You can find the case here: https://danguetta.github.io/files/cases/cargo.pdf

Part 1: Cargo and its Drivers The data associated with this part of the case can be found in the tab "Part 1 (summarized)" of the spreadsheet associated with this case. Carefully examine the data provided with the first part of the case and answer the following questions:

1. What strategy do you think the drivers used to decide what orders to place? Comment on this choice of strategy.

2. On average, what is the sum of the profit made by the drivers and by Cargo over each two-month replenishment period?

3. Suppose we were still using the first strategy (i.e., decentralized decisions), but that drivers now correctly optimized the quantity to order in each period. What would be the sum of the profit made by Cargo and its drivers over each two-month replenishment period?

4. Suppose we are now using the second strategy (i.e., centralized decisions) so that Cargo makes decisions for each driver in each period to optimize the total revenue earned by the drivers. What would be the sum of the profit earned by Cargo and its drivers over each two-month replenishment period?

5. Based on your answers above, write a paragraph summarizing the pros and cons of the centralized and decentralized strategies.

6. The tab "Part 1 (forecast model)" in the same spreadsheet contains the sales data used to calibrate the demand model from the simulated trial. This data was provided by Herbacetamol's manufacturer, and catalogues past Herbacetamol sales over a single year with hourly granularity. Look at these data and answer the following questions:

o Investigate the data contained in this tab. Is there a time of year that Herbacetamol is more popular? A day of the week? A time of the day? Is there a trend in sales over time? Do these factors depend on one another? Can you come up with explanations for the patterns you observe in these data?

o How would you build a demand model based on these data that Cargo could use to predict future Herbacetamol sales?