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Logistics

Supply Chain Demand Prediction

Client: Glasgow Logistics Ltd

98% Accuracy Impact
01. Overview

A major logistics provider in Glasgow struggling with overstocking and stockouts due to unpredictable demand.

02. The Challenge

Manual Excel-based forecasting was resulting in 15% annual stock wastage and missed delivery targets during peak seasons.

03. Our Approach

We implemented a Machine Learning regression model trained on 5 years of historical sales and seasonal data.

04. Implementation
  • Cleaned and normalized 500GB of historical data.
  • Built a dashboard using Streamlit for warehouse managers.
  • Automated weekly re-ordering triggers.
Solution Architecture

Visual representation of data flow and AI integration.

Key Results

-13%

Stock Wastage

Reduced from 15% to 2%

99%

Delivery Rate

On-time delivery achieved

300%

ROI

Return on investment in 6 months

Technologies Used
Scikit-Learn Pandas PowerBI AWS

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