📦 AI-Powered Supply Chain Simulation – Compare Cost, Speed, Risk, Resilience
Explore how AI transforms supply chain performance across four key dimensions. Select strategy below to simulate differences.
| Performance Factor | Traditional Model | AI-Driven Model | ROI Impact (%) |
|---|---|---|---|
| Cost Efficiency | Manual planning, siloed logistics → High overhead | AI route + load optimization → 12–18% cost savings | +8% |
| Delivery Speed | Static schedules → long lead times | Real-time dynamic scheduling → 25% faster delivery | +6% |
| Risk Management | Reactive to disruptions | Predictive analytics + scenario planning → proactive | +4% |
| Resilience | Low agility, central dependencies | AI-supported multi-sourcing + inventory buffers | +5% |
| Total ROI Improvement | +23% | ||
🚀 AI-Powered Sprint Project – Driving ROCE with Weekly Actions
- Week 1–2: Map critical cost and logistics points. Baseline KPIs (cost, speed, service level).
- Week 3–4: Implement AI route optimization pilot. Track efficiency gains.
- Week 5–6: Launch dynamic delivery scheduler. Integrate with existing ERP.
- Week 7–8: Activate predictive risk analytics. Start scenario drills.
- Week 9–10: Resilience boost: dual-source planning + smart buffers.
- Week 11: Consolidate dashboard KPIs. Compare traditional vs AI outcomes.
- Week 12: Present results. Lock-in ROCE gains. Plan enterprise rollout.