The last mile, from the local hub to the customer's door, is the most expensive and least predictable part of delivery. A rider searches for a house that has no number, a customer does not answer, a cash-on-delivery parcel is refused, and the order goes round again at double the cost.
AI can reduce many of these problems. But in most cities the obstacle is not the model. It is the data. This guide starts with the real hurdles, then covers use cases, a practical start and how to measure the gain.
The core hurdles in last-mile delivery
1. Informal addresses. In many places, addresses are landmarks and directions, such as "near the temple, second lane", which maps and models handle poorly.
2. Cash on delivery and refusals. Customers who refuse or cannot pay at the door make a delivery fail after the cost has been spent.
3. Many couriers, many systems. Online stores often use several delivery partners, each with its own tracking, so data is fragmented.
4. Thin delivery records. If riders do not record the time, place and reason for each failure, there is nothing to learn from.
5. Traffic, weather and road conditions change fast, especially in monsoon and festival periods.
6. Rider adoption. Tools must work on basic phones and weak connections, and riders must see a benefit.
7. Customer reachability. Wrong numbers and unanswered calls cause many failures.
Use cases and where the efficiency comes from
| Use case | What AI does | Where the efficiency comes from | Data it needs |
| Address cleaning | Standardises addresses and adds map coordinates; asks the customer to drop a pin when unsure | Fewer lost riders and wrong stops | Past addresses, delivery outcomes |
| Route batching and planning | Groups parcels into efficient runs | Less distance and time per parcel | Locations, rider capacity |
| Delivery time estimates | Predicts arrival windows from history and conditions | Fewer "where is my order" calls | Past delivery times |
| Failed-delivery and refusal risk | Scores each order for the chance it fails | Confirm or adjust risky orders before dispatch | Past outcomes, order type, payment method |
| Customer notifications | Sends timely messages and confirms availability | Fewer failed attempts | Order status |
| Delay analysis | Finds the causes of late deliveries | Targeted fixes | Timestamps, reason codes |
Practical usage: a worked example
An online store ships through two courier partners and has a high refusal rate on cash-on-delivery orders.
→ Record every delivery outcome with a reason code: delivered, customer unreachable, refused, wrong address, rescheduled.
→ Collect location data: ask customers to confirm a map pin at checkout.
→ Join the data from both couriers into one view in your order system.
→ Study patterns: which areas, order values and payment types fail most?
→ Introduce risk scoring: for high-risk cash-on-delivery orders, ask for a phone confirmation or a small advance before dispatch.
→ Send notifications with an arrival window and a way to reschedule.
→ Compare failed-delivery rates before and after.
Your first eight weeks
| Weeks | Activity | Output |
| 1 to 2 | Add outcome codes and map pins; join courier data | A clean delivery history |
| 3 to 4 | Analyse failures; add customer notifications | First insights and fewer missed deliveries |
| 5 to 6 | Add risk scoring with human review | Early reduction in refusals |
| 7 to 8 | Compare with the baseline | Decision on route tools and wider rollout |
How to measure efficiency
| Metric | What it shows | How to track it |
| First-attempt delivery rate | Success without repeat trips | Delivered first time divided by dispatched |
| Cost per delivery | Operating efficiency | Total delivery cost divided by deliveries |
| Cash-on-delivery refusal rate | Order risk | Refused divided by COD orders |
| On-time rate | Service quality | Delivered inside the promised window |
| Support contacts about delivery | Customer effort | "Where is my order" tickets |
| Parcels per rider per day | Rider productivity | Count |
Risks and where AI falls short
→ Unfair blocking. A risk score that rejects whole neighbourhoods can exclude honest customers. Review who is affected.
→ Location privacy. Handle addresses and maps securely, and limit who can see them.
→ Poor data. Without reason codes and timestamps, any model will guess.
→ Real-world shocks. Strikes, road closures and weather will always upset plans.
→ Pressure on riders. Tools that push for speed without care can hurt safety and morale.
How Quark Infotech can help
We can engineer unified supply chain platforms that seamlessly connect your purchasing, inventory, and last-mile delivery workflows. By eliminating fragmented courier data, we provide the structured delivery records required to make AI route optimization and failure prediction actually work.
Frequently asked questions
Q: Do we need route optimisation software first?
A: Not always. Clean delivery data and fewer failed attempts often save more than clever routes.
Q: Can AI fix bad addresses?
A: It can help, but asking the customer for a map pin works best.
Q: Is risk scoring fair? Only if you test it.
A: Review who gets flagged and keep a person handling exceptions.
Want fewer failed deliveries?
Tell us how you ship today, and we will suggest where to start. Book a free 30-minute call.
Enjoying this read? Help others find it too.
@manoj_malakar
