From Hidden PO Delays to Predictive Procurement: A Case Study in Supply Chain Intelligence

One-liner summary:
Industry
All Industries
Location
California, USA
Tech Stacks

Frontend: ReactJS

Backend: Python, Scikit Learn, Pandas

Cloud infrastructure: Azure

DevOps: Kubernetes, Docker, Git

Challenge

The semiconductor leader had deep SAP data but no way to predict PO delays before they hit production. Signals were scattered, dashboards were reactive, and procurement needed an explainable, zero-downtime forecasting system.

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The Problem with the Status Quo

The client, a Fortune 500 semiconductor manufacturer, relied on just-in-time procurement to maintain production velocity across a complex global supply chain. But as purchase orders delayed without early warnings, downstream operations suffered.

SAP ECC dashboards exposed lagging metrics but what the client needed was predictive foresight. Procurement leaders wanted a system that could automatically flag which vendors and parts were likely to delay delivery in the upcoming quarter, based on historical performance patterns, BOM complexity, and delivery behavior.

Without proactive intelligence, critical planning and production milestones were at risk.

Where the Gaps Were

Despite deep SAP data from BOMs, vendors, and item histories, the procurement team had no way to anticipate delivery risks before they impacted throughput. The existing dashboards were reactive at best. What they needed was predictive foresight that were grounded in explainable AI.

Ideas2IT uncovered four systemic gaps:

  • Dispersed Signals: Key predictors of delay were buried across SAP’s BOM, Vendor, and Item tables that were not directly usable for ML.
  • Data Imbalance: Some vendors and SKUs had far more data than others, introducing skew and limiting model generalizability.
  • Zero Downtime Requirement: Predictions needed to run weekly in a production environment, without interrupting procurement workflows.
  • Explainability Required: Procurement stakeholders needed to know why a delay was predicted and not just what. Black-box models were a nonstarter.

What We Delivered

We built a full-stack predictive procurement engine, spanning data engineering, custom AI model development, deployment on Azure Kubernetes, and dashboard interfaces for operations teams.

Key Implementation Highlights:

  • Custom AI Model with Interpretability
    Trained a Random Forest regression model on SAP ECC data, using engineered features like vendor reliability scores, item-level delivery deviation, and BOM complexity.
    Used Test-Time Augmentation (TTA) to improve inference accuracy and SHAP to explain each prediction.
  • Full Azure Deployment Pipeline
    Containerized the model with Docker, deployed to Azure Kubernetes Service with horizontal and vertical scaling, CI/CD automation, and zero-downtime rollbacks.
  • Integrated Forecasting Dashboard
    Built a ReactJS-based frontend for Procurement, Planning, and Ops teams to view vendor-wise and item-wise delay forecasts, updated weekly.

Outcomes We Achieved

Area Outcome
Billing cycle time Reduced from 45 days to fewer than 10 days
Release velocity Weekly rollouts enabled via CI/CD
Clinical data workflow Migrated to AI-assisted, browser-native environment
Partner integrations Expanded to 6+ platforms including ERP and CRM
Developer productivity Improved through service isolation and modular code

Procurement teams now receive a rolling forecast every week and flagging high-risk orders before disruptions occur, backed by feature-level insights they can act on.

Industry
All Industries
Location
California, USA
Tech Stacks

Frontend: ReactJS

Backend: Python, Scikit Learn, Pandas

Cloud infrastructure: Azure

DevOps: Kubernetes, Docker, Git

Challenge

The semiconductor leader had deep SAP data but no way to predict PO delays before they hit production. Signals were scattered, dashboards were reactive, and procurement needed an explainable, zero-downtime forecasting system.

‍

Key Takeaways

  • Prediction is only as good as the pipeline: The ability to retrain weekly on fresh SAP data without engineering overhead unlocked long-term sustainability.
  • Feature relevance trumps algorithm choice: Custom procurement features made the Random Forest outperform more complex black-box approaches.
  • ‍DevOps maturity drives AI impact: Without scalable deployment (AKS, CI/CD, TTA rollbacks), the model would’ve remained a POC.

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