Machine Learning For Demand Forecasting Manufacturing
Supply Chain & Operations · Inventory Analytics
Python · Feature Engineering · Cross-Validation · Model Serving
Operational Focus: SKU-level multi-horizon demand forecasting and safety-stock optimization.
Project Abstract & Technical Scope
This project introduces an end-to-end, production-ready machine learning framework for Machine Learning For Demand Forecasting Manufacturing. Within real-world operational environments, systems face severe obstacles including zero-inflated intermittent demand, promotional price elasticity spikes, and upstream bullwhip oscillations. The objective of this work is to implement a robust, leak-free computational pipeline that translates raw inputs into deterministic, high-confidence decision metrics.
The system ingests and processes records sourced from historical point-of-sale SKU sales, promo discount calendars, supplier lead-times, out-of-stock logs, and weather indices. Raw attributes undergo automated data sanitization, multivariate imputation, distribution rebalancing, and outlier filtering. Continuous numerical features are scaled using robust statistical scaling techniques, while categorical, spatial, and temporal attributes receive cyclical encoding, high-cardinality target transforms, or dense embeddings.
Modeling evaluates diverse algorithmic paradigms: LightGBM with Lag Covariates, Prophet with Exogenous Regressors, SARIMAX, Croston's Intermittent Model. Rigorous validation protocols employ stratified, temporal, or grouped cross-validation to prevent train-test contamination. Hyperparameter optimization is systematically executed via Bayesian search strategies (Optuna), targeting optimization of WAPE (Weighted Absolute Percentage Error), MASE, Inventory Turnover Lift, Stockout Rate Reduction rather than uninformative global accuracy.
To ensure practical viability and regulatory transparency, global and local feature contributions are derived using TreeSHAP and Partial Dependence profiles. The winning configuration is serialized into portable ONNX format and served via an asynchronous FastAPI microservice equipped with telemetry logging for real-time concept drift detection.
Tools & Technologies
The standard modern data science stack utilized for feature extraction, model tuning, and REST deployment:
Modular Machine Learning Workflow
1. Data Governance & Cleaning
Schema validation, missing value imputation via MICE/KNN, and robust outlier filtering across historical point-of-sale SKU sales.
2. Feature Synthesis
Domain-specific interaction metrics, rolling lookback windows, and high-cardinality encoding without label leakage.
3. Competitive Benchmarking
Parallel evaluation across candidate models with Bayesian hyperparameter searches optimized for WAPE (Weighted Absolute Percentage Error).
4. Explainability & API Serving
SHAP force plots, residual error distribution auditing, and low-latency REST endpoints containerized for production.
Candidate Algorithms Benchmarked
- • LightGBM with Lag Covariates
- • Prophet with Exogenous Regressors
- • SARIMAX
- • Croston's Intermittent Model
Final production selection is based on cross-validated Pareto efficiency balancing WAPE (Weighted Absolute Percentage Error), MASE, Inventory Turnover Lift, Stockout Rate Reduction against inference latency.
Technical FAQ & Viva Preparation
End-to-End Implementation Workflow
Systematic engineering lifecycle from raw ingestion to deployable microservices.
& Ingestion
& Scaling
Engineering
Training
& CV
Explainability
Deployment
Monitoring