Fordel StudiosSupply Chain Demand Forecaster
Ensemble demand forecasting with live supplier risk signals — not just historical averages.
The problem
being solved
A manufacturer sourcing 200+ components from 50 suppliers across 3 continents uses quarterly forecasts and static safety stock. When demand shifts or a supplier disrupts, the team scrambles for alternatives and expedites — often learning weeks late.
The digital supply chain tech market is projected at $147B by 2031 from $72B in 2025. Blue Yonder shows 20% inventory cost reduction. Project44 and FourKites cut late deliveries by 25% with predictive visibility.
The fundamental problem: demand forecasting, supplier risk, and supply planning operate as separate functions. The demand planner does not see risk data. Procurement does not see forecast updates. Decisions are made on partial information.
How this
agent works
This agent unifies forecasting, monitoring, and planning. Demand forecasting uses ensemble methods: statistical time-series, ML pattern detection, and external signals (economic indicators, weather). SKU-level forecasts with confidence intervals.
Supplier monitoring tracks risk continuously: news near facilities, port congestion, weather on shipping lanes, financial health, and your own supplier performance metrics. Events are mapped through your supplier network graph.
Supply plans integrate both. When forecast shows rising demand for a product whose key component comes from a high-risk region, the plan adjusts: increase safety stock, qualify alternatives, or pre-position buffer inventory.
We integrate directly with your ERP (SAP, Oracle, NetSuite), supplier portals, and external data feeds — port activity, weather APIs, financial filings — using Apache Kafka for real-time event ingestion and FastAPI for the planning API layer. Demand models run LightGBM and Prophet in ensemble, trained on 24+ months of SKU-level history and retrained continuously as new data arrives. Supplier relationships and multi-tier dependencies are modeled as a graph in Neo4j, so component-level exposure from a Tier 2 disruption surfaces automatically. Integration and calibration runs 4–5 weeks; PostgreSQL and Redis handle forecast state and caching.
- 01
Ensemble Forecasting
Combines LightGBM, Prophet, and external signal inputs — promotions, seasonality, macroeconomic indicators — into a single SKU-level forecast with confidence intervals. Models retrain incrementally as new sales and inventory data flows in, so accuracy improves over time rather than drifting.
- 02
Supplier Risk Monitoring
Ingests news feeds, port congestion data, weather events, and supplier financial filings via Kafka and maps each signal to your specific component network in Neo4j. When a port slowdown hits a region, you see which SKUs are exposed within minutes, not after a buyer raises a flag.
- 03
Integrated Supply Planning
Generates replenishment plans that treat demand uncertainty and supplier reliability as joint variables — not sequential steps. Safety stock targets are calculated per SKU using forecast confidence intervals and that supplier's historical lead time variance, so you're not padding buffers uniformly across the board.
- 04
Multi-Tier Network Visibility
Maps your supply network beyond direct (Tier 1) suppliers by inferring Tier 2 and Tier 3 relationships from purchasing records and supplier declarations stored in Neo4j. Flags single-source dependencies and geographic concentration risks — the kind of exposure that only becomes visible after a disruption hits.
Build this agent
for your workflow.
We custom-build each agent to fit your data, your rules, and your existing systems.
Free 30-min scoping call