Warehouse Operations Analytics

Features

• Predictive analytics for shipment delay detection using machine learning models
• Interactive warehouse performance dashboards and operational visualisations
• End-to-end data pipeline including preprocessing, cleaning, and feature engineering
• Exploratory data analysis for identifying operational bottlenecks and trends
• Real-time style KPI visualisation using Streamlit and Plotly
• Data-driven insights into warehouse throughput, processing time, and delivery performance

Challenges

• Preventing data leakage when training predictive models using operational timestamps.
• Cleaning and structuring inconsistent warehouse operational datasets.
• Selecting meaningful features that realistically exist before shipment completion.
• Balancing dashboard responsiveness with large-scale data processing workflows.
• Interpreting operational trends while avoiding misleading correlations.

Learnings

• Practical experience building machine learning pipelines for operational analytics.
• Improved understanding of feature engineering and predictive modelling limitations.
• Experience designing interactive data visualisation dashboards for industrial use cases.
• Hands-on application of operational data analysis and warehouse performance monitoring.
• Deeper understanding of real-world challenges such as noisy datasets and data leakage.

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