LIPSCOMB’S WAREHOUSE Case Solution And Analysis, HBR Case Study Solution & Analysis of Harvard Case Studies

LIPSCOMB’S WAREHOUSE Case Solution

Introduction

Lipscomb Warehouse was the most successful high street chain business, which involved selling the particular home wares as well as household products to the end-users with a diversified range of supply throughout anumber of cities. It was also analyzed that majority of the retail market's demand was at the peak during the Christmas days. Projected selling of the products were suddenly 100% as compared to the historical record of un-seasonal sales. So in order to make the accurate assumptions of achieving the 100% of the accuracy or at least some of the accurate value, the company determined the Monte Carlo simulation model to analyze the current impact of calculating the confidence interval as well as provide at least less than 100% accuracy to implement the decision regarding the expansion of the pre-Christmas project to increase the sales as well as achieve the desire results.

Model analysis

To determine the net results, several functions have been calculated to analyze the need to provide the accuracy ratio to implement the project for the certain period. In the case, Christmas peak and a long-term peak are calculated to compare the results with thoseinvolved in the projected figures. The following analysis shows how the sudden changes in probability would increase the confidence interval as well as meet the projected demand of at least near to 100% of accuracy.

Christmas Peak

According to the current results, the average rate of decanting stations of 27.6 would be required to achieve near the standard confidence interval and to assess the probability near to 1 in order to achieve the desired results. The other factors include AOC, AS, and Dolly stackers, which would require a certain amount of rates to meet the demand of pre-Christmas sales. The standard deviation includes the changes in the average stations with that to the proposed ones. The standard deviation rate has therefore been calculated in order to determine the risk factor involved in the recent stations required. The MSE rate shows that a normal rate of error would require achieving the entire simulation results, so all calculations show normal probability to meet the confidence interval of at least 66% (Greater than 50%,which is fair as compared to the projected results) to maintain the position to consider the project in the pre-Christmas days.

Long-term peak

On the other side, a long-term peak would require an increase in sales volume of the season as well as off the season product expansion. An average of 41.8 decants stations would be required to achieve the desired results as well as to increase the probability near to 1 in order determine the accurate confidence interval level as a modified standard error. An average of 90% CI has been calculated,its hows that this figure would help better understand the demand for the pre-Christmas products to expand in the certain period. The comparison of these two situations shows that long-term peak would achieve better results but would have higher standard deviation than the Christmas peak. This shows that it would require a higher capacity in order fulfill the demand.

Monte Carlos Simulation Model

In order to achieve the full accuracy of the project, a Monte Carlos Simulation model has been applied to standardize the errors and to fix them for determining the accuracy of the result. In the case where only 10000 observations are required;however, a maximum of 2500 observations have been included in order to eliminate the errors in the model and to provide a result that meets the demand of achieving the desired outcome. In conclusion, this model would benefit to analyze the accuracy of the results through the use of the suggested model and to increase the confidential level of the particular company to implement the project due to achieving the desired level of certain probabilities................

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