Enterprise POC: Validating AI Integration for a Fortune 500 Supply Chain
A 7-day proof of concept that validated whether AI could predict supply chain disruptions 2 weeks in advance using the company's existing ERP and logistics data.
Client: Confidential Fortune 500 Manufacturer
POC dashboard showing a world map with supply chain routes, highlighted risk zones in red/yellow, a prediction timeline with confidence bars, and a data quality assessment panel.
The Challenge
The manufacturer operated a global supply chain with 200+ suppliers across 15 countries. Supply chain disruptions (port delays, supplier quality issues, logistics failures) cost an average of $2.3M per incident, with 8-12 incidents per year. The VP of Supply Chain believed AI could predict disruptions by analyzing patterns in their ERP data (order history, lead time variations, quality scores) combined with external signals (shipping delays, weather, geopolitical events). But the C-suite wouldn't approve a $200K AI project without proof. They needed a fast, cheap POC that answered one question: 'Can AI predict disruptions 2 weeks in advance using our existing data?'
Our Approach
We structured the POC to answer the single question definitively. Day 1 was a data audit: we accessed their SAP ERP export (3 years of PO, delivery, and quality data) and assessed data quality, completeness, and signal strength. Day 2 through 3, we built a feature engineering pipeline that extracted predictive signals: lead time variance trends, supplier quality score trajectories, order volume anomalies, and seasonal patterns. Day 4 through 5, we trained a prediction model using Claude for pattern analysis on the engineered features, testing against known historical disruptions (the company had logged 28 incidents over 3 years). Day 6, we built a lightweight dashboard showing predictions vs actual disruptions on a timeline, with confidence scores and the top contributing factors for each prediction. Day 7 was the executive presentation: results, limitations, recommended next steps, and a go/no-go recommendation. The POC achieved 87% accuracy (24 of 28 historical disruptions predicted), with an average lead time of 16 days. The 4 missed disruptions were caused by sudden geopolitical events (sanctions, port closures) with no historical precedent.
What We Built
Delivery Timeline
Day 1: Data Audit
SAP ERP data access, quality assessment, completeness check, signal identification.
Day 2-3: Feature Engineering
Extract 12 predictive signals: lead time variance, quality trends, volume anomalies, seasonal patterns.
Day 4-5: Model Building
Prediction model training on 3-year historical data, backtest against 28 known disruptions.
Day 6: Dashboard
Executive dashboard with prediction timeline, confidence scores, and contributing factors.
Day 7: Presentation
Results presentation to C-suite, limitations disclosure, go/no-go recommendation, full project proposal.
Tech Stack
Architecture
frontend
Next.js lightweight dashboard for POC demonstration.
backend
Python (FastAPI) for data processing and model inference.
auth
Basic auth for POC access. Single-user demo.
data
PostgreSQL for processed features. Raw ERP data in local CSV.
ai
Claude for pattern analysis. scikit-learn for feature importance ranking.
Security
data
All data processed on-premises. No cloud uploads. Local-only processing.
access
POC dashboard accessible only on company VPN.
retention
All data and models deleted after POC presentation per NDA.
compliance
NDA signed. No proprietary data leaves company infrastructure.
The Results
“The board approved the full project 48 hours after seeing the POC. 87% accuracy on our own data, with 16 days of advance warning. That's $18M in potential savings annually. The $5K POC was the best investment we made all year.”
Key Takeaways
POCs must answer exactly one question. 'Can AI predict disruptions using our data?' Not 'What's the best AI approach?' Not 'How do we deploy this at scale?' One question, one answer, one week.
Backtest against known incidents, not synthetic data. The 87% accuracy meant something because it was tested against 28 real disruptions the company had documented.
The executive dashboard sells the full project. Numbers in a spreadsheet don't get approved. A visual timeline showing predictions vs. actual disruptions is worth $200K in consulting decks.
Deliverables
FAQ
Frequently Asked Questions
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