Problem Statement

Supply chain provider and its clients depend on a diverse network of regional and last‑mile carriers. Some carriers consistently miss SLA targets (e.g., delays, temperature excursions, damaged goods). Problems are detected after the fact—once penalties, refunds, or client churn are in motion. Leaders lack a unified, data‑driven capability to quantify risk, predict unreliability, and recommend better carrier or routing options.

• Reactive detection: SLA failures surface post‑incident, driving penalties and churn.
• Fragmented insight: Carrier performance, routes, contracts, and shipment histories are siloed.
• Limited foresight: No forward‑looking, risk‑adjusted guidance on which carrier or lane to choose.

Proposed Solution

1. Graph Database Layer: Models carriers ↔ routes ↔ shipment histories ↔ SLA performance ↔ contracts to reveal hidden dependencies and systemic underperformance across the network.
2. ML Predictive Models: Forecast likelihood of SLA breaches by carrier, route, lane, and seasonality; produce risk scores to guide allocation and routing.
3. GenAI / RAG Assistant: Natural‑language queries and prescriptive recommendations with evidence traceability.

   • “Which carriers are most likely to miss SLA next quarter?”
   • “What is the financial impact of continuing with Carrier X on Route Y?”
   • “Which alternatives improve compliance at minimal added cost?”

Example User Scenarios

• Risk Forecasting: Predict Carrier A has a 70% probability of SLA breach on Route B in Q4.
• Optimization Recommendation: Replace Carrier A with Carrier C (96% historical SLA compliance) or reroute via Lane D for higher reliability.
• Strategic Planning: Rank carriers by risk‑adjusted performance to drive procurement and contract negotiations.

Business Value

Where:

• SLA Penalties Avoided = Annual Penalties × Reduction %
• Client Retention Value = Revenue at Risk × Retention Improvement %
• Operational Efficiency Savings = Incident Management Hours × Hourly Rate × Reduction %

Sample Calculation (assumptions):

• SLA penalties: $10M/year → 40% reduction = $4.0M saved
• Client churn risk: $50M/year revenue at risk → 5% improvement = $2.5M saved
• Analyst workload: 15,000 hours/year @ $80/hour → 50% reduction = $0.6M saved

≈ $7.1M Annual Business Value

Key Metrics (Expected Impact)

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Strategic Impact

• Improved SLA reliability: Fewer disruptions and higher client satisfaction.
• Cost avoidance: Reduced penalties, refunds, and hidden remediation costs.
• Procurement leverage: Data‑driven negotiations with carriers using risk‑adjusted performance.
• Resilient network: Proactive re‑routing and allocation before disruptions materialize.

Strategic Fit: Elevates carrier management from hindsight analysis to proactive, risk‑aware optimization—improving reliability, cost, and client trust at scale.