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Supply Chain &
SKU Forecast Analytics
ETL-powered production analytics with real-time event monitoring, SKU-level demand forecasting, and Agile workflow intelligence for a North American automotive parts manufacturer (General Motors supply chain).
A complex manufacturing operation struggled with massive data volumes from production lines, supply chain feeds, and ERP systems — but no meaningful way to analyze it. ETL processes were slow and brittle. Forecasting was done manually at aggregate level, missing SKU-level demand signals. Unplanned downtime was a persistent cost driver with no early warning system.
We re-engineered the entire ETL infrastructure using PySpark, dramatically reducing processing times. We built real-time event monitoring using PyWin32 socket connections that continuously tracked production line states, deployed SKU-level forecasting models using historical demand patterns, and designed production analytics dashboards that gave operations and plant managers real-time production visibility.
ETL processing time dropped by 85%, enabling near-real-time data availability. Unplanned production downtime fell 28% through event monitoring and early warning. Forecast errors reduced by 14% at SKU level, improving supply chain planning accuracy. Agile workflow improvements, informed by analytics, reduced team coordination overhead significantly.
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Key Results
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Technical Stack