PlantIQ

Production Loss & Margin Intelligence

Turn yield, waste, downtime, and giveaway into a visible margin equation.

Help operations, continuous improvement, and finance prioritize the largest production losses, not just the loudest issues by connecting plant conditions to material, time, and capacity loss.

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Built for brownfield production data: PLC signals plus the production context required for credible loss math.

Yield & loss · Packaging Line 2

Illustrative example: sample data, not a customer result.

Problem

Losses are real. A shared, trusted calculation often is not.

Yield variance, packaging waste, overfill, rejects, rate loss, and downtime usually live in different systems, spreadsheets, and meeting narratives.

Without a consistent loss model tied to process context, teams struggle to agree on what changed, what it cost, and whether corrective action improved margin.

Yield & loss · Packaging Line 2

Illustrative example: sample data, not a customer result.

Connect & model

What PlantIQ connects and models

This solution depends on both process signals and production context. PLC tags alone are rarely enough for a credible margin equation.

PlantIQ makes the data requirements explicit so the first deployment stays honest and useful.

  • Input versus good output by product, shift, line, batch, or run
  • Waste and reject categories where counts or masses exist
  • Throughput, rate loss, and downtime context
  • Customer-approved cost assumptions for financialized views

Decision questions this solution answers

  1. 1

    Where did we lose the most material, time, or capacity this week?

  2. 2

    Which product, line, shift, or run drove the variance?

  3. 3

    Which process conditions correlate with waste, rejects, or slow cycles?

  4. 4

    Did the corrective action improve the loss measure against an agreed baseline?

Core experiences

  • Yield and variance views

    Compare expected versus actual yield by product, line, shift, batch, or run when the required inputs exist.

  • Waste and reject analysis

    Categorize material and packaging waste so teams can focus on the largest contributors.

  • Giveaway and overfill where measured

    Surface overfill or giveaway opportunities only when reliable measurement and targets are available.

  • Run-to-run comparison

    Compare runs against best-known patterns and agreed baselines rather than isolated snapshots.

  • Financialized loss framing

    Translate operational loss into margin impact using customer-approved cost assumptions, not invented unit economics.

Illustrative example: yield and waste

Example workflow

  1. 1.Review input, good output, and waste/reject categories for the selected product and line.
  2. 2.Compare variance to expected yield and estimate financial impact with approved assumptions.
  3. 3.Inspect correlated process conditions for the same run window.
  4. 4.Assign an action, then verify whether the next comparable runs improved.

The workflow is designed for shared accountability between operations and finance, not a black-box savings claim.

Required-data transparency

Required

  • Production output and, where possible, material input measures
  • Product/SKU or equivalent production context
  • A clear definition of good product versus waste/rejects

Helpful

  • Production orders, batch IDs, and shift calendars
  • Packaging material usage and fill-weight data
  • Downtime reasons and rate targets
  • Customer fact tables for cost and material standards

Limits

  • OEE is only useful when availability, performance, and quality inputs are reliable.
  • Financialization requires customer-approved assumptions; PlantIQ does not invent plant economics.
  • Correlation between process conditions and loss is not automatic proof of causation.

Guardrails

  • Cannot create trustworthy yield analytics from incomplete production context.
  • Does not replace MES, ERP, or quality systems of record.
  • Does not guarantee savings percentages.

Business value and measurable KPIs

  • Yield variance by product, line, shift, or run
  • Waste and reject contribution to loss
  • Throughput and rate-loss indicators
  • Estimated margin impact using approved cost assumptions
  • Before/after verification of corrective actions

Personas

Outcomes by role

The same plant model, composed for the decisions each stakeholder owns.

  • Operations / continuous improvement

    • Prioritize the largest operational losses
    • Connect process conditions with material and capacity loss

  • Finance

    • Review measurable loss with declared assumptions
    • Validate whether actions produced a verified result

  • Plant manager

    • See which issues deserve investment first
    • Track whether improvement work is changing the margin equation

Deployment path

  1. 1

    Scope one decision

    Choose one high-value loss question and the minimum data required to answer it.

  2. 2

    Connect production context

    Integrate process signals plus product, output, waste, and cost assumptions.

  3. 3

    Validate calculations

    Agree KPI logic with operations and finance before expanding coverage.

  4. 4

    Expand

    Add lines, products, packaging waste, or downtime context on the same model.

Related solutions

Expand on the same operational model

Next step

Start with Production Loss & Margin Intelligence

Bring us the process, asset, utility system, or production loss that matters. We will assess available data and define a focused first deployment.

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