Universal Rental Car Simulation Report Case Study Solution
Analysis and Discussion
In this final strategy report, I had performed terribly during my final runs and this was because I had changed by strategy a bit in each of the 1-month period but now it seems that it was not a wise decision made by me. I would first begin to analyze the overall strategy that I had implemented in the simulation and then I would analyze different issues and comment where I performed well and where I did not perform well.
Overall Strategy
The strategy that I had employed was to focus on those indicators that according to me mattered a lot in making money for specific regions such as the Florida region however, the end score and the results of the simulation had shown that those indicators did not matter much. The main indicators that really mattered included the adjustment of the prices and the adjustment of the supply demand of the cities. It had also been noted by me that we are allowed to move cars among cities however, we can never exceed the total capacity of the company. Therefore, I also did this so that I can increase the profits of the low performing regions such as the Florida region(Toni, 2017).
Overall, based on the overall strategy, I had noticed at the end of the simulation that one of the cities was making the lowest of the money as compared to the other cities. I saw that the demand was higher at weekends in those cities and therefore, I increased the prices on the weekends in order to make more money however, this was not at all a good idea because the demand had decreased as the prices increased on the weekend and the overall revenue had dropped. This had resulted in a lower score at the end of the simulation.
The current market demand in exhibit 1 in the appendices shows that the demand has been completely fulfilled for the month of October. The current profitability is set to be equal however, as I thought that the profits in Florida region would increase if the prices are increased therefore, during October I had increased the prices for the Weekend from $ 34 to $ 36 and decreased the Weekday prices from $ 41 to $ 39. This had resulted in a breakeven change in volume of 7.13% for the Weekday and -10.34% for the weekends(JohannesBecker, 2017).
However, this strategy did not work well for me and the profits had dropped significantly in the month of November as we could see by the income statement and line graph in exhibit 2 in appendices. I changed my strategies a lot of times by paying more attention to unnecessary indicators but they did not result in higher profits for the company in total and also for the Florida region. I had paid a lot of attention to the predictions also and then adjusted my prices accordingly however, it did not turn out to be good for the company.
Most of the times I was not able to beat the scores. For instance, in the month of December, I had reversed the prices and then I had decreased the prices for the Weekend from $ 34 to $ 32 and increased the Weekday prices from $ 41 to $ 43. This had resulted in a breakeven change in volume of 21.1% for the Weekday and -14.9% for the weekends. The monthly orders have also seemed to decreased between October and November as shown by the orders data for Orlando and Florida in exhibit 3 in appendices. However, the above strategy also resulted in higher revenue decreases and the overall profitability had declined(Gregson, 2008). As a result, I was left with 1083 cars remaining. As a result, I changed the weekday price back to $ 41 which resulted in increase of the profits in January.
During each of the runs, I had to pay attention to the most important factors and during each new run I felt that I could get a much higher score by focusing on all the important factors and then adjusting the pricing strategy accordingly. My ultimate goal in each of the runs was to maximize the profits of the regions and the total profits of the company. Initially you are able to add dew additional cars but when the cap is reached to the level of 36145, then the market share numbers show a lot of fluctuation as the cars move from one region to another. However, when I had tried to emulate the competition, then that had worked out well for me and this could be seen by the high scored that I achieved in later runs.
Weekday Vs. Weekend Demand
I had noticed that the weekdays within Orlando were always in demand and the scenario was also same in Miami however, the weekends in Orlando were priced higher as compared to Florida where the weekends were not in much demand. Moreover, when differentiating between the weekday and the weekend demand, I came across the leisure and the business factors. At first I thought that this difference made sense and impacted on the performance of the regions however, actually it did not impact on the performance and this information was not of much use(A.Begen, 2016).
Universal Rental Car Simulation Report Harvard Case Solution & Analysis
I also noticed that one of the cities initially such as Orlando was leisure while the other region Florida was more business however, this did not remain same as the prices were changed and as there was a change between the weekends and the weekdays. However, I was just over analyzing this information during the initial runs and it actually did not matter that whether any city was more business or leisure........................
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