Mola BV Real-time OEE and sales insights in 30 days
Mola produces unbaked, frozen baked goods for bakeries and in-store bakeries across the Netherlands, Belgium and Germany. They needed real-time OEE dashboards and faster sales visibility to act on losses during the shift. We delivered a working insight layer in 30 days and aligned multi-site teams on the same numbers and definitions.
Results
OEE
+3%
Observed in 6 months
Sales
+8%
Faster analysis
Agility
Real time
Less downtime
The first rollout made performance losses visible during the shift, so teams can respond faster and keep improvements consistent across locations.
OEE losses and sales shifts were discovered too late, which limited the ability to act while production was running.
An OEE and sales insight layer that updates continuously, so losses and trends are visible during the shift and decisions stay aligned.
See what we builtBjörn Meulepas | Manager Finance & IT @ Mola BV
“Food For Analytics enabled instant insights into our OEE per production site and provided sales figures on demand with exceptional performance. Working together is structured and very pleasant, with a concrete execution plan and strong knowledge across the data analytics space.”
Context
A multi-site bakery where real-time OEE and sales reporting must be consistent
Mola runs production across manufacturing locations and serves multiple markets.
Mola runs production across multiple locations and serves multiple markets. When OEE and sales figures arrive late, or differ per report, teams lose time and performance losses stay hidden until after the shift. The goal was governed, real-time insight so shift leaders can act during production and commercial teams always work with up-to-date numbers.
Scope
Real time OEE insights per site, plus up to date sales reporting that stays consistent across teams and locations.
Users
Shift leaders, operations, and commercial and finance teams aligned on shared definitions for performance and sales.
Foundation
Titan consolidates production and commercial data into governed models, so OEE and sales KPIs stay consistent across reports.
Outcome
More agility on the shopfloor and faster decisions through real time OEE loss visibility and on demand sales insights.
Challenge
Less waiting for OEE and sales numbers. More action during the shift
The biggest challenge was the lack of real-time visibility into OEE and the effort needed to produce up-to-date sales figures.This slowed decision-making and made it difficult to respond to downtime and loss reasons while production was still running.
Limited visibility
OEE signals arrived too late to correct issues in the same shift.
Manual reporting
Sales reporting depended on manual steps, which delayed analysis and decisions.
Slow feedback loops
Teams could see what happened, but not quickly enough to prevent repeat losses.
Solution
A practical analytics layer for real-time OEE and on-demand sales
We implemented Titan as a data and analytics layer that provides real-time OEE insight per manufacturing location and delivers on-demand sales figures from one governed model for faster decision-making.
- OEE visibility with consistent definitions and real-time views, so teams spot losses early
- On-demand sales insights with always current numbers, without manual reporting cycles
- First version built and deployed in 30 days using an automated delivery framework
A simple daily loop for operations, finance and commercial.
Monitor
Shift leaders see live OEE per site and the biggest loss reasons.
Diagnose
Teams drill into loss patterns and confirm actions for the next shift.
Align
Finance and commercial pull up to date sales figures from one governed model.
Improve
Daily insights reduce the time between issues and corrective actions.
Results
Measured gains and faster day-to-day decisions
With the platform in use, Mola improved OEE and accelerated sales analysis. More importantly, teams gained real-time visibility into downtime and loss reasons to reduce avoidable stops and act earlier.
Performance
+3%
OEE
OEE improvement observed in six months through faster visibility into losses and consistent definitions across sites.
Commercial impact
+8%
Sales impact
Faster and more accurate sales analysis supported quicker decisions on trends and exceptions.
Agility
Real time
Action during the shift
Loss reasons were available during the shift, so teams discussed issues earlier and reduced avoidable downtime.
What we built
A repeatable foundation for OEE and sales reporting
The focus was to standardize OEE definitions across sites, remove manual steps from sales reporting, and deliver real-time dashboards that shift teams use during production.
Governed OEE model
One definition for OEE and loss categories, aligned across sites and production lines.
- Consistent KPI layer across sites and lines
- Standardized downtime and loss reasons for comparison
- Reliable drilldowns from OEE to root causes
Automated pipelines and environments
A repeatable delivery framework to generate and deploy data pipelines and analytics layers.
- First version delivered in 30 days
- Scalable structure for new sources and KPIs
- Azure-native setup with IT in control
Live OEE dashboards
Views per site and line to spot losses during the shift and act immediately.
- Site and line level views for daily standups
- Drilldowns to loss reasons and time loss trends
- Clear action lists for shift teams
On-demand sales reporting
Current sales figures without spreadsheet consolidation, aligned for finance and commercial.
- Shared sales logic for finance and commercial teams
- Faster analysis for trends and exceptions
- Reduced manual reporting steps and delays
Implementation
Phased delivery from quick win to a daily routine
The first version was delivered in 30 days, then expanded with additional locations and KPIs, plus a stable way of working for continuous improvement.
Phase 1
Scope and connect
Align on OEE definitions and sales dimensions. Connect machine signals and sales sources into a governed model.
Phase 2
Build and validate
Deliver the first operational dashboards for OEE and loss reasons, plus on demand sales views. Validate with real shifts and users.
Phase 3
Run and improve
Embed the dashboards into daily standups, expand to additional sites and KPIs, and keep definitions stable while improving insights.
Frequently asked questions
What was the main goal for Mola BV?
Get real-time visibility into OEE per production site and provide on-demand sales insights, with a practical rollout that delivers value quickly.
What did Food For Analytics deliver?
A Titan rollout that connects the required sources and delivers usable real-time OEE dashboards and on-demand sales reporting with validated KPI definitions.
How comparable is this case to our situation?
The best comparison is your OEE measurement setup, how production status is captured, and how quickly teams need insights. If you share those three, we can propose a realistic first scope and timeline.
Do we need perfect machine data to start with OEE?
No. We can start with what is available and improve precision step by step. The key is to agree on the OEE logic, the events you can reliably capture, and the validation routine with the plant team.
How fast can we get a first live OEE view?
If the required production signals and basic context data are accessible, a first live output can often be delivered within weeks. The exact pace depends on source access and validation availability.
Can this be expanded to planning or downtime improvement?
Yes. Once the core OEE signals and definitions are stable, teams often extend to downtime analysis, loss-reason patterns, planning impact, and operational actions.
OEE and sales insights that teams actually use?
In a short call we map your plants, systems and decisions. You leave with a clear first rollout that delivers a working view fast and scales without rework.
Practical session. Concrete data sources, loss reasons and decision cadence.