Luiten Food Standardized insights across the business
Luiten Food, a family business importing meat and poultry since 1938, needed one reliable view across subsidiaries for procurement, inventory, production, sales and finance. We built a governed model with traceability, forecasting signals and repeatable domain modules, so teams share one set of definitions and act earlier.
Results
Revenue
+58%
Observed over 5 years
Inventory cost
-8%
With seasonal forecasting
Efficiency
+6%
By removing bottlenecks
A governed model reduced debate about numbers and enabled forecasting signals that improved purchasing and stock decisions.
Multiple subsidiaries and domains made it hard to align definitions, trust inventory numbers, and plan with confidence.
A shared, governed data model across domains, plus forecasting signals that support inventory control and planning decisions.
See what we builtRoald Heinsbroek | Finance Director @ Luiten Food
“We have been working together with Food For Analytics for more than 5 years. In this time they created a great BI platform and great insights into our data. These insights helped us in making the right decisions to grow our business.”
Context
Why standardization was needed across subsidiaries
Across subsidiaries, procurement, inventory, production, sales and finance often work with different definitions and different extracts. That creates delay, discussion about numbers, and decisions that come too late. The goal for Luiten Food was one reliable view that teams trust and reuse, so reporting and planning start from shared definitions.
Scope
Customer and product master data audits, with a focus on pricing integrity and invoice reliability.
Users
Finance, master data owners and operations teams working from shared rules and clear exceptions.
Foundation
Titan consolidates ERP data, adds governed KPI logic, and keeps definitions consistent across teams.
Outcome
Fewer invoicing surprises through continuous checks, traceable changes and clear ownership for fixes.
Challenge
ERP detail gaps, delayed subsidiary reporting, and limited planning signals
The ERP could not deliver the level of detail teams needed. Reporting across subsidiaries and domains was incomplete or delayed, so procurement, inventory, production, sales and finance spent time gathering numbers instead of acting on them. Inventory movements and stock taking required traceability and auditability, and planners needed better forecasting signals for seasonal demand swings.
Manual reporting
Multiple files, multiple definitions, and a lot of reconciliation effort.
Limited traceability
Inventory movements and stock counts needed a consistent audit trail.
Seasonality pressure
Without forecasting signals, stock levels were harder to optimize.
Solution
a governed model with traceability and repeatable domain modules
We built a governed model on Titan as the shared foundation across subsidiaries. This model includes traceability in the underlying data flows and makes KPI logic explicit and reusable. On top of that foundation, we added forecasting signals and repeatable domain modules so procurement, inventory, production, sales and finance can adopt the same standards step by step.
- Insights for procurement, inventory, production, sales and finance in one governed model
- Extensive audit trail for inventory movements and stock taking
- Seasonal demand forecasting signals based on history and current inputs
A simple loop that stays maintainable.
Connect
Operational and financial data flows into a governed layer for reporting and decisions.
Model
Shared definitions align stock, margin and customer metrics across subsidiaries and domains.
Signal
Forecasting and exception views highlight seasonality patterns, stock risks and bottlenecks.
Act
Teams use the views in a weekly cadence to adjust purchasing, inventory and operations.
Results
Observed effects that made the rollout stick
With one set of definitions and a reliable cross-domain view, teams spend less time debating numbers and more time acting on them. Traceability improves confidence in reporting, and forecasting signals help spot changes earlier. The result is a more repeatable way of steering across subsidiaries, without rebuilding logic per department.
Revenue
+58%
Observed over five years
Revenue increase observed over five years, attributed to Customer 360 insights supporting commercial decisions.
Inventory cost
-8%
With seasonal forecasting
Inventory cost reduction using seasonal demand forecasting and earlier purchasing signals.
Efficiency
+6%
By removing bottlenecks
Operational efficiency increase by identifying and addressing production bottlenecks.
What we built
A governed data foundation for planning and control
With one set of definitions and a reliable cross-domain view, teams spend less time debating numbers and more time acting on them. Traceability improves confidence in reporting, and forecasting signals help spot changes earlier. The result is a more repeatable way of steering across subsidiaries, without rebuilding logic per department.
Standardized definitions
One governed model that aligns KPIs and definitions across purchasing, inventory, production and sales.
- Shared KPI logic across domains and subsidiaries
- Consistent stock, margin and customer metrics
- Fewer discussions about “which number is right”
Inventory traceability
Audit-ready views for inventory movements and stock taking to speed up checks and reconciliation.
- Trace movements, counts and deviations with context
- Faster root cause checks during stock taking
- Clear ownership and a practical routine for follow up
Purchasing and forecasting insights
Signals for seasonality and demand swings to improve purchasing decisions and stock coverage.
- Earlier warnings for demand shifts and stock risks
- Purchasing signals based on historical patterns and current inputs
- Better balance between availability and working capital
Production and sales performance
Operational and commercial views to spot bottlenecks and act on customer and product performance.
- Bottleneck signals to focus improvement where it matters
- Customer and product views to support sales decisions
- One rhythm for weekly and monthly decision cycles
Implementation
roll out by domains to keep standards consistent
We start with one domain and one decision flow, validate the definitions and outputs with the people who use them, and then extend the model. Because the modules are repeatable, each new domain builds on the same standards rather than introducing new definitions or custom logic.
Phase 1
Foundation and validation
Connect core sources, validate numbers with users, publish first dashboards.
Phase 2
Forecasting and traceability
Implement audit trail views and seasonal forecasting signals for inventory decisions.
Phase 3
Scale and improve
Expand to more users and domains, keep governance stable, extend insights.
Frequently asked questions
What was the goal for Luiten Food?
Create one reliable view across subsidiaries for procurement, inventory, production, sales and finance, so teams share one set of definitions and can act earlier.
What did Food For Analytics deliver?
A governed data model on Titan with traceability, forecasting signals and repeatable domain modules, plus reporting outputs that fit planning and steering routines.
What does 'insights standardized across the business' mean in practice?
It means KPI definitions and core master data logic are aligned once and reused, so procurement, operations and finance work from the same numbers across subsidiaries.
How do you keep data traceable and explainable?
By modelling definitions and data flows in a governed layer, so key views can be explained back to source transactions with an audit trail mindset.
What are forecasting signals in this context?
Signals derived from historical patterns and drivers that help planners and commercial teams spot changes earlier and check scenarios faster. The signals are validated with the people who use them.
How do repeatable domain modules help rollout?
They allow you to implement procurement, inventory, production, sales and finance step by step, using the same standards and patterns instead of rebuilding logic for each domain or subsidiary.
Are the outcomes guaranteed?
No. Outcomes depend on scope, data quality, adoption and the starting situation. We validate one decision flow or domain first before scaling.
Do you want insights across the full business cycle?
In a short call we map your stock risks, seasonality patterns and reporting needs. You leave with a concrete first use case and a rollout outline.
No generic slide deck. We discuss SKUs, lead times, and decision cadence.