Operational performance analysis to identify opportunities to improve productivity and increase revenue.

This is my submission for Maven Analytics’ Pizza Challenge October 2022. This report summarizes Plato’s Pizza restaurant’s annual performance of the year 2015. Additionally, it makes recommendations to identify opportunities to increase sales and perform more efficiently.


Data Set

Plato’s Pizza is a fictitious Greek-inspired pizza place in New Jersey. The dataset provided in the Data Playground contains one year of sales data. It contains four tables in CSV format.

Task

Create data models and use your data analysis & visualization skills to bring the data to life. Analyze data and create a report to help Plato’s Pizza find opportunities to drive more sales and work more efficiently.

Also, answer the following questions.

  1. What days and times do we tend to be busiest?
  2. How many pizzas are we making during peak periods?
  3. What are our best and worst selling pizzas?
  4. What’s our average order value?
  5. How well are we utilizing our seating capacity? (we have 15 tables and 60 seats)
My Process

Plato’s Pizza has already defined the goals of this analysis, so I mainly focused on answering all the questions using Power BI. Created some Dax measures and calculated columns.

Visualization is my favorite part. An important component of being a data analyst is the ability to communicate your findings in a way that will appeal to your audience. We are always tempted to show every metric possible. But more is not always good. Since too many elements can be distracting I decided to include graphs that answer Plato’s questions.

1. What days and times do we tend to be busiest?

2. How many pizzas are we making during peak periods?

3. What are our best and worst selling pizzas?

4. What’s our average order value?

Average Daily Revenue = 
AVERAGEX(
    VALUES(orders[date]),[Total Revenue]
)


Average Daily Orders = 
AVERAGEX(
    VALUES(orders[date]), [Total Orders]
)
Average Daily Pizzas Sold = 
AVERAGEX(
    VALUES(orders[date]), [Total Quantity Sold]
)
Average Pizzas Per Order = 
DIVIDE([Total Quantity Sold],[Total Orders]
)
Average Order Value = 
DIVIDE([Total Revenue], [Total Orders]
)


5. How well are we utilizing our seating capacity? (we have 15 tables and 60 seats)

Recommendations:
  • The most popular large size pizzas are generating highest (45.89%) revenue $375.32K whereas XXL size pizzas are generating least (0.12%) revenue $1.01K. Therefore, XXL size can be removed from the menu.
  • Changing restaurant opening time to 11:00 to 22:00 hours can reduce operational cost.
  • Replacing 50% seating arrangement with 2-seater tables will maximize utilization of seating capacity.
  • To drive more sale on Sunday and during post lunch off hours, Plato’s should run promotions / special offers.

Data Visualization

Check out an Interactive Version.