Optimizing Supply Chain Efficiency with ML-Based Demand Forecasting
Industries
Retail
Services
Machine Learning Forecasting, Supply Chain Optimization
Tools We used
Python, PySpark, Databricks, Airflow, Sarimax, FB Prophet, XGBoost, R Shiny
Challenges We Faced
- Unoptimized Supply Chain Operations – The client faced inefficiencies in labor usage, truck waiting times, and truck load capacity due to a lack of accurate demand forecasting across distribution centers and retail stores.
- Inefficient Warehouse Space Utilization – Without predictive visibility, managing warehouse inventory and movement was reactive and inconsistent.
- Vendor Planning & Resource Gaps – Poor demand insight led to suboptimal contracts with third-party logistics providers and unclear projections for truck and equipment needs.
The client required a machine learning-driven solution to forecast demand accurately and improve operational decisions across their national logistics network.
WhizzBridge’s Solution
- End-to-End ML Forecasting Framework – Built a custom pipeline to forecast truck inflow/outflow using a combination of SARIMAX, FB Prophet, and XGBoost models.
- Robust Data Processing Pipeline – Developed scalable ETL using PySpark and Airflow to perform comprehensive EDA, data cleansing, and feature engineering.
- Model Experimentation & Selection – Tested multiple algorithms and selected models based on performance, achieving over 90% prediction accuracy.
- Forecast Range Flexibility – Enabled forecast windows ranging from 4 to 54 weeks, accounting for external variables such as holidays and seasonality.
- Visual Analytics Dashboards – Created interactive dashboards using R Shiny, giving stakeholders real-time visibility into trends and forecast projections.
- Scalable Cloud-Based Infrastructure – Hosted the solution on Databricks, allowing seamless model training, collaboration, and future scalabilit
Results We Achieved
- Cost Reduction Through Labor Optimization – Improved planning reduced labor redundancy and boosted workforce efficiency.
- Optimized Truck Routing & Load Planning – Enabled data-driven decisions on truck movements, minimizing idle time and maximizing capacity.
- Improved Warehouse Space Utilization – Helped balance inventory and streamline warehouse flow based on accurate demand forecasts.
- Strategic Vendor Management – Provided better visibility to renegotiate contracts with third-party logistics providers using predictive data.
The client now benefits from a scalable, ML-powered supply chain forecasting engine that enhances planning, reduces costs, and supports long-term growth.
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