JourdanLabs

FFRO Case Study

A systems-driven portfolio of product innovation, prototypes, and technical product management by Leland Jourdan II.

Systems thinkingProduct & ArchitecturePrototypes in production

Fulfillment Forecast & Routing Optimizer (FFRO)

AI-assisted capacity forecasting and routing recommendations for Amazon Operations.

Leland Jourdan II • JourdanLabs • 2025

Network Complexity & Challenges

  • Rapid Fluctuations: Regional demand spikes, carrier availability changes, and weather disruptions.
  • Current Reality: Planning relies on static dashboards, manual forecasting, and spreadsheets.
  • The Problem: Bottlenecks are discovered too late, with no clear prioritization.
  • The Need: A shift from reactive firefighting to proactive, AI-driven intelligence.

FFRO Solution Overview

  • Predicts SLA Risk: Identifies potential service level failures before they occur.
  • Surfaces Bottlenecks: Region-by-region capacity imbalance detection.
  • Recommends Routing Shifts: Suggests concrete percentage adjustments to increase efficiency.
  • Rapid What-If Scenarios: Planners test assumptions instantly.

User Personas

Regional Operations Planner

Primary User: Needs early warnings of capacity risks and routing recommendations.

Pain Points: Overwhelming data volume and time pressure.

Fulfillment Center Manager

Secondary User: Needs to understand how volume shifts affect local FC utilization.

Pain Points: Reactive planning and lack of upstream visibility.

Last-Mile Delivery Supervisor

Tertiary User: Needs forecasts on DSP capacity and bottleneck identification.

Pain Points: Late notification of volume surges.

How FFRO Works

Input

  • Origin FC(s)
  • Destination Regions
  • Time Window (1–14 days)
  • Optional Assumptions

AI Processing

  • Forecast Demand
  • Regional Capacity
  • Carrier Availability
  • Historic Bottlenecks

Output

  • Risk Score (0–100)
  • Regional Breakdown
  • Routing Recommendations
  • Interactive “What-If” Scenarios

Actionable Recommendations

  • Region 7A: 84% SLA stress probability. Capacity shortfall of 12%.Recommendation: Shift 2.5% of load to Region 7D.
  • FC CLTI: Expected to exceed optimal load by 9%.Recommendation: Rebalance 11% of outbound volume to RDU2.
  • Carrier Blue Logistics: 15% lower reliability than baseline.Recommendation: Route 18% of volume to carriers X and Y.

MVP Core Features

  • Risk Prediction: SLA failure identification via minimal inputs.
  • Routing Recommendations: Data-driven regional and carrier shifts.
  • Scenario Simulation: Instant recalculation of risk based on adjustments.

User Experience Flow

  1. Input Entry — Enter constraints and forecast assumptions.
  2. Analyze — AI engine processes data.
  3. Results — View risk score & recommendations.
  4. Scenario Test — Adjust assumptions & rerun.
  5. Export & Action — Share results & implement shifts.

Business Impact & Metrics

  • Reduces SLA Breaches: Predictive alerts reduce surprises 3–14 days ahead.
  • Increases Planning Efficiency: 50% reduction in manual scenario testing.
  • Improves Routing Decisions: Structured recommendations optimize cost & speed.
  • Standardizes Excellence: 90% planner adoption rate across regions.

Future Roadmap

  • v1.0 MVP (8–12 weeks): Core risk prediction & routing intelligence.
  • v1.5 Enhanced Data (+12 weeks): Real data integration & region-specific models.
  • v2.0 Advanced Intelligence (+16 weeks): Multi-region optimization & daily digests.
  • v3.0 Full Platform (+20 weeks): Simulation engine, ML models, full Ops integration.