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variance-analysis

variance-analysis

github.com/gajetosoData & databases4550 installsUpdated 4w ago
Repository

About variance-analysis

variance-analysis is a skill for plant controllers analyzing differences between standard and actual production costs. It organizes material, labor, and overhead variances and supports root-cause review and corrective actions.

Key features

  1. 01Material price and usage variances
  2. 02Labor rate and efficiency variances
  3. 03Fixed overhead variance review
  4. 04Root-cause analysis prompts
  5. 05Production variance report format

Use cases

  1. 01Investigating higher-than-budget material costs
  2. 02Reviewing labor efficiency against standards
  3. 03Identifying fixed overhead absorption variances

Install

Copy the command and run it in a terminal.

$npx skills add https://github.com/gajetoso/financeskills --skill variance-analysis

After installing, type/variance-analysisin Claude Code to confirm it loaded.

Bundled files

References and scripts installed along with SKILL.md.

EXAMPLE.md
evals/
evals.json
evals/files/
production_data.xlsx
references/
overhead-logic.md
variance-math.md
scripts/
calculate.py

Details

From the project README

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

  1. 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.

  2. 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
  1. Price Variance: (Standard Price - Actual Price) * Actual Quantity.
  2. Usage Variance: (Standard Quantity - Actual Quantity) * Standard Price.
Labor Variances
  1. Rate Variance: (Standard Rate - Actual Rate) * Actual Hours.
  2. 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.py to self-test; import the functions for actual computations.

References

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.

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