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Assessment 2- Simulation Case Studies

There are two case studies. You are required to use simulation to answer the following questions.

For each question:

•    Provide the R code used for your simulation, along with the results/output.

•    Clearly explain the sections where you simulated the data.

•    Offer recommendations or suggestions for the organization based on your findings.

Question 1- Online retailing

Suppose you are working as a data scientist for an online retailer, and you are tasked with optimising the pricing strategy for a new product. The product is currently priced at $50, but you want to determine whether a higher or lower price would result in greater profit. You estimate that the demand for the product can be modeled using a normal distribution with a mean of 100 units per day and a standard deviation of 20 units per day. The cost of producing each unit is $30, and the retailer's profit is the difference between the revenue and the cost. The retailer incurs a fixed cost of $1000 per day for operating expenses, regardless of the number of units sold. What is the best price? Imagine the demand and price has a quadratic relationship. Use simulation to answer to the question. You need to define specific range for the price ($30-$100)

Question 2- Coffee Shop

Suppose you are managing a small coffee shop, and you want to optimise your daily revenue through pricing strategies. The demand for coffee is influenced by both price and the day of the week. The demand follows a linear relationship with the price, and weekdays have higher demand compared to weekends.

1.          Price-Demand Relationship:

The demand for coffee (D) is modeled as: D=150−18*Price. Price is the price of a cup of coffee.

2.          Weekday Influence:

Weekdays have a 30% higher demand than weekends.

3.          Simulation Instructions:

•   Simulate the daily revenue for 1000 scenarios for different prices (ranging from $1.50 to $5.00) on both weekdays and weekends.

•   Assume that the probability of a weekday is 70% and a weekend is 30%.

•   Calculate revenue for each scenario.

•   Consider a normal distribution for random variations in demand.

4.          Objective

•           Find the optimal price that maximises the average daily revenue over the 1000 scenarios.