harvard food truck challenge simulation tips

Therefore, all of these factors made quantitative analysis valuable. On the contrary, the demand increased if there was any foodie festival in any of the cities.

The demand decreased as the precipitation and temperature increased. Lastly, whether also impacted on the business and made quantitative analysis more valuable. The particular day of the week, whether it was a weekday or weekend also impacted on food truck business. Certain cities like Toronto had high demand for burgers as compared to other cities. The prices also impacted on the profitability of the food truck business as high demand meant charging lower prices and low demand meant charging higher prices. The parking rate also different in each of the city, while the parking rate was $ 0 in London as Sally got a free parking spot at Richmond Row with the help of her good friend. However, Sally had to cover only 5 KM to reach at her lucrative spot in downtown Hamilton. The travelling distance from Hamilton to each of the other three cities was also different with the highest time being taken to reach at the chosen point in London. For instance, Toronto had the highest food costs and London had the lowest food costs per burger. First, the food costs for beef, buns etc different across the cities. There are a number of the factors of the food truck industry that make quantitative analysis more valuable. The data has been collected for the past entire year and now we need to formulate a quantitative model using the collected data.įood Truck Forecaster Harvard Case Solution & Analysisĭiscuss what factors of the food truck industry make quantitative analysis valuable. Therefore, the main issue in this case is to develop an automotive model for daily decision making of Sally. She wanted a quantitative model that would help her to decide where to sell and what price to charge for her burgers in each city, that are London, Waterloo, Toronto and Hamilton. Sally was not pondering that whether any sort of statistical analysis and analytics of the data would help her to maximize her revenues and optimize the profitability. The fresh ingredient prices also differed with respect to each of the city and so did the travel costs, as the distance in kilometres was different for each city. The business had been profitable for the entire year and she had maximized her revenues by charging different prices per burger and driving to the various cities. She had started the business in the year 2013 and it was now November 2014.

Sally Fry, who is the owner of the Hamilton, Ontario based food truck, had just completed her first year of operations of her business. What is the issue or core problem in the case?

Harvard food truck challenge simulation tips