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Below is the demand data of a retail store which sells bikes. The data represents the frequency of demand for the last 200 days. Demand for Bikes Frequency 0 10 1 20 2 40 3 60 4 40 5 30 The store implements the (s,S) order policy (which we discussed in our last lecture). According to this policy, the store orders new bikes only if the amount of inventory at the end of the day is less than or equal to s. The amount of order is determined such that the total amount of inventory after the replenishment is S. Currently the store uses (s, S) = (3,10). Assume that the starting inventory is 10. The costs are: Holding Cost Fixed Order Cost Variable Order Cost Cost of a Lost Sale Sell Price = $ 10 (per day per bike) = $100 (cost of a shipment, independent of the order amount) = $50 (purchase price of a single bike) = $150 = $100 Using the historical data and cost parameters, compute the expected profit of that store using Monte Carlo simulation in MS Excel. Below is the demand data of a retail store which sells bikes. The data represents the frequency of demand for the last 200 days. Demand for Bikes Frequency 0 10 1 20 2 40 3 60 4 40 5 30 The store implements the (s,S) order policy (which we discussed in our last lecture). According to this policy, the store orders new bikes only if the amount of inventory at the end of the day is less than or equal to s. The amount of order is determined such that the total amount of inventory after the replenishment is S. Currently the store uses (s, S) = (3,10). Assume that the starting inventory is 10. The costs are: Holding Cost Fixed Order Cost Variable Order Cost Cost of a Lost Sale Sell Price = $ 10 (per day per bike) = $100 (cost of a shipment, independent of the order amount) = $50 (purchase price of a single bike) = $150 = $100 Using the historical data and cost parameters, compute the expected profit of that store using Monte Carlo simulation in MS Excel