A large Australian retail chain with 80+ stores struggled with inventory inefficiencies and delayed demand insights, relying on weekly reporting cycles.
- Services: AI-Powered Demand Forecasting & Inventory Intelligence
- Client: Large Australian Retail Chain (80+ stores)
- Location: Australia
- Completed Date: 20-12-2025
Business Problem
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Overstocking and stockouts due to delayed insights
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Weekly batch reporting limiting responsiveness
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No real-time demand forecasting capability
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Missed opportunities for personalised promotions
Solution Delivered by AIST
AIST implemented an AI-driven demand forecasting and inventory intelligence platform:
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Real-time data streaming using Spark Streaming
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Predictive forecasting models using Python
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Centralised data warehouse using Snowflake
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Advanced analytics dashboards via Tableau
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Cloud infrastructure deployed on Google Cloud Platform (GCP)
Architecture Overview
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Data Sources: POS systems, online sales, inventory data
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Streaming Layer: Real-time ingestion and processing
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Forecasting Engine: ML models for demand prediction
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Analytics Layer: Tableau dashboards for decision-makers
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Integration: Promotion and pricing systems
Key Capabilities Delivered
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Real-time demand forecasting
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Inventory optimisation recommendations
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Personalised promotion targeting
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Cross-channel analytics (online + in-store)
Business Outcomes
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22% reduction in overstock
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25% increase in repeat purchase rate
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Real-time analytics replacing weekly reporting
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Improved supply chain efficiency
Technology Stack
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Python
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Snowflake
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Spark Streaming
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Tableau
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GCP