Manufacturing, Quality, EHS and environmental performance become decision-ready only when the underlying information systems are decision-ready too.
A reproducible synthetic-data case study connecting MES, QMS, EHS and EMS source contracts to formula-driven KPIs, decision confidence, cross-domain intervention priorities and scenario economics across a French cosmetics manufacturing network.
Latest month: June 2026. Numbers use tabular numerals and consistent formatting throughout the site.
The network average is stable; the decision risk is local. Two lines require P2 intervention, while half of the network still falls short of the stricter decision-grade evidence threshold.
Six deterministic stages. Each stage exposes typed data and reproducible outputs. Validation labels below refer to internal project QA, not external certification.
MES, QMS, EHS and EMS owners, dependencies and SLAs mapped.
Completeness, validity, consistency, timeliness, uniqueness and lineage.
OEE, RFT, Cpk, TF/TG, CAPA, energy, water and waste intensity.
Fitness-for-use per decision with minimum-source floor.
Weighted multi-domain risk, dominant risk, concurrence multiplier.
Benefit, payback, residual risk, confidence uplift.
Monthly network trends and the site scorecard together separate systemic movement from localised issues.
FR03-L2 is not an average-network problem. It is a local capability case: Cpk 0.54, RFT 86.8%, monthly value loss €195k and intervention score 52.4.
| Site | OEE | RFT | Median Cpk | Decision conf. | Priority score↓ | Annualised loss | Dominant action |
|---|---|---|---|---|---|---|---|
| FR03 | 78.1% | 95.3% | 1.01 | 95.6% | 52.43 | €4.65m | Restore process capability and CAPA flow |
| FR06 | 75.5% | 99.0% | 1.33 | 96.5% | 35.98 | €6.93m | Stabilise equipment and changeovers |
| FR02 | 82.6% | 96.5% | 1.36 | 96.6% | 27.80 | €4.01m | Close high-potential EHS actions |
| FR08 | 81.1% | 96.6% | 1.22 | 97.8% | 24.80 | €5.35m | Close high-potential EHS actions |
| FR05 | 79.0% | 93.3% | 1.35 | 96.9% | 18.89 | €5.55m | Close high-potential EHS actions |
| FR07 | 79.2% | 97.4% | 1.28 | 95.8% | 16.83 | €5.96m | Validate data before operational action |
| FR01 | 80.0% | 94.4% | 1.44 | 97.9% | 5.23 | €4.77m | Close high-potential EHS actions |
| FR04 | 81.1% | 95.7% | 1.46 | 97.5% | 3.71 | €4.61m | Close high-potential EHS actions |
Six data-quality dimensions, transparent weights and a source-floor gate. Without decision-grade evidence, an operational escalation is a validation task first.
FR07-L1 shows why sequence matters. At 95.8% network confidence, a weak EMS contract makes environmental variance a validation case before it becomes an operational escalation.
Filter, search and toggle between table and chart. Ranking is driven by a weighted multi-domain score with a concurrence multiplier.
| # | Line | Site | Family | OEE | RFT | Cpk | TF12 | TG12 | Dec. conf. | Class | Value loss | Score↓ | Priority | Dominant risk | Decision gate |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | FR03-L2 | FR03 | Lip & Eye | 76.7% | 86.8% | 0.54 | 0.00 | 0.000 | 95.6% | Guarded | €195k | 52.43 | P2 | Restore process capability and CAPA flow | Restore process capability and CAPA flow |
| 2 | FR06-L1 | FR06 | Shampoo & Care | 67.3% | 100.0% | 1.22 | 0.00 | 0.000 | 96.5% | Guarded | €374k | 35.98 | P2 | Stabilise equipment and changeovers | Stabilise equipment and changeovers |
| 3 | FR02-L2 | FR02 | Cleansers | 82.0% | 95.9% | 1.44 | 38.84 | 0.272 | 96.6% | Guarded | €184k | 27.80 | P3 | Close high-potential EHS actions | Close high-potential EHS actions |
| 4 | FR08-L2 | FR08 | Lip & Eye | 79.9% | 93.6% | 1.08 | 19.65 | 0.236 | 97.8% | Decision-grade | €257k | 24.80 | P3 | Close high-potential EHS actions | Close high-potential EHS actions |
| 5 | FR05-L1 | FR05 | Serums | 80.2% | 95.5% | 1.22 | 39.30 | 0.845 | 96.9% | Decision-grade | €170k | 18.89 | Monitor | Close high-potential EHS actions | Close high-potential EHS actions |
| 6 | FR07-L1 | FR07 | Face Care | 83.0% | 98.4% | 1.35 | 0.00 | 0.000 | 95.8% | Guarded | €213k | 16.83 | Monitor | Investigate resource-intensity variance | Validate data before operational action |
| 7 | FR07-L2 | FR07 | Cleansers | 75.4% | 96.2% | 1.21 | 0.00 | 0.000 | 95.8% | Guarded | €284k | 14.67 | Monitor | Stabilise equipment and changeovers | Validate data before operational action |
| 8 | FR05-L2 | FR05 | Dermo Care | 77.8% | 91.3% | 1.48 | 0.00 | 0.000 | 96.9% | Decision-grade | €293k | 10.71 | Monitor | Restore process capability and CAPA flow | Restore process capability and CAPA flow |
| 9 | FR08-L1 | FR08 | Foundation | 82.3% | 98.6% | 1.36 | 0.00 | 0.000 | 97.8% | Decision-grade | €189k | 6.20 | Monitor | Investigate resource-intensity variance | Investigate resource-intensity variance |
| 10 | FR01-L1 | FR01 | Shampoo & Care | 78.9% | 94.2% | 1.51 | 0.00 | 0.000 | 97.9% | Decision-grade | €224k | 5.23 | Monitor | Close high-potential EHS actions | Close high-potential EHS actions |
| 11 | FR06-L2 | FR06 | Hair Color | 83.4% | 98.2% | 1.44 | 0.00 | 0.000 | 96.5% | Guarded | €203k | 4.66 | Monitor | Investigate resource-intensity variance | Investigate resource-intensity variance |
| 12 | FR03-L1 | FR03 | Foundation | 79.5% | 100.0% | 1.48 | 0.00 | 0.000 | 95.6% | Guarded | €192k | 4.20 | Monitor | Investigate resource-intensity variance | Investigate resource-intensity variance |
| 13 | FR02-L1 | FR02 | Face Care | 83.2% | 97.3% | 1.27 | 0.00 | 0.000 | 96.6% | Guarded | €151k | 3.87 | Monitor | Restore process capability and CAPA flow | Restore process capability and CAPA flow |
| 14 | FR04-L1 | FR04 | Fine Fragrance | 82.5% | 94.4% | 1.56 | 0.00 | 0.000 | 97.5% | Decision-grade | €175k | 3.71 | Monitor | Close high-potential EHS actions | Close high-potential EHS actions |
| 15 | FR04-L2 | FR04 | Gift Sets | 79.7% | 96.8% | 1.36 | 0.00 | 0.000 | 97.5% | Decision-grade | €208k | 3.26 | Monitor | Investigate resource-intensity variance | Investigate resource-intensity variance |
| 16 | FR01-L2 | FR01 | Hair Color | 81.1% | 94.7% | 1.38 | 0.00 | 0.000 | 97.9% | Decision-grade | €174k | 3.09 | Monitor | Close high-potential EHS actions | Close high-potential EHS actions |
The five highest-loss lines represent 33.8% of current monthly value loss. Prioritisation should be line-specific, not a generic network programme.
Adjust the five intervention levers around the validated scenario. Outputs are deterministic sensitivities, not forecasts or approved business cases.
Illustrative decision-support logic. Scenario outputs are deterministic sensitivities, not forecasts, approved business cases or operational commitments.
Value case driven by recoverable capacity and quality loss.
Findings are stated in engineering terms. Decisions translate them into what a leadership team should sequence next.
The five highest-loss lines represent 33.8% of current monthly value loss.
Prioritise line-level interventions instead of a network-wide generic programme.
FR03-L2 combines Cpk 0.54, RFT 86.8% and an intervention score of 52.4.
Stabilise the process window and accelerate repeat-deviation CAPA closure.
FR02-L2 shows a high action backlog and TF12 of 38.84.
Use overdue high-potential actions as the intervention trigger, not near-miss volume alone.
FR07-L1 has 95.8% decision confidence; environmental variance should be validated before operational escalation.
Restore EMS lineage and completeness, then reassess resource intensity.
Predictive maintenance sprint has the shortest modelled payback at 2.9 months, while EHS/data scenarios primarily reduce decision risk.
Govern the portfolio with two lenses: financial return and control-risk reduction.
Only 50.0% of lines meet the 95% decision-confidence threshold in the latest month.
Treat data contracts as a product backlog with named owners, SLAs and measurable remediation value.
A formula-first Excel control model, a reproducible Python pipeline and a PostgreSQL-compatible SQL product, all aligned to ISA-95 source, calculation, decision and control layers.
Every claim below sits inside a clear boundary. Thresholds and economic assumptions are transparent project choices.
The implementation is inspired by public frameworks. It is not a certification assessment, a compliance tool, an incident prediction model or an external benchmark. Thresholds and economic assumptions are transparent project choices.
Reproducibility is a first-class output. The pipeline is seeded and every check reruns identically.
| ID | Category | Description | Observed | Expected | Status |
|---|---|---|---|---|---|
| QA01 | Scope | Synthetic site count | 8 | 8 | PASS |
| QA02 | Scope | Production line count | 16 | 16 | PASS |
| QA03 | Scope | Line-month record count | 288 | 16 × 18 = 288 | PASS |
| QA04 | Keys | Unique line-month keys | 288 | 288 | PASS |
| QA05 | Reconciliation | Good + scrap equals total units | 288 | 288 | PASS |
| QA06 | Bounds | OEE within 0–100% | 0.639–0.878 | [0,1] | PASS |
| QA07 | Bounds | Data-quality score within 0–100% | 0.738–1.000 | [0,1] | PASS |
| QA08 | Formula | OEE identity reconciles | 0 | < 1e-10 | PASS |
| QA09 | France EHS | TF calculation uses 1,000,000 hours | 6.1 | non-negative | PASS |
| QA10 | France EHS | TG calculation uses 1,000 hours | 0.084 | non-negative | PASS |
| QA11 | Scenarios | Scenario count | 5 | 5 | PASS |
| QA12 | Scenarios | All scenario priority scores improve | 5 | 5 | PASS |
| QA13 | Claims | No real factory names used | Aube, Camélia, Dune, Éclat, Iris, Nacre, Orme, Quartz | synthetic names only | PASS |
| QA14 | Claims | Independent project positioning is explicit | independent synthetic-data project | required | PASS |
| QA15 | Completeness | No nulls in core KPI mart | 0 | 0 | PASS |
Availability × Performance × Quality vs. reported OEE.
CosmaOps demonstrates how industrial performance, quality, EHS, environmental intensity and data governance can be engineered as one decision product instead of five disconnected reporting streams.