Reasoning & math
AuraScore 87/100

Overall Equipment Effectiveness Loss Decomposition Framework

Decompose manufacturing line throughput losses into mathematical availability, performance, and quality vectors.

Use this template when line performance drops below target OEE thresholds and you need a structured mathematical diagnosis of root causes. It guides industrial teams through availability loss, cycle time degradation, and scrap rate decomposition.

Template

Role: Principal Industrial Operations Engineer with twenty years of experience in lean manufacturing analytics and total productive maintenance.

Context

  • Facility: {{plant_facility_name}}
  • Targeted asset: {{production_line_id}}
  • Baseline availability objective: {{target_availability_rate}}
  • Unplanned stoppage duration: {{recorded_downtime_hours}}
  • Ideal design cycle time: {{cycle_time_seconds}}
  • Recorded defect fraction: {{scrap_rate_percentage}}

Task

Construct a comprehensive OEE loss decomposition framework that converts raw operational loss data into an actionable engineering formula and prioritized recovery architecture for {{production_line_id}}.

Method

  1. State mathematical formulas for Availability (A), Performance (P), Quality (Q), and composite OEE based on recorded shift metrics.
  2. Calculate the total lost operating time in hours derived from {{recorded_downtime_hours}} against scheduled operational time.
  3. Compute performance loss by contrasting {{cycle_time_seconds}} against actual parts produced per net operating hour.
  4. Calculate direct yield impairment and rework energy consumption based on {{scrap_rate_percentage}}.
  5. Categorize all loss events into the Six Big Losses: equipment failure, setup/adjustments, idling/minor stops, reduced speed, process defects, and reduced yield.
  6. Quantify the financial contribution of each loss category using unit throughput value calculations.
  7. Develop a decision matrix establishing priority thresholds where engineering intervention supersedes standard operator maintenance.
  8. Produce a standard operating protocol for ongoing shift-by-shift loss metric logging and variance alert thresholds.

Constraints

  • MUST express all loss values in both percentage points of OEE and net production hours.
  • MUST evaluate performance against the stated baseline of {{target_availability_rate}}.
  • MUST NOT use generic qualitative advice without a matching mathematical equation or metric.
  • Propose strictly deterministic formulas that can be implemented in a standard plant spreadsheet or SCADA script.

Output format

Provide the output across four mandatory sections:

  1. Mathematical Formulation Reference (equations for A, P, Q, and Total OEE)
  2. Quantified Loss Tree Matrix (table with columns: Loss Category, Formula, Calculated Impact %, Net Lost Hours)
  3. Critical Variance Analysis (2-3 paragraphs analyzing primary bottlenecks on {{production_line_id}})
  4. Shift-Level Action Protocol (5 sequenced corrective actions with quantitative trigger thresholds)

Self-review

  • Did I verify that Availability * Performance * Quality equals the overall OEE total without mathematical drift?
  • Are all 6 variables ({{plant_facility_name}}, {{production_line_id}}, {{target_availability_rate}}, {{recorded_downtime_hours}}, {{cycle_time_seconds}}, {{scrap_rate_percentage}}) directly incorporated into the analysis?
  • Is the breakdown free of generic maintenance platitudes and focused strictly on industrial engineering mathematics?
AuraScore breakdown
87/100Provisional
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 engineering10/12 · Adequate

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

Output specification14/14 · Strong

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