Throughput Accountant
by a5c-ai
TOC financial metrics skill for throughput, inventory, and operating expense analysis with decision support
Skill Details
Repository Files
1 file in this skill directory
name: throughput-accountant description: TOC financial metrics skill for throughput, inventory, and operating expense analysis with decision support allowed-tools:
- Read
- Write
- Glob
- Grep
- Edit metadata: specialization: operations domain: business category: theory-of-constraints
Throughput Accountant
Overview
The Throughput Accountant skill provides comprehensive capabilities for applying Theory of Constraints financial metrics. It supports throughput, inventory, and operating expense analysis to drive better operational and investment decisions.
Capabilities
- Throughput calculation (T)
- Inventory valuation (I)
- Operating expense tracking (OE)
- Net profit computation
- ROI analysis
- Product mix optimization
- Make vs. buy decisions
- Investment justification
Used By Processes
- TOC-003: Throughput Accounting Analysis
- TOC-001: Constraint Identification and Exploitation
- CAP-001: Capacity Requirements Planning
Tools and Libraries
- Financial analysis tools
- ERP integration
- Decision support systems
- Optimization software
Usage
skill: throughput-accountant
inputs:
products:
- name: "Product A"
selling_price: 100
raw_material_cost: 30
constraint_time: 10 # minutes
- name: "Product B"
selling_price: 150
raw_material_cost: 60
constraint_time: 20 # minutes
constraint_capacity: 480 # minutes per day
operating_expenses: 5000 # per day
investment_options:
- description: "Add second constraint machine"
cost: 100000
throughput_increase: 50 # percent
outputs:
- throughput_per_product
- constraint_utilization
- product_prioritization
- profitability_analysis
- investment_roi
TOC Financial Measures
Throughput (T)
T = Sales Revenue - Totally Variable Costs
Where Totally Variable Costs = Raw materials, piece-rate labor, sales commissions
Inventory (I)
I = All money invested in things intended for sale
Note: Does not include value-added labor or overhead
Operating Expense (OE)
OE = All money spent turning Inventory into Throughput
Includes: Labor, utilities, depreciation, overhead
Key Metrics
| Metric | Formula | Purpose |
|---|---|---|
| Net Profit | NP = T - OE | Overall profitability |
| Return on Investment | ROI = NP / I | Investment efficiency |
| Productivity | P = T / OE | Operational efficiency |
| Inventory Turns | Turns = T / I | Cash flow velocity |
Decision Rules
Product Mix Optimization
Throughput per Constraint Unit = (Price - TVC) / Constraint Time
Prioritize products with highest T/CU
Make vs. Buy Decision
If Buy Price < (TVC + Constraint Usage Value)
Then BUY
Else
MAKE
Investment Justification
Payback Period = Investment Cost / (Delta T - Delta OE)
Accept if Payback Period < Target
Integration Points
- ERP financial modules
- Cost accounting systems
- Product profitability systems
- Capital planning tools
Related Skills
Attack Tree Construction
Build comprehensive attack trees to visualize threat paths. Use when mapping attack scenarios, identifying defense gaps, or communicating security risks to stakeholders.
Grafana Dashboards
Create and manage production Grafana dashboards for real-time visualization of system and application metrics. Use when building monitoring dashboards, visualizing metrics, or creating operational observability interfaces.
Matplotlib
Foundational plotting library. Create line plots, scatter, bar, histograms, heatmaps, 3D, subplots, export PNG/PDF/SVG, for scientific visualization and publication figures.
Scientific Visualization
Create publication figures with matplotlib/seaborn/plotly. Multi-panel layouts, error bars, significance markers, colorblind-safe, export PDF/EPS/TIFF, for journal-ready scientific plots.
Seaborn
Statistical visualization. Scatter, box, violin, heatmaps, pair plots, regression, correlation matrices, KDE, faceted plots, for exploratory analysis and publication figures.
Shap
Model interpretability and explainability using SHAP (SHapley Additive exPlanations). Use this skill when explaining machine learning model predictions, computing feature importance, generating SHAP plots (waterfall, beeswarm, bar, scatter, force, heatmap), debugging models, analyzing model bias or fairness, comparing models, or implementing explainable AI. Works with tree-based models (XGBoost, LightGBM, Random Forest), deep learning (TensorFlow, PyTorch), linear models, and any black-box model
Pydeseq2
Differential gene expression analysis (Python DESeq2). Identify DE genes from bulk RNA-seq counts, Wald tests, FDR correction, volcano/MA plots, for RNA-seq analysis.
Query Writing
For writing and executing SQL queries - from simple single-table queries to complex multi-table JOINs and aggregations
Pydeseq2
Differential gene expression analysis (Python DESeq2). Identify DE genes from bulk RNA-seq counts, Wald tests, FDR correction, volcano/MA plots, for RNA-seq analysis.
Scientific Visualization
Meta-skill for publication-ready figures. Use when creating journal submission figures requiring multi-panel layouts, significance annotations, error bars, colorblind-safe palettes, and specific journal formatting (Nature, Science, Cell). Orchestrates matplotlib/seaborn/plotly with publication styles. For quick exploration use seaborn or plotly directly.
