DPD Polska
Engagement summary
| Industry | LSP B2C — parcel / courier (European) |
| Region | Poland |
| Engagement type | Route optimization + address intelligence + predictive CX |
| Status | Live |
The business problem
DPD Polska is one of the largest parcel operators in Europe. Three operational challenges were eroding margins and customer satisfaction simultaneously:
- Manual load balancing. Driver route planning relied on tribal knowledge — experienced dispatchers knew the territory, new ones didn’t. Routes were suboptimal, and the knowledge didn’t scale.
- No live ETA. Customers had no real-time visibility into when their parcel would arrive, leading to high “where is my parcel?” contact volume.
- Low delivery-window adherence. Only ~30% of deliveries landed within the promised time window. The rest generated complaints, redelivery attempts, and cost.
The solution we delivered
Shipsy deployed three capabilities working together:
| Capability | What it does |
|---|---|
| Zoning + micro-cluster route optimization | Replaces tribal-knowledge dispatching with algorithmic route planning. Groups deliveries into micro-clusters and optimizes sequence within each. |
| Predictive CX | Real-time ETA pushed to customers. Reduces “where is my parcel?” calls before they happen. |
| Address intelligence | Validates and normalizes addresses before dispatch. Catches bad data before it becomes a failed delivery. |
Results
| Metric | Before | After | Impact |
|---|---|---|---|
| Driver productivity | Baseline | +10-15% | Fewer km per delivery |
| Delivery window adherence | ~30% | 90%+ | 3× improvement |
| Estimated annual savings | — | USD 37M/year | Fuel, labor, redelivery |
Why CS folks should study this
Three transferable patterns:
- Route optimization as the wedge. DPD didn’t buy “AI agents” — they bought operational efficiency. The AI conversation came later. For LSP B2C customers, lead with route optimization + address intelligence, expand to agents.
- The 30% → 90% story. Delivery-window adherence is a metric every B2C LSP cares about. This case study is the proof point to cite.
- Tribal knowledge → algorithmic. Most logistics operations run on experienced dispatchers who retire, quit, or don’t scale. Positioning Shipsy as “your best dispatcher’s knowledge, available to every driver” resonates.
Materials
- DPD Poland Case Study — Multi-pager (4 slides)
- DPD Poland Case Study — Single-pager (1 slide)
Changelog
- 26 May 2026: Full content from Drive case study decks. Metrics and solution details added.