Poultry Processing.
Two processing facilities. ~180,000 birds per week. CSB-System ERP and Marel MES connected through a single definitional layer. No system replaced.
Two poultry processing facilities in central Poland — processing approximately 180,000 broilers per week across rotating three-shift operations — operating three separate systems with no shared definition of what a yield event means. Marel MES, CSB-System ERP, and a legacy HACCP database each captured relevant data. None defined the relationship between a weight anomaly, a cost impact, and a decision threshold. Stathon deployed at Forge Tier: within the first 60 days of the yield scoring model entering production, per-bird recovery improved by 0.8–1.2 percentage points at monitored cutting stations, decision latency compressed from 6–8 hours to under 4 minutes, and approximately 40 hours of monthly supervisor reconciliation capacity was freed.
The operation.
A family-owned poultry processor operating two facilities in central Poland — one primary site in the Łódź corridor handling slaughter through deboning, one satellite facility in the Radom corridor focused on further processing and retail-ready packaging. Combined throughput of approximately 180,000 broilers per week across rotating three-shift operations.
Established market position across domestic retail chains and EU export channels. Revenue of approximately €95 million annually. A production workforce of around 620, supported by 14 operations and planning personnel. Marel equipment with IRIS visual inspection handles primary slaughter and weight grading. CSB-System ERP covers procurement, slaughter planning, inventory, lot traceability, and financial consolidation.
Quality and HACCP compliance was managed through a customized Microsoft Access database in continuous use since 2011. Shift scheduling operated through Excel workbooks supplemented by a legacy Polish HR module — neither integrated with production data. Three systems. No shared operational language.
Net margins between 3% and 5%. A 1% yield deviation on the primary line exceeds the cost of several full-time staff annually.
Three systems. No shared definition.
Marel, CSB-System, and the HACCP Access database each captured operationally relevant data. The problem was not access to data — the problem was that none of the three systems shared a definition of what a yield event means. Marel measured weight. CSB tracked cost. HACCP tracked risk. The relationship between a weight anomaly at a cutting station, its cost consequence, and the threshold at which a supervisor should act existed nowhere in the organisation's systems — only in the heads of three senior supervisors.
The result was a predictable temporal gap. Marel data arrived in near-real-time CSV exports. CSB batch data arrived 6–8 hours later. HACCP data was polled every 15 minutes. By the time the three streams could be reconciled — a manual task consuming approximately 45 minutes per shift across supervisors — the product had already left the cutting room. The organisation was operating in a permanently retrospective mode.
The problem was not that data was unavailable. The problem was that no one had defined what a yield event means across three different systems — and until that was done, every reconciliation was performed too late to matter.
Stathon engagement assessment
The operations director had calculated that the primary site alone was losing approximately €400,000–€600,000 annually to preventable yield variance. Previous attempts to address the problem — a business intelligence dashboard overlay and an OEE module evaluation — had treated the symptom. Neither had addressed the definitional absence underneath it.
No unified definition existed. Marel measured weight. CSB tracked cost. HACCP tracked risk. No system defined the relationship between a weight anomaly, a cost impact, and a decision threshold.
Cutting station output was measured in aggregate at shift end. Individual station deviation during a shift was visible only to supervisors physically present on the floor.
Expected recovery rates varied by flock grade and weight band, but the adjustment was implicit — carried in the experience of three senior supervisors, never formalised.
Marel data arrived in near-real-time CSV exports. CSB batch data arrived 6–8 hours later. HACCP data was polled every 15 minutes. No system handled this mixed-latency environment.
Previous approaches
A business intelligence dashboard had been evaluated and partially deployed — it visualised existing data but could not resolve the latency mismatch between Marel, CSB, and HACCP. An OEE module had also been assessed but not adopted. Both approaches treated the problem as a reporting gap. The actual gap was definitional: without a shared model of what a yield event is, faster access to three separate definitions produces three faster wrong answers.
Three phases.
Forge-tier deployment. Four modules — Arché, Core, Athena, Aegis — deployed in phased sequence. All three source systems remained in place, unchanged. No data was migrated. No system was replaced.
Definition & Integration Spine
Weeks 1–6The first work was not integration — it was definition. The flock-grade adjustment logic that three senior supervisors carried implicitly had never been written down. Making it explicit was the prerequisite for making it operational.
Stathon deployment record
Yield Scoring Model
Months 2–4Yield deviation scoring model operating on a per-station, rolling 15-minute window. Trained on historical Marel weight data cross-referenced with flock grade and lot identifiers from CSB. Outputs a continuous deviation score, not a binary alert.
Supervisor-facing interface showing live deviation scores per cutting station. Flags emerging variance while product is still on the line. Designed in advisory mode from the outset: the system signals, it does not stop the line. Intervention authority remains with the shift supervisor.
A separate tuning cycle was required for the night shift. Night-shift yield patterns deviated systematically from day and evening shifts due to crew composition and flock-grade sequencing. Three weeks of separate calibration before night-shift alerts reached acceptable precision.
Day and evening shift supervisors adopted within the first two weeks. Night-shift adoption required the full 45-day trust-through-validation cycle — supervisors cross-checked alert outputs against their own floor observations before relying on the scoring model. The pattern matches healthcare deployments precisely.
Second Facility & Roadmap
Current · OngoingOnboarding of the satellite facility in the Radom corridor is underway. The packaging lines and retail-ready grading operations introduce entity types not present in the primary site — retail pack specifications, labeling compliance, and shelf-life assignment require Arché domain model extension before Core integration can proceed. Supply chain disruption scoring and workforce coverage modelling are on the confirmed roadmap.
Packaging lines and retail-ready grading. Arché domain model extended to cover retail pack entity types before Core event spine extension begins. Onboarding sequenced to match the primary site pattern.
Four-tier RBAC extended to cover the satellite facility. Site-level access boundaries enforced: floor operators at the Radom facility cannot access Łódź station data. Operations director retains cross-site visibility.
GDPR and the Polish national data protection framework (UODO). Operator-level production data handled under explicit legal basis. Aegis audit log supports regulatory review at both national and EU level.
What changed.
Measured impact from the first 60 days of the yield scoring model in production at the primary facility. Second facility onboarding is ongoing.
This is not a faster report. It is a structural change in the temporal relationship between the organisation and its yield reality. What was previously retrospective — visible only after the shift ended and the product had already left the cutting room — is now operational. The organisation can see variance while it is still forming, in time to intervene.
At 180,000 birds per week with an average dressed carcass weight of 1.8 kg, a 1 percentage point yield improvement represents roughly 3,240 kg of product per week reclassified from trim to primary cut. In an industry where net margins are measured in single-digit percentages, this is not optimisation. It is structural margin recovery.
What this case revealed.
The data existed. The meaning did not.
Three systems captured operationally relevant data. Marel measured weight at every station. CSB tracked cost per lot. HACCP recorded risk events. None of the three defined what a yield event means — the relationship between a weight anomaly, its cost consequence, and the threshold at which a supervisor should act. The supervisors’ implicit knowledge was the only integration layer the organisation had. Externalising it was the prerequisite for everything that followed.
Advisory mode as institutional design.
The system flags. It does not stop the line. Human judgment is retained at every decision point. Night-shift adoption took 45 days — supervisors validated alert outputs against their own floor observations before trusting the scoring model. The trust-through-validation pattern documented in healthcare deployments appeared identically in food manufacturing. Different domain, identical institutional dynamic. Advisory mode is not a technical limitation. It is the correct architecture for operational environments where the cost of a false positive is non-trivial.
Margin recovery, not optimisation.
In single-digit margin industries, definitional infrastructure is not a nice-to-have. A 1% yield deviation at 180,000 birds per week has a direct, calculable margin impact measured in hundreds of thousands of euros annually. The previous approaches — a BI dashboard, an OEE module evaluation — treated the symptom. Both assumed the problem was reporting speed. The actual problem was that the organisation had no shared definition of what it was reporting on. The definitional work treats the cause. The margin recovery follows.
Three systems. Three definitions of what mattered. One definitional layer made them a single operational reality for the first time.
Stathon deployment conclusion
Forward roadmap.
Full second facility integration
Complete Core event spine extension to packaging and retail-ready grading lines. Arché domain model expanded to cover retail pack specification, labeling compliance, and shelf-life assignment entity types.
Supply chain disruption scoring
Athena ingests supplier delivery variance data from the CSB-System procurement module. Correlates with flock grade distribution patterns to flag probable raw material quality shifts before they reach the processing line.
Workforce coverage modelling
Historical yield-per-station data correlated with operator assignment patterns and shift scheduling to assist supervisors in constructing shift rosters that minimise yield variance risk. Depends on Aegis RBAC framework extension for operator performance data.
Deployment record.
Stathon · Definitional Infrastructure Company. Client identity withheld by agreement. Deployment metrics reflect production conditions as of March 2026.