – max	27.938	23.446	7.988	1.331	2.952
CI  – min	27.285	22.909	7.809	1.300	2.882
CI	0.653	0.537	0.179	0.031	0.070

 

Long term Peak
	Number of Decant Stations Required	Number of AOC Stations Required	Number of AS/RS aisles needed	Number of Dolly Stackers needed	Number of Sorting OSR aisles needed
Average	41.5	34.9	11.6	2.0	6.6
Standard Deviation	12.4	10.2	3.5	0.6	2.0
min	3.4	-1.8	-1.1	0.2	1.6
max	80.6	73.4	23.8	3.9	12.7
MSE	0.247	0.204	0.070	0.012	0.040
Confidence at 95%	0.484415986	0.399993513	0.137807306	0.023034848	0.078730817
CI – max	41.943	35.277	11.785	1.997	6.666
CI  – min	40.974	34.477	11.509	1.951	6.509
CI	0.969	0.800	0.276	0.046	0.157

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