TECHNOLOGY

Engineering Intelligence From the Production Record.

Reliable manufacturing intelligence begins with accurate operational data. Yieldstem’s technology approach combines data engineering, production accounting, statistical analysis, and machine learning to develop a clearer understanding of manufacturing performance.

Plant systems of record

ERP
MES
LIMS / QC
WMS / inventory
Scales
Equipment logs
Files

Ingestion
Read-only adapters
- Export and file adapters
- Interface connectors
- Immutable raw landing
Production model
Canonical records
- Schema normalization
- Unit and identifier mapping
- Validation rules
- Batch attribution
Production accounting
Deterministic
- Material balance engine
- Yield measures
- Moisture and solids basis
- Cost and value model
Modeling
Statistical and ML
- Expected-yield models
- Uncertainty estimation
- Anomaly and drift detection
- Factor association
Decision services
Human review
- Findings and evidence
- Investigation briefs
- Change evaluation
- Workspace and exports

Lineage and audit. Every derived value references its source records, rule version and model version.

Tenant isolation and access control. Organization-level separation and role-based permissions across all zones.

Reviewed outcomes. Findings confirmed or rejected by people return as labelled context for later analysis.

System architecture. There is deliberately no path from this system to plant control equipment.

Specific methods are selected and refined against each plant’s own data. Accounting calculations are kept separate from statistical models, so a yield figure never depends on a model being correct.

Manufacturing Data Engineering

Most of the difficulty in manufacturing analytics is in the record. The engineering is designed for sources that are late, inconsistent, corrected after the fact and often kept by hand.

Data ingestion: Idempotent loading of exports and interface data, so a re-sent file cannot double-count a movement. Raw data is retained immutably in its original form.
Schema normalization: Source fields map to a canonical production model of runs, lots, movements, measurements and products, with mappings versioned per site.
Validation: Declarative rules catch unit errors, implausible weights, broken sequences and missing closures. Results are recorded alongside the data as queryable quality findings.
Traceability: Every derived value keeps references to its source records, the mapping version and the calculation version that produced it.
Production batch attribution: Movements are assigned to runs using transfer events, schedules and time windows. Continuous processes use defined windows, and each attribution stores its method and confidence.
Historical consistency: Corrections arrive late. A bitemporal record separates when something happened from when it was known, so past reports can be reproduced and revised results explained.

A canonical movement record. Example format.

{
  "run_id": "AP-2417",
  "movement": "pomace_out",
  "mass_kg": 3120,
  "class": "byproduct",
  "basis": "measured",
  "attribution": {
    "method": "line_time_window",
    "confidence": "high"
  },
  "lineage": {
    "source": "inventory_export",
    "source_record": "ADJ-7702",
    "ingested_rule_version": "site-map-14"
  }
}

Production Analytics

Yield and material balance are accounting, not prediction. They are calculated deterministically, under definitions agreed with the people who own them.

Yield calculations: Multiple defined measures: in-specification yield on an as-received basis, solids or dry-matter yield where the process changes water content, and stage yields where the weights exist.
Material balance: Closure per run and per stage, with each stream classified and its basis stated as measured, calculated or inferred.
Output comparisons: Like-for-like sets of runs filtered by product, line, raw-material range and period, with the selection rule visible.
Quality metrics: Specification results, holds and rejects joined to run data, so recovery and quality can be examined together.
Process-specific measurement: Configurable definitions, for example extraction on a stated moisture basis for milling, or solids recovery per tonne of fruit for juice.

In-specification yield

Y = M(finished, in spec) / M(input, attributed)

Solids-basis yield

Y_s = (M_out × s_out) / (M_in × s_in)

Balance closure

M(unresolved) = M(input) + M(rework in) + WIP(open) − M(finished) − M(byproduct) − M(documented) − M(process) − M(rework out) − WIP(close)

Predictive Modeling

AI in Yieldstem has a defined job: estimating what a run should have produced, and identifying what is unusual about one that did not. Methods are chosen for reliability on modest plant datasets.

Regression: Regularized and tree-based regression are natural starting points for expected yield. Hierarchical structures can share information across related lines and products when any one has limited history.
Statistical forecasting: Seasonal structure and raw-material trends support output estimates for planned lots and upcoming periods.
Anomaly detection: Robust limits on residuals identify abnormal runs, and sequential methods such as CUSUM or EWMA charts identify slow drift that single-run limits miss.
Uncertainty estimation: Prediction intervals are calibrated and checked against held-out runs. If a stated 90% range holds only 70% of the time, the range is wrong and should be corrected.
Historical production modeling: Validation is time-ordered to avoid leakage from the future. Inputs are restricted to what would be known at the time of prediction, and models must beat the plant's existing standard to be useful.
Change and drift: Equipment rebuilds, new varieties and recipe changes break historical patterns. Monitoring flags degrading estimates, and engineers can mark structural breaks in the history.

Explainable Decision Intelligence

A finding is a structured object with its evidence attached. It is not a sentence produced by a model.

Supporting inputs: Each finding carries references to the records, measurements and comparable runs that it rests on, and flags inputs that were missing or estimated.
Factor association: Attribution methods such as SHAP values help rank which conditions moved an estimate. These describe the model and the history, and are labelled as associations, not causes.
Operational context: Findings are expressed in plant terms such as stages, lots, equipment and shifts, and they keep a place for the conclusions reached by reviewers.
Reproducibility: Model version, data snapshot and rule version are stored with each finding, so anyone can later establish why the system said what it said.

Financial Modeling

Material flow and production economics are connected through a transparent calculation, so a plant's finance team can examine it and disagree with it.

Opportunity estimation: A recovery gap is valued at the difference between product value and the value of the stream the mass otherwise joins, and is stated as a range.
Change assessment: Before-and-after comparisons on expected-yield residuals, reported with uncertainty, and net of the costs that a change introduces.
Configured economics: Prices, standard costs and byproduct values come from the customer's records or stated assumptions, are time-stamped, and access to them can be restricted.
Constraints: Economic estimates should reflect specifications, capacity and market limits. Value that could only be reached by breaching a specification is not an opportunity.

Integration Architecture

Designed for compatibility with existing ERP, MES, quality and plant data systems, subject to the interfaces available at each site.

Progressive connection: File exports first, scheduled transfers next, and interfaces where they exist and the value justifies them. The same canonical model serves all three.
Read-only posture: Connections read data. Yieldstem is not designed to write back to systems of record or to send commands to equipment.
Plant network boundaries: Data leaves plant networks by routes that IT controls, typically through existing business-system exports, without connections to control networks.
System independence: The mapping layer allows different plants to run different ERPs or none, which is common in groups formed through acquisition.

Security & Data Governance

Yield and cost data are commercially sensitive. Security is treated as an architectural requirement, and the details are open to a customer's own review.

Role-based access: Permissions by role and site, including restriction of financial data to those who need it.
Customer data separation: Data is logically separated at organization level, and one customer's data is not used to train models for another without explicit agreement.
Controlled access: Encrypted transport and storage, least-privilege administration, and logged access to customer data.
Auditable data handling: A record of what data arrived, who viewed or changed configuration, and which model versions were used.

Questions from your technical team are welcome.

If you are responsible for manufacturing data or IT, we would be glad to talk through your systems, your data, and the parts of this architecture you would want to examine more closely.

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Engineering Intelligence From the Production Record.

Ingestion

Production model

Production accounting

Modeling

Decision services