Actual vs. Theoretical Food Cost

Project Description:

We know our actual (raw ingredient) food cost and our at-store sales, and our estimated theoretical food cost, which is 3-5% too high. We don’t know our precise theoretical food cost or what is driving the variance.

How can we use our recipes to get precise theoretical food cost and use our historical food purchase data to determine what is causing the variance? What systems can we put in place to continuously measure that variance, and reduce it?

Also, we want to predict our food orders based on historical sales data to avoid unnecessary swings in inventory.

Keywords:

Supply Chain Analytics, Machine Learning, Data Analysis, Cupcakes

Tools / Skills / Systems:

SQL, Rest APIs (Toast, 7Shifts), Javascript, Python, R

Meeting Times:

Tuesdays at 11:30am-12:20pm EST + Thursdays at 11:30am-1:20pm EST