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“Mexican” fast food

“Mexican” fast food . Greg Gerold Harry Wong ESE 251. Model. Open 70 hours a week Taxes/ Rent … -> 2000 USD per week Labor Cost -> 10 USD/hr Weighted average cost of all items -> 2.53 USD/item. Regimes. WAP (weighted average price) of 6.50 USD

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“Mexican” fast food

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  1. “Mexican” fast food Greg Gerold Harry Wong ESE 251

  2. Model • Open 70 hours a week • Taxes/ Rent … -> 2000 USD per week • Labor Cost -> 10 USD/hr • Weighted average cost of all items -> 2.53 USD/item

  3. Regimes • WAP (weighted average price) of 6.50 USD • 40 items were sold per an hour and 6 people were required • WAP of 7 USD • 20 items were sold per an hour and 4 people were needed

  4. Profit • Profit = total revenue – total cost • @ $6.50 = $4,916 • @ $7.00 = $1,463 • Is there a price regime within the range that maximizes profit?

  5. Model • Cobb - Douglas Equation • Based on least squares regression fitting of statistical data. • are constants with respect to time. • Beta =1 as K is constant • L = man hours • K= capital (rent, taxes…) • Y = productivity factor

  6. Solutions • Algebraic solution • Two regimes two unknowns • Y= 0.0000484 • alpha=1.71

  7. Optimization • Profit = Total Revenue – Total Cost • Optimal at: • 0 = Marginal revenue – Marginal Cost

  8. Marginal Functions

  9. Demand Estimation • Assume demand can be modeled by: • P(Q) = a – b*Q • 7.00=a - b * 1400 • 6.50=a – b * 2800 • Solve two simultaneous linear equations • a= 7.49, b=0.000357

  10. Finding the Optimal Solution • There are two solutions within the domain • One is 2 burritos a week • The other is 11,120 burritos • So plugging in this quantity to the Profit equation we get: • $6574/week

  11. Logistics • Labour • 13.5 employees working 70 hour weeks • 944 total hours • Pricing • $3.97

  12. Analysis • Sensitivity • Price • Quantity • What if?

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