Universal Car Rental Pricing Simulation (Havard Busines School Pricing Simulations) Essay

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Havard Business SCHOOL PAPER: Universal Car Rental Pricing Simulation
July 2012

Universal Car Rental Pricing Simulation

The objective of the simulation was to maximise profits of Universal Car Rental Company. The simulation was run across three cities in Florida; Tampa, Orlando and Miami.

Overall strategy:
We adopted a strategy of offering the highest price achievable whilst maintaining 100% capacity utilisation irrespective of market share. In the context of the scenario, where growth in demand outstripped supply and with only twelve ‘rounds’, we felt market share was not fundamentally important. In respect of setting the pricing level, we calculated the price elasticity of demand to give us an insight into the
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We would adjust this for the price elasticity using judgement.

Figure 1: Summary of pricing in Tampa, Miami and Orlando for the year

Cost Structure
The fixed costs of the business were a combined $9m. In reality decisions may have been made about closing operations or altering the cost base but in the context of the scenario such decisions were not possible. As such the costs were considered sunk and irrelevant to individual city pricing decisions.

At the start of the simulation the gross profit of operations in Tampa were 73%, Orlando 65% and Miami 58%. This ranking was repeated at Net Profit level, with Miami delivering only 3%, and that was despite having a fleet of 12,900 vehicles to cover fixed costs, which was nearly 3 times the size of the Tampa fleet of 4,333 cars. Figure 2 shows the net profit by city and how we grew profit by month. The drivers of the improvement in margins were sales volume and sales price.

Figure 2 Growth in net profit of each city by month.

Capacity Management
As our strategy was capacity utilisation we focused on it entirely, at the expense of losing market share. We reached full weekday capacity utilisation in 1 month and full weekend capacity utilisation within 4 months with plenty of excess demand. We used the break-even calculator and understanding of price sensitivity to determine how far we could