Reasoning & math
AuraScore 81/100

Production Line OEE Loss Decomposition Specification

Decomposes plant telemetry into availability, performance, and quality loss equations to specify line throughput remediation.

Use this template when diagnosing unexplainable throughput shortfalls on continuous or discrete assembly lines. It guides step-by-step mathematical decomposition of Overall Equipment Effectiveness losses into a rigorous engineering spec.

Template

Role: Principal Industrial Engineer specializing in discrete manufacturing throughput and loss modeling.

Context

  • Target manufacturing facility: {{facility_location}}
  • Evaluated workcell or assembly line: {{target_workcell}}
  • Planned shift duration minus planned pauses: {{planned_production_minutes}} minutes
  • Design-basis ideal cycle duration: {{theoretical_takt_seconds}} seconds per unit
  • Aggregate unit count logged during observation window: {{gross_units_produced}} units
  • Scrap and non-conforming items rejected: {{defective_parts_logged}} units

Task

Perform a deterministic mathematical decomposition of manufacturing telemetry to derive Availability, Performance, and Quality ratios, producing an engineering-grade OEE loss specification that isolates primary throughput constraints and establishes baseline numerical remedies.

Method

  1. Calculate Net Operating Time by subtracting documented unplanned downtime events from {{planned_production_minutes}}.
  2. Derive Availability Ratio as the quotient of actual operating run time over {{planned_production_minutes}}.
  3. Determine Net Operating Output rate against {{theoretical_takt_seconds}} to isolate speed loss anomalies.
  4. Compute Performance Ratio comparing actual throughput velocity to standard theoretical capacity.
  5. Quantify Quality Ratio by subtracting {{defective_parts_logged}} from {{gross_units_produced}} divided by gross output.
  6. Compute composite OEE as the product of Availability, Performance, and Quality ratios expressed as a percentage.
  7. Isolate the dominant loss factor among availability stops, speed throttling, or defect scrap.
  8. Define parameter limits and engineering corrective targets for {{target_workcell}}.

Constraints

  • Calculations MUST explicitly document intermediate mathematical formulas using standard operational notation.
  • The specification MUST NOT round intermediate statistical ratios prior to final decimal outputs.
  • All time-based calculations must be normalized to standard minutes and seconds.
  • Corrective actions must remain strictly grounded in empirically derived engineering parameters.

Output format

Deliver the response as a structured engineering specification containing:

  1. Executive Metric Summary (Table of Availability, Performance, Quality, and composite OEE)
  2. Algebraic Loss Breakdown (Formulas, variable definitions, and intermediate calculated values)
  3. Root-Cause Variance Analysis (Max 250 words evaluating the lowest operational vector)
  4. Target Operating Limits Specification (Table of metric thresholds, tolerance bands, and corrective triggers)

Self-review

  • Confirm composite OEE mathematically equals the product of the three independent sub-ratios.
  • Verify all inputs from {{target_workcell}} and {{planned_production_minutes}} appear in the calculations.
  • Ensure constraints regarding notation and intermediate precision are strictly respected.
AuraScore breakdown
81/100Provisional
Instruction clarity15/15 · Strong

Explicit role, a named task, and discrete steps the model can follow.

Context architecture12/12 · Strong

Background, inputs and variables the model needs before it starts.

Constraint engineering12/12 · Strong

Hard boundaries — what the model must and must not do.

Output specification6/14 · Thin

A named, field-level shape for the response.

Reasoning structure10/10 · Strong

Ordered work items that force analysis before an answer.

Model compatibility10/10 · Strong

Length and structure that travel across frontier models.

Token efficiency5/10 · Thin

Signal density — instruction weight without padding.

Reusability7/7 · Strong

Documented variables so the scaffold adapts to new inputs.

Robustness3/5 · Adequate

Quality bar, assumptions and behaviour when inputs are thin.

Observed performance1/5 · Thin

How much real usage the template has behind it.

research-analysis
research-reasoning-math
manufacturing-industrial
manufacturing
oee
industrial-engineering