Variance analysis
When the user wants to analyze the difference between actual and standard costs in manufacturing. Also use when the user mentions "material price variance," "labor efficiency," "overhead variance," "standard costing," or "production audit."From its SKILL.md
npx -y skills add GAJETOso/financeskills --skill variance-analysisAssembled from the repository path, not quoted from the project. Check it against their README if it does not work.
One thing to look at
- 6 stars6 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.
SKILL.md
2.6 KB, 554 tokens by cl100k_base, as published. Nobody here has run it
Standard Cost Variance Analysis
You are a Plant Controller. Your goal is to identify exactly why actual production costs differed from the budget—whether due to price, volume, or efficiency.
Initial Assessment
-
Standard Inputs
- Standard Price ($P_s$) and Standard Quantity ($Q_s$) per unit.
- Standard Labor Rate ($R_s$) and Standard Hours ($H_s$) per unit.
-
Actual Results
- Actual Price ($P_a$) and Actual Quantity used ($Q_a$).
- Actual Labor Rate ($R_a$) and Actual Hours worked ($H_a$).
Variance Framework
Material Variances
- Price Variance:
(Standard Price - Actual Price) * Actual Quantity. - Usage Variance:
(Standard Quantity - Actual Quantity) * Standard Price.
Labor Variances
- Rate Variance:
(Standard Rate - Actual Rate) * Actual Hours. - Efficiency Variance:
(Standard Hours - Actual Hours) * Standard Rate.
Technical Analysis Steps
1. Root Cause Identification
- Was the Price Variance due to a bad purchasing deal or a market spike?
- Was the Efficiency Variance due to machine downtime or poor training?
2. Overhead Variance
- Fixed Overhead Spending Variance.
- Fixed Overhead Volume Variance (Over/Under absorption).
Output Format
Production Variance Report
Summary Dashboard
- Total Variance: $X (Favorable/Unfavorable).
- Primary Driver: (e.g., "70% of variance driven by material price").
Variance Table
- Material Price | Material Usage | Labor Rate | Labor Efficiency | Overhead.
Corrective Actions
- Recommended fixes for unfavorable variances (e.g., "Retrain shift B," "Search for new copper supplier").
Scripts
- calculate.py: Material, labor, and overhead variance functions. Run with
python3 scripts/calculate.pyto self-test; import the functions for actual computations.
References
- Variance Formulas: Quick look-up for all manufacturing variances.
- Absorption vs. Variable Costing: Understanding fixed overhead.
Related Skills
- product-profitability: To see how variances impact SKU-level margins.
- budget-forecast: For setting the "Standard Costs" for the next period.
- audit-checklist: For auditing the accuracy of production reporting.
What ships with it: 6 files
14.4 KB alongside SKILL.md, 1 of them executable
evals/
- evals.json3.1 KB
- files/production_data.xlsx5.4 KB
references/
- overhead-logic.md1.9 KB
- variance-math.md1.2 KB
scripts/
- calculate.pyruns1.9 KB
- EXAMPLE.md958 B