🚐 REDUCES DELIVERY COST 18%
🚐 Last Mile
Delivery Optimizer
Python routing optimizer using Berlin district geodata. Calculates optimal delivery sequences for a fleet of 5 vans across 50 daily stops reducing total distance by 18%, cutting fuel costs by €1,240/month, and increasing on time delivery probability from 71% to 94%.
18%
Delivery Cost Reduction
312 km
Distance Saved Daily
€1,240
Monthly Fuel Saving
94%
On Time Delivery Rate
// route visualisation
Berlin Delivery Network Optimized vs Manual Routes
Show:
Click a van below to highlight its route
Optimized routes
Manual routes
Delivery stops
Depot (Wedding)
// fleet performance
Per Van Route Savings Breakdown
📊 Distance Saved per Van manual vs optimized (km/day)
⏱ On Time Delivery Rate before vs after optimization (%)
⛽ Fuel Cost per Day 4 week trend (€)
📦 Stops per Hour efficiency gain
🗺 Delivery by District stop volume
▶ Problem
- Last mile delivery accounted for 53% of total logistics costs yet route planning was done manually each morning by dispatchers using intuition, taking 45 60 minutes per day.
- Drivers frequently criss crossed districts rather than following geographically efficient sequences adding 60 90 km of unnecessary driving per van per day.
- On time delivery rate of 71% triggered 3 5 customer complaint calls daily and put SLA agreements with key accounts at risk.
- No visibility into route performance by driver, district, or time of day making it impossible to identify where the most time was being lost.
▶ Solution
- Python implementation of nearest neighbour heuristic applied to 50 daily delivery stops across 12 Berlin districts using OpenStreetMap coordinate data for each delivery address.
- Algorithm assigns stops to 5 vans by district cluster (k means grouping), then sequences each van's stops using nearest neighbour from the depot in Wedding, Mitte.
- Model outputs optimal stop sequence per van, total distance, estimated drive time, fuel cost (at €0.18/km), and on time probability based on historical window compliance data.
- Daily run takes 4 seconds. Output is a CSV dispatch sheet handed to drivers each morning zero change to existing workflow, maximum adoption.
▶ Key Findings
- Total daily fleet distance reduced from 1,728 km to 1,416 km a saving of 312 km/day, equating to €1,240/month in fuel at current diesel rates in Berlin.
- Van 3 (Neukölln / Tempelhof route) showed the highest inefficiency in the manual plan crossing between districts 7 times per day. Optimized route eliminates all cross district backtracking.
- On time delivery rate improved from 71% to 94% within 2 weeks of implementation driven purely by earlier arrival at first stop and reduced mid route delays.
- Spandau and Reinickendorf deliveries are most time sensitive both districts have narrow delivery windows (08:00 12:00). Sequencing these first in the van assignment produced the largest OTD improvement.
- A full VRP (Vehicle Routing Problem) solver would yield an additional 4 6% distance reduction beyond nearest neighbour worth commissioning if fleet scales to 10+ vans.
// projections
📈 Monthly Fuel Cost Savings Projection
⛽ Cumulative Fuel Cost Saving solid = realised dashed = projected (€)
Base scenario (5 vans): Projected cumulative fuel saving reaches €14,880 within 12 months of deployment a direct bottom line impact with zero capital expenditure required. Scale to 8 vans: If the fleet grows to 8 vans (planned for Q3 2026), optimized routing delivers €23,800 annual saving making the business case for a dedicated route planning tool compelling at that scale. Fuel price +15% scenario: A diesel price increase of 15% (from €1.75 to €2.01/litre in Berlin) would make the current saving worth €17,100 annually the optimization becomes more valuable as fuel costs rise, not less.
// dataset
📊 Raw Dataset
Real data used in this analysis 50 rows shown. Download full dataset below.
| Stop ID | District | Van ID | Window Start | Window End | Weight (kg) | Seq Manual | Seq Optimized | Dist. Saved (km) | Time Saved (min) |
|---|
// strategic guidance
💡 Advice for Companies with Similar Challenges
💡 Practical Recommendations
- Start with a simple nearest neighbour algorithm before investing in expensive route optimisation SaaS. A Python script using OpenStreetMap coordinate data can deliver 15 20% distance savings within days of deployment, at near zero cost. The ROI is immediate and measurable.
- Cluster your stops by district before sequencing. Assign each van a geographic zone first, then optimise the sequence within that zone. This two step approach outperforms single pass optimisation for multi van fleets and is far simpler to explain to drivers and dispatchers.
- Prioritise time sensitive stops at the start of each route. Stops with narrow delivery windows (e.g. 08:00 11:00) should always be sequenced first even if this adds marginal distance. The cost of a missed delivery window is almost always higher than the cost of extra kilometres.
- Track your baseline before you optimise. You cannot prove value without a before/after comparison. Log manual route distances and on time rates for 2 weeks before deploying the optimizer the data will justify the investment and build internal support for the tool.
⚡ Key Takeaway
Last mile is where most logistics budgets leak, and it is also where a simple Python script delivers the most immediate, visible return. You do not need a €50,000 SaaS platform to reclaim 15 20% of your delivery costs. You need clean stop data, a coordinate lookup, and a nearest neighbour loop. Total build time: one day.