Scaling UK home delivery at speed

The situation

Cazoo sold cars online and delivered them to people's doors. When I joined in March 2020 the delivery operation had 85 drivers, 3 team leaders and 9 hubs, moving around 185 cars a week. Demand was about to more than triple, and every new hub was a remote site, so there was no walking the floor to keep things consistent.

What I did

• Built a capacity plan. A model that turned the weekly volume forecast into how many delivery specialists each hub needed, so hiring stayed ahead of demand instead of chasing it.
• Grew the team and the network. Hired for skill and culture, built a team-leader layer for every site, and opened 10 new delivery hubs.
• Measured every delivery. A daily and weekly tracker against a 98% success target, with every failure put down to its cause — customer, logistics or operations — so we fixed the right thing.
• Standardised how hubs ran, so planning, execution and reporting worked the same way at every remote site.
• Built the people processes the team needed: an employee-relations toolkit of 11 templates covering investigations, return to work and leavers. I also negotiated the drug and alcohol testing contract and rolled out the policy.

What changed

• 185 → 615cars moved a week, March to November
• 85 → 175drivers, and 3 → 12 team leaders
• 9 → 19delivery hubs
• <1%of deliveries failed for logistics reasons
• 96%delivered inside the booked window
• 13,000+cars moved between March and November

All of that happened in nine months. Volume more than tripled, and quality held: on-time delivery stayed at around 96% in every phase of the growth.

What I'd do next

Most of the planning and tracking ran on spreadsheets I built and updated by hand. Today I'd automate that layer: forecasts flowing straight into the hiring plan, failure reasons tagged at source, and AI drafting site SOPs and onboarding packs from one master process. Same decisions, made faster, with the team leaders' time spent on their drivers instead of on data.