AI in Retail Supply Chain: Reducing Food Waste & Boosting Sales with Azure Databricks

Introduction: The Hidden Cost of Food Waste in Retail

Retailers worldwide are facing a silent crisis—massive food waste and missed revenue due to outdated forecasting and poor supply chain visibility. The retail industry discards millions of tons of perishable goods every year, largely because of reactive planning and siloed data systems.

At Huebits Tech, we took on this challenge using a cloud-native AI architecture powered by Azure Databricks and Snowflake. The result? Over 50% waste reduction for key SKUs and sustained sales performance, even in volatile markets.

This blog outlines how we achieved these results—and how you can too.

Executive Summary

Objective

Reduce food waste and enhance sales through AI-powered demand forecasting and cross-tier collaboration across supply chains.Technology Used: Azure Databricks, Azure Blob Storage, Snowflake, Azure OpenAI Service

Technology Stack

• Cloud Platform: Microsoft Azure
• Data Lake: Azure Blob Storage
• Analytics Engine: Azure Databricks
• Data Warehouse: Snowflake on Azure
• AI Models: Azure OpenAI Service

Key Outcomes

50%+ reduction in perishable food waste

Stable or improved sales across AI-managed SKUs

Forecasting accuracy on par with senior human planners

The Challenge: Bullwhip Effect & Siloed Forecasting

The global food industry is plagued by the bullwhip effect—where small changes in consumer demand cause large swings upstream in supply planning. Traditional, disconnected systems only make this worse:

• No real-time data sharing between manufacturers, distributors, and retailers
• Manual or simplistic forecasting approaches
• Overproduction to “play it safe,” leading to spoilage

Our goal was to replace fragmented planning with a shared AI-driven forecasting system that provides accurate, adaptive insights across the value chain.

Solution Architecture: End-to-End AI Forecasting on Azure

Core Technologies Used

System Components

1. AI Demand Forecasting
   - Time-series + multi-pattern ML models
   - Historical ID-POS data (Item, Date, Store)
   - Handles perishables with short shelf lives
2. Shelf Layout Optimization
   - Uses sales velocity + visibility data
   - Dynamically adjusts product placements
3. Latent Demand Modeling
   - Identifies "invisible" lost demand during low traffic
   - Helps maintain sales volume under adverse conditions
4. Order Automation System
   - AI-driven triggers for replenishment
   - Reduces stockouts and overstock

Implementation Timeline

Business Impact: From Theory to Measurable Value

1️⃣ Waste Reduction

• 50%+ decrease in spoilage for key perishable categories
• Up to 90% drop in manufacturer-level waste for select SKUs

2️⃣ Sales Stability and Growth

• AI-managed items maintained or grew sales during market slowdowns
• Latent demand models uncovered new revenue streams previously invisible

3️⃣ Forecasting Accuracy

• AI models matched or outperformed expert planners with 3+ years’ experience
• Accuracy improved further with continuous model retraining

Future Directions

Building on the success of this deployment, Huebits plans to:

Expand forecasting to non-perishable inventory categories

Use Generative AI to build autonomous, self-optimizing ordering systems

Create deeper B2B integrations across manufacturer and distributor systems

Lessons Learned & Recommendations

Start with good data – Clean, consistent POS and inventory data are crucial

Focus on collaboration – AI works best when retailers and suppliers share data

Iterate continuously – Forecasts improve with more cycles and feedback

Adopt cloud-native architecture – Scale and retrain without infrastructure constraints

Frequently Asked Questions (FAQs)

Q: How does AI reduce food waste in retail?

AI improves demand forecasting using historical patterns, seasonality, and real-time trends—allowing precise inventory management and reduced overproduction.

Q: Why use Azure Databricks for this solution?

Databricks offers a scalable, unified platform for real-time data processing and model training—perfect for handling large retail datasets efficiently.

Q: Can this solution scale to other industries?

Yes! While this case focused on perishables in retail, similar architectures work in healthcare, manufacturing, and e-commerce for supply optimization.

Let’s Build Your Smart Supply Chain

At Huebits, we combine domain expertise with cutting-edge AI tools to modernize supply chains, reduce waste, and uncover new business opportunities.