PG Kaas Real-time yield, waste and margin insight per product
PG Kaas wanted end-to-end visibility from intake to packed product, without relying on manual exports and spreadsheet logic. With Titan as the data foundation and Power BI as the reporting layer, the team gained a shared view on yield, waste and margin per product and reduced manual corrections in the ERP to finance flow.
Results
Efficiency
+9%
Observed increase
Inventory
Optimized
More just in time
Errors
-99%
Manual corrections
The rollout reduced manual corrections and made production performance visible within minutes, so shopfloor teams and management steer on the same numbers.
Production performance signals arrived too late and ERP to finance flows required manual corrections, which slowed steering and created unnecessary errors.
A governed model that connects shopfloor and ERP data, delivers near real-time production dashboards, and automates the ERP to finance flow for consistent reporting.
See what we builtSander Peters | Business Manager @ PG Kaas GmbH
“Food For Analytics implemented a compact but effective data & analytics platform. Microsoft Power BI gets us the insights that we need. Through these insights we optimized our production processes. Food For Analytics’ knowledge of the food-industry is a real benefit. They just understand our business and deliver.”
Context
From intake to packed product, the numbers must match
In food production, yield and waste are not only operational KPIs. They directly affect margin, planning, and customer profitability. PG Kaas needed one consistent view across production, inventory and finance, so teams could act on losses earlier and trust the numbers in monthly close.
Scope
Near real-time production performance insight, inventory reporting, and automated ERP to finance data flows.
Users
Operations and shopfloor leads, plus finance and management aligned on shared KPI definitions.
Foundation
Titan consolidates production, inventory and ERP data into governed models, feeding Power BI with consistent definitions.
Outcome
Faster shopfloor feedback loops and more reliable finance reporting through automated flows and trusted performance views.
Challenge
Less manual correction and earlier insight into yield and waste
The main challenge was not a lack of data. It was a lack of consistent definitions and a reliable flow from ERP and production signals to finance reporting. That led to manual steps, late detection of yield loss, and extra corrections during reporting cycles.
Fragmented definitions
Yield, waste and consumption were calculated differently per report or team.
Manual reporting steps
Exports and spreadsheets increased effort and created avoidable errors.
Issues ERP and finance
Manual corrections created noise in the flow from ERP to finance. Affecting trust in reporting.
Solution
A governed model that feeds shopfloor and management
Food For Analytics delivered a cloud-native data and analytics platform built on Titan. The foundation combines ERP, production, and finance logic in one reusable model. On top of that, Power BI delivers near real-time insight for operations and trusted reporting for management.
- One model with shared definitions for production KPIs, losses, and inventory
- Near real-time performance dashboards that work for daily standups
- Automated validation and integration logic to reduce manual corrections
A simple flow that stays maintainable.
Connect
ERP and production data flow into a standardized landing zone and model.
Model
KPIs are translated into a governed layer with shared definitions.
Validate
Automated checks and reconciliation reduce manual corrections.
Use
Power BI dashboards support daily standups and management routines.
Results
Faster feedback, fewer errors, more trust
The platform created visible operational impact quickly, and it also reduced noise for finance and reporting.
Performance
+9%
Observed production efficiency
Faster feedback helped teams target losses and improve throughput.
Inventory
JIT
More just in time inventory
Better inventory insight supported stock optimization and reduced firefighting.
Reliability
-99%
Fewer manual errors
Automation reduced corrections between ERP and finance processes.
What we built
A foundation for production, inventory and finance
he solution was designed to start compact and remain reusable. A single model supports operational decisions and reporting cycles without duplicating logic across departments.
Data model
A governed layer that combines ERP, production and finance logic with shared definitions.
- Production orders, batches, yields, downtime reasons
- Inventory movements and valuation aligned to finance
- Reusable semantic layer for Power BI
Production KPI logic
Clear definitions for efficiency, losses, and throughput, validated with the production team.
- Line performance per shift, day, and product group
- Loss categorization for focused improvement actions
- Near real-time refresh for shopfloor routines
Automation and guardrails
Automated flows and checks to keep the platform stable and the numbers trusted.
- Validation rules to detect missing or inconsistent data
- Reconciliation routines between ERP and finance logic
- Reusable automation framework for rollout speed
Adoption routine
A simple cadence that makes dashboards part of daily work, not an extra task.
- Daily standup: yesterday performance and losses
- Weekly review: trends, root causes, improvement actions
- Clear ownership for KPI definitions and changes
Implementation
From first usable insight to a stable routine
We started with a focused scope: the minimum set of sources and definitions needed for a first usable view on yield and waste. After validation with the team, we expanded to additional views and checks. This approach keeps time-to-value short, while ensuring definitions remain stable when the solution scales.
Phase 1
Scope and connect
Align on KPI definitions for efficiency, losses, yields and inventory. Connect ERP and production signals into a governed landing zone and model.
Phase 2
Build and validate
Deliver the first near real-time production performance dashboards and inventory views. Validate with shopfloor leads and reconcile ERP versus finance logic.
Phase 3
Run and improve
Embed the dashboards into daily standups, automate checks and exception handling, and extend with new lines, KPIs and reporting needs while keeping definitions stable.
Frequently asked questions
What was the main goal for PG Kaas?
Create one reliable data foundation that delivers real-time insight into yield, waste and margin per product, supports inventory decisions, and reduces manual corrections from ERP to finance.
What did Food For Analytics deliver?
A compact Titan-based data and analytics foundation with standardized KPI definitions and reporting outputs (for example Power BI) used for daily production, yield and inventory decisions.
How comparable is this case to our situation?
The best comparison is your yield measurement, how you track intake and consumption, and where manual corrections happen between ERP and finance today. If you share those three, we can propose a realistic starting scope.
Do we need to replace our ERP or MES to do this?
No. Titan connects to your existing systems. The focus is consistent definitions, automated flows, and usable outputs that reduce manual steps and errors.
How fast can we see the first yield and waste insights?
Typically within weeks once source access is in place. We start with one production flow, validate the numbers with the team, and then expand to more products, shifts or lines.
Can this be extended to real-time monitoring and checks?
Yes. After the first stable model is live, teams often extend to near real-time monitoring, automated checks, and operational routines that keep yield and margin under control.
Want yield and margin insight you can trust?
In a short call we map your production flow and systems, identify where yield loss and manual corrections occur, and propose a first scope that delivers a usable output within weeks.
Practical session. Concrete data sources, validation checks and decision cadence.