Optimizing multi-site supplier orders: AI, an essential lever for organized catering

The challenges of multi-site supply management

Organized foodservice chains continue to face major constraints when it comes to purchasing and inventory management:

• Demand fluctuations: volumes ordered must be adjusted in line with sales forecasts, promotions and local events.
• Multiplicity of suppliers and platforms: heterogeneity of purchasing conditions, delivery times, risks of shortages or overstocking.
• Material cost control:optimize purchase prices and minimize losses to maximize gross margin.
• Standards and sustainability: alignment with regulatory constraints and reduction of food waste.
• AI for order management: automation and flow optimization.

Artificial intelligence is involved in three major areas:

1. Accuracy of consumption forecasts

Machine learning algorithms in production ERP exploit historical sales data, cross-referenced with external variables (weather, events, seasonality) to refine actual requirements by site. Unlike traditional methods based on averages, these models make it possible to adjust orders with a particularly low error rate at the point-of-sale level.

Example: A fast-food chain equipped with an AI solution can now predict, with a margin of error of less than 5%, the weekly consumption of burger buns per site, taking into account peak crowds linked to local matches or festivals.

2. Supplier order automation

AI integrates specific business rules (economic volumes, delivery times, purchase prices) to automate order generation:

• Real-time comparison of prices and lead times between multiple suppliers.
• Optimized grouping of orders to reduce logistics costs.
• Dynamic adjustments based on stock levels and differences between forecasts and actual sales.

Example: a catering group reducing its ordering errors by 30% thanks to an automatic recommendation engine connected to its ERP and its suppliers' EDI.

3. Intelligent inventory management and waste reduction

AI cross-references real-time inventory data with sales forecasts to adjust requirements:

• Overstock alerts: adjust orders on slow-moving products to avoid losses.
• Intelligent reallocation: redistribution of surpluses between nearby facilities via an internal platform.
• Optimization of consumption dates:analysis of DLUO/DLC to prioritize sales and avoid unsold stock.

Example: a fast-food chain reducing its losses of fresh vegetables by 20% by integrating AI capable of reconfiguring orders according to the shelf life of products in stock.

AI, a strategic investment for multi-site catering

The implementation of an AI solution in supplier order management relies on:

• A good level of data consolidation: a centralized, well-structured IS enables reliable predictive models to be fed.
• Fluid integration with existing tools: compatibility with ERP, purchasing management software and supplier interfaces.
• Continuous performance monitoring:KPIs to track: forecast reliability rate, breakage rate, overstock rate, material cost evolution.

Adoria supports multi-site catering groups in optimizing their processes with advanced purchasing and inventory management solutions embedding AI in their decision-making processes.

To find out more, discover our order-advice-by-AI solutions.