# AI supply-chain optimisation for global electronics manufacturing

Supplier delays, inventory imbalances and transport bottlenecks left plants idle for missing parts while warehouses overflowed. A supply-chain command centre that predicts disruptions, simulates what-if scenarios and recommends inventory, carrier and production moves.

- **Sector**: Consumer electronics
- **Region**: Global
- **Disciplines**: Machine learning · Data platform · Optimisation
- **Practices**: [AI & machine learning](https://www.winapps.co.in/services/ai-ml/), [Data & analytics](https://www.winapps.co.in/services/data-analytics/)
- **Client**: Under NDA. References available on a call.

## The challenge

A consumer electronics manufacturer with factories across Asia, Europe and North America saw its supply chain buckle under fast demand growth. Its ERP reported what had already happened but couldn't anticipate disruptions from weather, geopolitics or component shortages, and planners spent most of their time fire-fighting in spreadsheets.

## Our approach

We built a digital twin of the supply chain that fuses ERP, IoT, weather, news and shipment-tracking data. A graph model of supplier relationships scores how risk travels across tiers, demand sensing improves the forecast, and optimisation models recommend inventory positions, carriers and production schedules across plants.

Planners stress-test scenarios such as port strikes or tariff changes and get ranked contingency plans. Suppliers share capacity through a collaboration portal, and each role (plant, logistics, leadership) gets its own view of exceptions and recommended actions.

## Architecture

- **Sources**: ERP, IoT sensors, Weather and news feeds, Shipment trackers
- **Data**: Multi-source data fusion, Supply-chain digital twin
- **Intelligence**: Supplier risk graph model, Demand sensing, Inventory and routing optimisation, Scenario simulation
- **Users**: Planner exception alerts, Supplier portal, Executive dashboards

Illustrative architecture, simplified to protect client confidentiality.

## Results

Outcome details for this engagement are shared under NDA. We're happy to walk through results and put you in touch with a reference on a call.

## Stack

- Graph neural networks
- Reinforcement learning
- Digital twin
- React Native

Next case study: [AI vision quality control on high-speed electronics lines](https://www.winapps.co.in/work/ai-vision-electronics-inspection/) ([Markdown](https://www.winapps.co.in/work/ai-vision-electronics-inspection.md))

## Facing something similar?

A senior engineer, not a salesperson, replies within one business day. NDA available before any detailed discussion.

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