7. Using the Fastest Service Level for Every Order Can Increase Cost Unnecessarily

Reducing delivery cost sounds straightforward until the business also needs to maintain speed, reliability and customer experience. Cut rider capacity too aggressively and SLA can deteriorate. Keep too much dedicated capacity and riders sit idle. Choose the cheapest delivery partner for every order and failed deliveries may increase. Promise the fastest delivery on every order and the operation may spend more than the customer or order actually requires. That is why last-mile cost reduction should not begin with: “How do we pay less per delivery?” It should begin with: “Where is cost leaking from the delivery operating model?” For most businesses, delivery cost per order is not determined by one line item. It is created by a combination of capacity, utilization, allocation, route efficiency, failed attempts, partner selection, vehicle type, COD handling and operational effort. The best cost reduction therefore comes from improving how those parts work together—without breaking the SLA customers already expect.

A logistics partner may quote a fixed price per order, per kilometre, per rider or per vehicle. That number is important, but it does not necessarily represent the business's true delivery cost. The real cost can also include: idle rider capacity, unsuccessful delivery attempts, reattempts, unnecessary kilometres, low drop density, manual allocation effort, additional customer-support effort, COD handling and reconciliation, partner penalties or escalations, and fixed fleet capacity that remains underused. This creates an important distinction: Quoted Delivery Cost ≠ Effective Cost Per Successful Delivery If a ₹70 delivery fails and requires a second attempt, the effective cost of successfully completing that order is no longer ₹70. Similarly, a dedicated rider may look inexpensive on a high-volume day and expensive on a low-volume day because the fixed cost is being spread across fewer successful deliveries. This is why CPO should be analysed operationally rather than only financially.

Our review of competitor content around last-mile cost showed strong coverage of fuel, route optimization, shipping charges and RTO. Newer India-focused pages are also beginning to discuss true cost per delivered order, COD, reverse movement and successful-delivery rates. The bigger opportunity is to connect these costs back to the operating decisions that create them. A useful leakage model looks like this: Capacity Leakage → Allocation Leakage → Utilization Leakage → Routing Leakage → Failure Leakage → Post-Delivery Leakage Each represents a different way money can disappear from the last mile.

A rider can complete every assigned order on time and still be economically inefficient. If the rider spends a large part of the working day waiting for demand, fixed cost is spread across fewer deliveries. For example, imagine the same rider cost being distributed across: 8 deliveries per day, 14 deliveries per day, or 20 deliveries per day. The cost per completed order changes even if salary, vehicle and administrative costs remain unchanged. This is why utilization is one of the most important delivery economics metrics. A historical example from India illustrates the principle particularly well. In 2016, Zomato reported that its delivery partners incurred about ₹62 to fulfil an order while Zomato paid them approximately ₹50 per order. Zomato explained that partners could operate more efficiently because delivery personnel also handled ecommerce and grocery demand during periods when food-ordering volumes were lower. Those figures describe Zomato's 2016 business and should not be treated as a current India delivery-cost benchmark, but the operating insight remains valuable: productive use of capacity during demand troughs can materially change delivery economics. For businesses today, the question should therefore be: Are we reducing rider cost—or increasing rider productivity? The second often creates the more sustainable result.

Two riders can travel the same total distance and produce very different economics. A rider completing one delivery every several kilometres will have a higher effective cost per drop than a rider completing several nearby orders along a well-sequenced route. This is the density problem, cost improves when more successful deliveries can be completed with the same amount of rider time and movement. Businesses should therefore monitor: orders per rider, deliveries per hour, kilometres per successful drop, average distance between stops, and route-level delivery density. Zomato's FY2019 reporting provides another historical India-specific example. The company reported that its last-mile cost per delivery had fallen from ₹86 to ₹65 while deliveries per rider per hour increased from 0.9 to 1.4. Those are historical Zomato figures rather than market benchmarks, but they illustrate the relationship between rider productivity and per-delivery economics. More riders are not always the answer, sometimes the better answer is getting more productive deliveries from the capacity already deployed.