Full Truckload (FTL) vs. Part Truckload (PTL): When to Use Which
- FTL is cost‑effective for large, consolidated loads and offers higher control over transit time.
- PTL shines when volume is moderate, SKU mix is complex, or you need frequent deliveries across multiple destinations.
- Use Edgistify’s EdgeOS to model load patterns and Dark Store Mesh to convert PTL into near‑FTL efficiencies.
Introduction
In India’s e‑commerce landscape, logistics is the invisible engine that keeps the supply chain humming. From Mumbai’s bustling metro corridors to the tier‑2 towns of Guwahati, the choice between Full Truckload (FTL) and Part Truckload (PTL) can make or break margins. With cash‑on‑delivery (COD) still dominant and return‑to‑origin (RTO) rates high, choosing the right freight mode is a strategic decision that blends cost, speed, and reliability.
1. Understanding FTL vs. PTL
| Feature | Full Truckload (FTL) | Part Truckload (PTL) |
|---|---|---|
| Definition | One truck dedicated to a single shipment. | Multiple shipments share a truck. |
| Capacity Utilisation | 100 % (owner’s goods only). | 30‑70 % (mixed goods). |
| Transit Time | Predictable; no intermediate stops. | Variable; stops at multiple depots. |
| Control | High; you dictate pickup, route, schedule. | Low; dependent on carrier’s routing. |
| Cost Structure | Fixed per truck; cheaper per kg for large loads. | Variable per kg; higher per‑unit cost. |
2. Cost Dynamics
| Metric | FTL | PTL | Typical Scenario |
|---|---|---|---|
| Base Rate | ₹5 / km per truck | ₹8 / km per kg | |
| Additional Handling | Minimal | 3–5 % of cargo value | |
| Fuel Surcharge | Fixed per truck | Variable per stop | |
| COD Handling Fee | ₹15 per transaction | ₹25 per transaction | |
| RTO Charge | ₹20 per return | ₹30 per return |
When FTL Wins
- Volume > 10 t per shipment.
- Consistent SKU mix (e.g., same product line).
- Long haul to tier‑2 cities (Mumbai ➜ Jaipur).
When PTL Wins
- Volume < 10 t or highly variable.
- Multiple SKUs requiring different packaging.
- Shorter routes with frequent stops (Bangalore ➜ Hubli).
3. Speed & Reliability
| Factor | FTL | PTL |
|---|---|---|
| Transit Time | 1–2 days for inter‑city | 2–4 days (depends on stops) |
| On‑time Delivery | 95 % | 85 % |
| Damage Risk (NDR) | 0.5 % | 1.2 % |
| COD Collection Success | 97 % | 90 % |
> Data Note: A survey of 150 e‑commerce firms (2023) found that PTL shipments to tier‑2 towns had a 12 % higher RTO rate than FTL, largely due to multiple handovers.
4. Volume & SKU Complexity
| Scenario | Recommended Mode | Rationale |
|---|---|---|
| Single SKU, 15 t | FTL | Maximise truck utilisation. |
| Multiple SKUs, 6 t | PTL | Avoid under‑utilisation & keep packaging intact. |
| Seasonal spike (Diwali) | Hybrid (FTL for core, PTL for per‑item) | Balance volume with urgency. |
5. Use Cases by City & Courier
| City | Courier | Preferred Mode | Why |
|---|---|---|---|
| Mumbai | Delhivery | PTL | Dense traffic, multiple city‑wide stops. |
| Bangalore | Shadowfax | FTL | High‑value electronics needing secure transport. |
| Guwahati | Blue Dart | Hybrid | Long haul with occasional detours for RTO. |
6. Edgistify Integration
EdgeOS aggregates real‑time load data, fuel prices, and traffic feeds. By running a scenario model (e.g., 10 t from Mumbai to Jaipur), EdgeOS recommends the most cost‑efficient mix: 70 % FTL + 30 % PTL, saving ₹12 k/month.
Deploying dark stores in tier‑2 hubs allows PTL loads to be consolidated locally. A dark store in Surat can receive 5 PTL shipments, combine them into a single FTL leg to Pune, cutting per‑unit costs by 18 %.
NDR Management tracks damage rates across modes. For PTL, it flags carriers with >1 % NDR, prompting immediate re‑routing to a more reliable partner.
Strategic Recommendation 1. Model your typical shipment patterns in EdgeOS. 2. Cluster PTL loads around dark stores to approximate FTL volume. 3. Monitor NDR to keep damage rates below 0.7 %.
Conclusion
Choosing between FTL and PTL isn’t a binary decision—it’s a dynamic optimisation problem that hinges on volume, SKU complexity, city dynamics, and customer expectations. By leveraging Edgistify’s EdgeOS for data‑driven insights, Dark Store Mesh for local consolidation, and NDR Management for quality control, Indian e‑commerce players can strike the sweet spot: lower freight costs, higher on‑time delivery, and happier customers.