Building for a supper club in Minneapolis.

KPI’s for the restaurant below:

Below is provided data supporting the KPI’s restaurant owners will experience after implementing this solution.

ROI as the developer outlined below:

Below is provided data supporting the ROI potential as a result of execution and scaling of this solution.

Example of forecasting from an early proof of concept above

The problem:

Menu items are frequently unavailable due to inaccuracies in the inventory ordering system, caused by the natural limitations of human forecasting.

The proposed solution (areas to apply AI):

1. Demand forecasting:

Apply AI to analyze historical sales data (POS) to predict the demand for certain menu items, therefore predicting the need or lack thereof for certain ingredients to be restocked in the inventory. The model takes into consideration external factors like weather, time of year, upcoming holidays, etc.
2. Inventory optimization:

Apply AI to calculate stock depletion rates, and recommend reorder quantities and dates. The AI uses the inventory data from the current digital tracking system, its predicted menu item demand, and ingredient-to-item mapping to execute this task.
3. Automated order execution

Eventually, apply AI to executing reorders automatically. Order triggers based on the forecasted need, and adjusts based on delivery time.

Food Waste Savings: Eliminating 4-10% of food waste could save U.S. restaurants $162 billion annually.
Time Savings: Gains ~30 hr/month of labor back, saving >$6,600 annually.
Revenue Losses: One-third of restaurants lose $10,400-$15,600 yearly per location due to shortages and waste.
Sales Boost: Eliminating shortages could increase traffic by 5-10% via consistent menu availability.
Customer Satisfaction: 91% of consumers favor restaurants reducing waste, boosting loyalty; 51% avoid places with shortages.
Profitability Gains: Improved satisfaction and efficiency could lift profits by 2-5% through repeat visits and upsells.

Investment is a lot of time upfront, return is future revenue, which could turn into generous profits once model is optimized. The model would be a monthly fee.
The average restaurant manager spends 5hr/week @ $40/hr managing inventory. Cut that, and $850/month saved. At a fee of $499/month, capturing 0.1% of restaurants nationwide results in $6 million in annual revenue. The numbers are there for potential ROI.
Advantage being an early mover in restaurant agentic systems.

Proof of concept idea for dashboard - includes immediate actions, examples of prediction vs sales, and more.

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