Forecast to Save: How Data-Driven Ordering Reduces Food Costs in Restaurants
Solve longstanding inefficiencies in restaurant inventory management with smart, dynamic procurement automation.
In this sample case study, we will demonstrate how ORCA’s neutral connectivity layer can solve practiced restaurant inefficiencies through orchestrating data communication between historically siloed tech.
The Problem:
Doing food ordering, inventory, and procurement has always been a haphazard job in restaurants.
Manual inventory counts, accomplished individually by managers or chefs, are time-consuming and inaccurate. Purchasing decisions are often based on static par levels rather than dynamic demand, leading to costly overstocking, food waste, and emergency stockouts of key ingredients.
In this case, Restaurant X has particular trouble with ordering, resulting in unsustainably high food costs and unnecessary time spent on inventory. It has become a constant waste of valuable resources.
However, Restaurant X does it this way for a reason: because there aren’t any other reliable options. At least, not traditionally.
Now, thanks to data model standards and ORCA’s orchestration layer, there is finally a solution to this longstanding industry issue.
The Solution:
Through ORCA, Restaurant X can automate the entire procurement cycle by linking their inventory management system to AI-powered demand forecasting. This workflow not only triggers a reorder in real-time when stock is low, but also calculates the optimal quantity to order based on predicted sales, seasonality, and upcoming promotions.
How?
Here’s the workflow:
Trigger: The restaurant’s inventory management system signals an alert when an ingredient's stock level falls below its predefined par level.
Enrichment Pipeline: The event is first routed to an AI Demand Forecasting service. This service analyzes historical sales data, seasonality, weather forecasts, and planned marketing promotions to calculate and append “predicted usage” data onto the event for the next delivery cycle. A second enrichment service then queries the APIs of approved suppliers to find the best current price for the required quantity, appending information on optimal supplier and best price. The event is now enriched with highly contextual data and ready to be delivered.
Subscribers: The final, enriched event, containing the optimal order quantity and supplier, is sent to the procurement or accounting system (e.g., Restaurant365, Craftable) to automatically generate a purchase order for manager approval.
Here AI is applied practically. Past attempts to solve inventory management procurement lacked the flexibility and dynamic thinking necessary for efficient ordering. Today’s AI finally boasts that flexibility and dynamism, and can reliably order what a restaurant needs based on a number of variables.
Business Impact:
The pairing of inventory management with a demand forecast AI reduces food waste at Restaurant X by preventing over-ordering and minimizes lost sales from prematurely 86’d items.
Food costs are also consistently lowered by enabling dynamic, price-optimized procurement.
Finally, Restaurant X’s management gets back valuable time by automating this time-consuming core administrative task.
The restaurant industry is rife with inefficiency, much of it for so long insurmountable due to the necessity of personal management of important tasks and systems that do not communicate with each other. However, when systems can communicate and data is shared and connected across a business, even these seemingly cemented industry headaches can finally be relieved.
Curious what inefficiencies ORCA can solve in your tech stack? Get in touch to set up a demo here.
