AI-driven analysis makes more than 10% of cross-border movements in the dental trade transparent and manageable
Industry: Dental industry (dental equipment & accessories)
Company size: approx. 1,500 employees, >€300m revenue
Region: Europe
Objective: Fact-based transparency of cross-border product and value flows in indirect trade channels to stabilise margins, commissions and sales steering
Results at a glance
≈95% transparency level across accessory and consumables product flows
>10% of all product movements identified as cross-border, particularly in margin-relevant categories
Significant reduction in sales and distributor conflicts and a stronger basis for
margin-related steering (qualitative evidence)
1. Initial situation
The manufacturer sells its portfolio across Europe primarily via specialised dental distributors. Local subsidiaries are responsible for pricing, field sales and market performance.
– Indirect sales model with strong dependence on distributors
– Significant price differences between European markets
– Accessories and consumables as a high-margin, recurring revenue stream
2. Challenge
Cross-border product movements led to structural distortions – without a reliable, group-wide data foundation.
Cause → Effect → Risk
– Cause: Differing national price levels and high market transparency via price comparison portals
– Effect: Product flows from low-price markets into high-price markets
Risk:
– Revenue and margin erosion in high-price markets
– Commission losses and declining motivation in local field sales
– Increasing conflicts with distributor partners
– Lack of steering and argumentation capability for local and central management
While legally permissible within the EU, cross-border trade was operationally unmanageable without transparency.
3. Solution / Approach
The goal was reliable, fact-based transparency of actual product and value flows – without interfering in operational sales or pricing decisions.
Approach: AI-supported analysis as a scalable and cost-efficient transparency and reporting layer.
Implementation
– Data aggregation
Internal shipment data to distributors
External sell-out and invoice data from a representative sample of dental practices
Additional test purchases and validation measures
AI-based pattern recognition
Normalisation of heterogeneous data across countries, distributors and product groups
Identification of origin–destination product and value flows
Reporting & governance
Unified reporting logic
Dashboards for management, local subsidiaries and sales teams
Role of AI
AI supported data aggregation, scaling and pattern recognition only
No automated pricing, sales or management decisions
Interpretation and action remained fully with human experts
4. Value of AI compared to traditional analytics
Classical BI or analytics teams would have required months of manual consolidation
Highly fragmented data landscape across countries and distributors
AI enabled:
1. Rapid scaling across large data volumes
2. Consistent logic across all markets
3. Significantly lower analysis and operating costs
Outcome: High transparency in a short time frame with superior cost efficiency.
5. Results & impact
≈95% transparency level with <5% deviation
>10% cross-border share, especially in high-margin maintenance and consumables categories
Solid factual basis for structured discussions with distributors in high-price markets
Expected margin improvements as pricing, commission and partner models could be addressed based on evidence
fundamentally changed our internal discussions and conversations with distributors.”
AI-driven transparency for complex, indirect sales models in regulated B2B environments.
Learn how data-based analysis enables fact-driven steering of European pricing, sales and margin structures.