A pleasant checkout should be the goal of a successful ecommerce business. Processing online orders precisely and delivering the same to the customers' doorsteps on time, without the need for repeated follow-ups will build trust and make customers return. Seamless last-mile delivery is a challenge due to unpredictable traffic congestion, weather, reverse logistics, and driver attitudes. A streamlined last-mile delivery management system aligned with updated technology will ensure smooth ecommerce business operations and boost profits.
What is last-mile delivery?
This is the final phase of the ecommerce order delivery process that starts from the warehouse or billing point and continues to the customer's location. Last-mile delivery is critical for an ecommerce business as it measures customer satisfaction levels. Delivering the right products without damage and within the promised time slot makes a last-mile delivery a successful one.
What are the pitfalls in conventional last-mile delivery processes?
Heavy costs incurred
Manually managing the workforce, especially drivers, will increase expenditures. Fuel and vehicle maintenance also cost more. Due to staggered delivery locations, costs per delivery fluctuate and cannot be precisely calculated.
Poor route optimization
Unpredictable weather, route diversions due to traffic, inefficient and naive drivers, and the absence of an agile tracking system can create delayed last-mile deliveries.
Incomplete deliveries
A lack of a proper system to handle customer unavailability, returns, and incorrect addresses results in failed last-mile deliveries and rescheduling.
Customer dissatisfaction
Not offering preferred delivery times like same-day delivery deprives customers of receiving their orders at required times, leaving them disgruntled.
What is the role of AI in overcoming last-mile delivery challenges in an ecommerce business?
With increasing demand for doorstep deliveries and preferences for same-day deliveries, a proper system aligning with updated technology is the need of the hour. AI offers solutions on par with human intelligence, and applying the same for doorstep deliveries can help execute last-mile deliveries efficiently and enhance customer satisfaction. Here are a few areas where AI can be impactful as far as last-mile deliveries are concerned.
Route optimization for seamless deliveries
AI algorithms help implement dynamic route planning to identify the shortest routes, considering traffic and weather. This will reduce fuel costs and driver charges per delivery.
AI can improve environmental sustainability by planning routes that yield lower carbon emissions and also save battery levels of electric vehicles, which can be monitored during transport. AI can then alert drivers when they need to recharge and find the nearest charging stations. In this way, AI promotes sustainability for a safer environment.
Optimizing the number of trips for effective utilization of vehicles and drivers can also be done effectively through AI algorithms and is cost-effective.
Self-driven vehicles, drones, and robots can be supported through automated navigation with AI to minimize human intervention.
Effective forecasting for insightful decisions
When sudden surges in sales happen during holidays or weekends, AI can forecast the expected demand accurately so you can allocate resources properly. Similarly, this can help you determine an appropriate driver count to assign delivery routes and achieve successful last-mile deliveries.
Slot allocation for convenient deliveries
Personalized choices for delivery slots can be framed with AI algorithms. AI-powered notifications on delivery statuses will keep customers informed about the probable time of delivery so they can schedule their availability to receive their order.
Address validation for safe deliveries
Instant updates on changes of delivery addresses will optimize last-mile deliveries for the best customer satisfaction. Delivery failures can be eliminated by using AI for address validation to verify the correct address while scheduling the delivery. Rerouting failed deliveries to the nearest hub or distribution center can also be scheduled through AI so the customer can collect their order.
Technology behind AI and its applications in ecommerce deliveries
AI technology for ecommerce last-mile deliveries combines algorithms, machine learning models, and computational processes to make correct and safe deliveries to customers by a committed time.
Here are a few AI technologies used for ecommerce last-mile deliveries
Machine learning
This AI concept is used for reliable predictions like estimated delivery times and personalized customer behavioral patterns. Dynamic route planning for optimized routes, especially for last-mile deliveries, and automated navigation for autonomous vehicles like drones and robots can be done with machine learning concepts of AI.
BlueDart and FedEx use AI algorithms for delivery route optimization and real-time delivery updates.
Natural language processing
Conversational AI is used for chatbots to communicate with customers for real-time delivery updates and resolve inquiries related to missed or delayed deliveries. Rescheduling deliveries can also be done with NLP.
Walmart Voice Order uses NLP to understand requests, product names, and categories to identify product and brand preferences.
IOT integrations
This includes smart sensors that help monitor critical packaging data like location, temperature, and condition. IOT-enabled autonomous vehicles can provide real-time data for monitoring performance and route optimization.
Alibaba uses autonomous robots and self-driven electric vehicles to deliver parcels for last-mile deliveries.
Computational methods
Packages can be sorted by labels, sizes, and dimensions. Package handling can be monitored through surveillance systems, enhancing security. Last-mile deliveries can be done in an orderly and systematic way to build trust.
Amazon uses AI-powered drones to deliver around 5 pounds in 30 minutes. They also use robots for sorting packages.
Big data analytics
This helps businesses collect data related to customer expectations and previous delivery history, and analyze for insights leading to better forecasting and to work out ways to improve efficiency of last-mile deliveries.
UniUni gathers group client orders by location to reduce their delivery costs and time. They also use AI to collect and analyze data and connect with drivers on demand.
Conclusion
Statista reports show that revenue spent on last-mile delivery accounts for up to 50% of the total delivery costs. Managing last-mile deliveries effectively needs to be meticulously done to build trust among customers. With growing demand for online purchases and doorstep-slotted deliveries, accuracy and commitment cannot be compromised. To eliminate human errors and work on actionable insights, AI is becoming a powerful tool that can almost match human intelligence. It will not be a surprise if in another five years, AI will come close to replacing manual processes for ecommerce last-mile deliveries.