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
Diagram illustrating steps to effectively use supply management software for inventory control and logistics.

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.

Image illustrating digital transformation in supply chain management using machine learning for business efficiency.

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
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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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