Inventory Optimization

Introduction

After working in restaurants across multiple roles—from staff member to general manager—I repeatedly experienced the same operational inefficiency: inventory management and supplier ordering. It was manual, time-consuming, and error-prone, yet critical to the business.

This project was born directly from that pain.

The Inefficiency

Restaurants rely on accurate stock levels to avoid running out of key ingredients or over-ordering items that won’t be used. In practice, most restaurants:

• count inventory manually once or twice per week

• compile reorder lists by hand

• Compare supplier prices manually

• calculate food cost on the fly

• rely on estimates instead of data

A Chef who earns $25/h, and spends 2+ hours twice a week counting stock and building orders — this is roughly $5,200/year of labour tied to a process that should not require human time at all.

This inefficiency exists industry-wide.

The Solution

I designed and built a system that automatically generates optimized ingredient reorder lists using:

• sales data

• recipe-level ingredient usage

• supplier pricing

• par levels

• food cost targets

The goal:

eliminate manual counts and manual ordering logic, while ensuring accuracy and cost efficiency.

How It Worked

The system connected every operational element into one automated flow:

1. Each menu item had a unique ID

2. Each item is linked to a recipe with specific ingredient quantities

3. Ingredients linked to suppliers, conversion factors, and prices

4. Every time an item was sold, the system deducted the exact ingredient usage

5. Inventory levels stayed updated automatically

6. When ingredients fell below par levels, the system:

   
   

   • calculated required reorder quantities

   • compared supplier prices

   • Recommended which supplier to order from based on cost efficiency

   • Generated a completed purchase list

A task that took hours was now completed in seconds, with no guesswork.

Conclusion

The system reduced weekly labour, eliminated manual errors, and brought clarity to food cost and supplier decisions. More importantly, it freed operational time that could be spent improving the business instead of counting stock.

This project reinforced my ability to:

• translate real operational pain into automated systems

• map complex data flows

• design logic that improves efficiency

• build solutions that reduce cost and save time