Meyer QSL master data controls for cold chain logistics
Meyer Quick Service Logistics (QSL) runs cold chain logistics for major fast food chains. Customer and product master data drives contracts, pricing, assortments, and every invoice. We implemented automated anomaly detection, audit trails, and continuous completeness checks on Titan, so pricing outliers are flagged early and every change stays traceable.
Results
Root cause
Faster
Less investigation
Data quality
Improved
Automated audits
Invoices
Fewer
Pricing errors
The platform made inconsistencies visible early, so teams could act before issues impacted invoicing and reporting.
Price and master data changes were hard to monitor, so exceptions surfaced late and required manual investigation.
Master data audits and traceability built into daily operations, so outliers are flagged early and changes stay auditable.
See what we built“Food For Analytics provided excellent guidance with a clear roadmap for structure, infrastructure, architecture and automation. It shortens our time to market for actionable insights.”
Context
A fast-moving cold-chain network where master data drives every invoice
Meyer Quick Service Logistics (QSL) supports large fast food chains with end-to-end logistics.
Customer and product master data sits at the heart of contracts, pricing, assortments and invoicing.
When master data changes are not governed, small inconsistencies quickly become operational noise.
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
When master data slips, invoicing suffers
QSL needed better master data management for customer and product data. Their existing systems did not reliably detect anomalies, which increased the risk of invoicing errors and made root cause analysis slow.
Anomalies went unnoticed
Pricing outliers and inconsistent values were not flagged early enough, creating operational risk.
Limited traceability
Without a clear audit trail, it was difficult to see what changed, when, and what the impact was.
Data completeness gaps
Missing or incomplete master data created inconsistencies and extra manual work to correct issues.
Solution
Master data audits and traceability built into daily operations
Food For Analytics implemented a data analytics platform for Meyer QSL GmbH to detect anomalies, improve completeness, and provide a full audit trail. The setup supports proactive control instead of reactive firefighting.
- Detect pricing outliers and other anomalies in product master data
- Detailed audit trail for every customer or product entry
- Improve data quality by identifying missing or incomplete data
A simple loop that stays maintainable.
Connect
Master data flows into a governed layer for checks and reporting.
Detect
Automated rules flag outliers, missing fields, and inconsistencies.
Trace
Every change is tracked, so investigations stay fast and auditable.
Act
Teams resolve exceptions early and prevent recurrence through routine audits.
Results
More control, fewer surprises
The platform made inconsistencies visible early, so teams could act before issues impacted invoicing and reporting.
Investigation time
Faster
Root cause analysis
Exceptions are visible with context, reducing time spent investigating inconsistencies.
Controls
Improved
Data quality
Regular automated audits identify missing and incomplete data, keeping quality on track.
Invoice reliability
Fewer
Invoicing errors
Pricing outliers are detected early, reducing the risk of incorrect invoices.
What we built
Controls that run continuously, not once
The focus was to prevent issues early, make exceptions clear, and give teams confidence in the data behind invoicing and reporting.
Outlier detection
Rules that flag unusual pricing and inconsistent values in product master data.
- Detect pricing outliers and anomalies
- Support root cause analysis with clear context
- Reduce risk before invoices are created
Audit trail per record
A detailed audit trail for every customer and product entry.
- See what changed and when
- Make decisions traceable for audits
- Improve trust across teams
Completeness checks
Regular audits to identify missing or incomplete master data.
- Identify missing fields and incomplete records
- Create a clear backlog for fixes
- Prevent recurring errors
Operational routine
A simple way of working to handle exceptions and keep quality improving.
- Exceptions reviewed with clear ownership
- Root causes documented to avoid repeat issues
- Audits run regularly and stay visible
Implementation
A phased approach that fit the business
Fast start, real output, then a routine that keeps improving quality.
Phase 1
Scope and connect
Define the audit rules and connect the relevant master data and pricing inputs.
Phase 2
Build and validate
Implement checks, anomaly detection, and audit trails. Validate with real examples.
Phase 3
Run and improve
Introduce a routine, tune thresholds, and expand to additional domains when needed.
Frequently asked questions
What was the main goal for Meyer QSL?
Improve control over customer and product master data by detecting anomalies early and creating repeatable data quality routines with clear audit trails.
What did Food For Analytics deliver?
A Titan-based data quality approach with automated anomaly detection, monitoring outputs, and traceability so teams can resolve issues faster and prevent recurring errors.
How comparable is this case to our situation?
The best comparison is how often master data changes, how many systems touch customer and product records, and how issues are currently detected. If you share those three, we can propose a practical first set of rules and checks.
What kinds of anomalies can be detected?
Typical examples are missing or inconsistent attributes, unexpected changes in key fields, duplicates, unusual price or unit patterns, and mismatches between related master data entities.
Does anomaly detection replace master data ownership?
No. It supports ownership. The goal is to surface exceptions with context and an audit trail so the right owner can act quickly and prevent the same issue from returning.
What is a good first step if we want better master data quality?
Start with the 10 to 20 master data fields that drive daily operations and reporting, define what “good” looks like, implement the first automated checks, and iterate based on the exceptions you see.
Want stronger control over your master data?
In one call we map your systems and pick one high-impact control track, such as pricing outliers or data completeness. Then we roll it out as a repeatable routine that keeps improving.
No big program. Just practical controls that stay usable.