Nadia

Blending, Yield & Batch Control

Nadia is the yield and batch discipline agent for Manufacturing. It makes the quiet profit leaks loud: theoretical vs actual yields, loss by step (blend, transfer, filtration, fill), and variability by batch, SKU, tank, and shift. Nadia helps plants protect both throughput and margin by catching drift early—ingredient overuse, mis-weighs, off-spec additions, rework risk, and process conditions that create chronic yield loss. It standardizes how yield is calculated, explains what changed (formula, process, operator practice, equipment, temperature/time, material quality), and produces actionable calls tied to the cost impact. Built to support judgment, not replace it: your team chooses the corrective action, Nadia ensures the yield losses are quantified, traced, and prevented from repeating.

Primary Outputs

Typical deliverables

Yield scorecards: theoretical vs actual yield by batch/SKU/tank, with loss quantified in units and dollars

Loss attribution by step: where yield is lost (blend, transfer, filtration, line start-up, QC holds, rework)

Variability watchlist: abnormal batch outcomes, trend shifts, and “same formula, different result” signals

Rework and off-spec risk flags: triggers tied to deviations, ingredient tolerance breaches, or unstable process windows

Batch exception packets: deviations, evidence, probable causes, and corrective action recommendations by owner

Control actions: tightening tolerances, SOP changes, parameter guardrails, and checks that prevent repeat loss

Core Capabilities

What it does

Calculates and standardizes yield: theoretical inputs vs finished outputs, with clear loss definitions and traceability

Detects drift and variability: abnormal ingredient usage, parameter shifts, and repeat patterns by tank, line, and shift

Links yield loss to cost: converts losses into dollars and units so teams prioritize what matters

Flags deviation and rework risk early: ingredients out of tolerance, sequence errors, and off-spec triggers

Builds exception narratives: what happened, what changed, and the likely causes—using plant-ready language

Recommends control points: parameter guardrails, SOP updates, and checks that prevent recurring yield loss

Operational Fit

How it’s used

Used By

Process engineers, production supervisors, QA, batching/blending teams, and plant controllers.

Used For

Yield loss reduction, batch discipline, variance explanation, and preventing rework and off-spec escalation.

Typical Questions

Where are we losing yield—by step, tank, line, or shift?
What changed in this batch versus normal (inputs, parameters, sequence, materials, operator practice)?
Is this trending toward rework or off-spec—and what control point prevents it?

Agent Overview