Case Study: Predictive Inventory Forecasting with Azure OpenAI

Ryse Technologies implemented an Azure-native AI platform combining a fabric-based data lake, fine-tuned LLM, and retrieval-augmented generation (RAG) to deliver daily demand forecasts.

May 28, 2025

By: Steven Settle

Contents

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Client: Multinational Industrial Services Provider

Partner: Ryse Technologies

Solution: AI-powered Bill of Materials (BOM) Forecasting Platform

The Challenge

Planning high-volume, equipment-intensive turnarounds is risky when:
- Lead times exceed 30 weeks
- Projects span 1000+ SKUs
- Only 400 past jobs exist for training
- Data is siloed across Salesforce + homegrown trackers

Traditional ML models failed due to data sparsity, complex BOMs, and fragmented systems—resulting in costly last-minute orders and overstock.

The Solution

Ryse Technologies implemented an Azure-native AI platform combining a fabric-based data lake, fine-tuned LLM, and retrieval-augmented generation (RAG) to deliver daily demand forecasts.

Key Components:

- Data Ingestion: Azure Data Factory, Databricks

- Storage: Fabric Lakehouse + Azure SQL

- AI Intelligence: Fine-tuned Azure OpenAI model

- Orchestration: Azure Functions, Power Automate

- Visualization: Power BI inventory heatmaps

How It Works:
1. Unified Salesforce and historical BOM data into a curated lakehouse
2. LLM interprets upcoming opportunities + auto-generates draft BOMs
3. Daily Azure Functions update 12-month SKU-level demand curves
4. Power BI alerts procurement when items near safety thresholds

Results & Impact

Metric Before After
SKU Lead-Time Coverage <40% 92% (6-month horizon)
Safety Stock Ratio Up to 10× ≤1.5× on top 200 SKUs
Planning Cycle Manual & ad hoc Fully automated, nightly

Qualitative Wins:

✅ A single, trusted forecast used by Sales, Ops & Procurement

✅ LLM + RAG outperformed ML in low-data, high-complexity environments

✅ Future-proof Azure stack that scales across facilities

What’s Next (Phase 2+)

- Feedback-driven model tuning (Git-integrated coaching)

- Warehouse space forecasting

- Sentiment-adjusted deal probabilities

- Photo & diagram ingestion

- AI-driven procurement suggestions

Takeaway

When data is shallow but context is rich, LLMs + RAG on Azure can outperform traditional AI—delivering smarter, leaner, and faster decisions across the enterprise.

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