Tracing costs to their causal drivers for accurate product and customer costing
Process Costing Engine addresses a critical challenge in modern supply chain management: cost management. This implementation combines rigorous academic methodology with production-ready Python code, suitable for both research and enterprise deployment.
Built on the foundational work of Professor Charles Horngren, this tool provides supply chain professionals with an analytical framework that transforms raw operational data into actionable optimization decisions. Whether you're managing a single warehouse or a global multi-echelon network, this toolkit scales to your complexity.
The solution follows industry best practices from APICS/ASCM, CSCMP, and ISM frameworks, implemented with clean, extensible Python code that integrates with existing ERP, WMS, and TMS systems.
Key capabilities:
- Activity-based costing with multi-level cost pools
- Product-level and customer-level profitability analysis
- Standard vs. actual variance decomposition
- Cost driver identification and rate computation
- Profitability waterfall visualization
flowchart LR
A[📥 Input\nData] --> B[⚙️ Processing &\nAnalysis]
B --> C[🔢 Optimization\nEngine]
C --> D[📊 Results &\nMetrics]
D --> E[📋 Recommendations\n& Actions]
style C fill:#fff9c4
style E fill:#c8e6c9
Supply chain cost management is a persistent operational challenge that impacts cost, service, and working capital across the enterprise. Organizations that fail to optimize cost management typically see:
| Impact Area | Without Optimization | With Optimization | Improvement |
|---|---|---|---|
| Cost | Baseline | 15-30% reduction | Significant |
| Service Level | 85-90% | 95-99% | +5-14 pts |
| Working Capital | Over-invested | Right-sized | 20-40% freed |
| Decision Speed | Days/weeks | Minutes/hours | 10-50x faster |
"The goal is not to optimize individual functions, but to optimize the entire supply chain system — which often means sub-optimizing individual nodes for the benefit of the whole."
This implementation follows a structured analytical approach:
- Data Ingestion & Validation — Load operational data, validate completeness, handle missing values and outliers
- Exploratory Analysis — Statistical profiling, distribution analysis, correlation identification
- Model Construction — Build the optimization/analytical model with configurable parameters and constraints
- Solution Computation — Execute the algorithm with convergence checking and solution quality metrics
- Results & Recommendations — Generate actionable outputs with sensitivity analysis and implementation guidance
| Requirement | Version |
|---|---|
| Python | 3.8+ |
| pip | Latest |
git clone https://github.com/virbahu/process-costing-engine.git
cd process-costing-engine
pip install -r requirements.txt
python process_costing_engine.py# Quick start example
from process_costing_engine import *
# Run with default parameters
result = main()
print(result)
# Customize parameters
# See docstrings in process_costing_engine.py for full parameter referencenumpy
scipy
pandas
matplotlib
| Based on | Professor Charles Horngren, Stanford |
| Key Reference | Horngren et al. (2015) Cost Accounting: A Managerial Emphasis. Pearson |
| Domain | Cost Management |
Virbahu Jain — Founder & CEO, Quantisage
Building the AI Operating System for Scope 3 emissions management and supply chain decarbonization.
| 🎓 Education | MBA, Kellogg School of Management, Northwestern University |
| 🏭 Experience | 20+ years across manufacturing, life sciences, energy & public sector |
| 🌍 Scope | Supply chain operations on five continents |
| 📝 Research | Peer-reviewed publications on AI in sustainable supply chains |
MIT License — see LICENSE for details.
Part of the Quantisage Open Source Initiative | AI × Supply Chain × Climate