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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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