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ManufacturingForecastingAISupply Chain

Inventory Chaos Tamed

Building a demand forecasting system for a manufacturing plant managing 10,000+ SKUs.

94%
Forecast Accuracy
10K+
SKUs Managed
67%
Stockout Reduction
01

The Challenge

A precision manufacturing company was drowning in inventory complexity. With over 10,000 SKUs, seasonal patterns, and long supplier lead times, their spreadsheet-based forecasting was creating costly stockouts and overstock situations.

  • Stockouts causing $3M+ in lost revenue annually
  • Excess inventory tying up $8M in working capital
  • Manual forecasting taking 40+ hours per week
  • No visibility into demand patterns across product lines
02

Our Approach

We built an intelligent forecasting platform that learns the unique demand patterns of each SKU while understanding cross-product relationships.

01

Pattern Discovery

Analyzed 5 years of historical data to identify seasonality, trends, and hidden correlations between product categories.

02

Hierarchical Forecasting

Developed a multi-level model that forecasts at SKU, category, and plant level—ensuring consistency across all aggregation levels.

03

Anomaly Detection

Built early warning systems to flag unusual demand signals, supplier risks, and potential stockout situations weeks in advance.

04

Planner Interface

Created an intuitive dashboard letting planners override, adjust, and add market intelligence to automated forecasts.

03

The Results

94%
Forecast Accuracy
At SKU level, up from 62% with manual methods
67%
Fewer Stockouts
Critical parts availability dramatically improved
$4.2M
Inventory Reduction
Working capital freed up in first 18 months
32hrs
Time Saved Weekly
Planners now focus on exceptions, not spreadsheets
"We went from firefighting stockouts daily to proactively managing our inventory. The system paid for itself in the first quarter."
Sarah Martinez
Director of Supply Chain
Technologies Used
PythonProphetTensorFlowSnowflakedbtStreamlitAirflow

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