5 Must-Have Features in a Smart Last-Mile Delivery System
By Denis Baranov
As online shopping increasingly becomes synonymous with just‐in‐time delivery, fast and reliable last-mile order fulfillment is no longer a competitive advantage but a necessity. Convenient delivery, especially for perishable goods, is essential. It is also a huge logistics challenge for brick-and-click grocery retailers.
These deliveries need to arrive at the customer's doorstep the same day or within a few hours of placing the order. Even during rush hours, retailers try their best to fit into a 30-minute delivery window the customer chooses on the website. That window is also narrowing, as consumers increasingly expect 15-minute delivery timeslots. This would be impossible without modern transportation management software (TMS).
The last-mile delivery market for the food and grocery industry is predicted to hit $72 billion in 2025, accounting for the surge during the COVID-19 pandemic. More and more retailers invest in custom TMS instead of out-of-the-box solutions.
Here are the top five features of transportation management software that can help companies improve the accuracy of last-mile deliveries and increase customer loyalty.
1. Automated Continuous Route Optimization
Modern TMS solutions solve the notorious traveling salesman problem with proprietary algorithms that calculate optimal delivery routes for each vehicle. As a new order arrives, the initial route is dynamically adjusted and re-calculated, which allows for quick, accurate, and efficient orchestration of the delivery pipeline, with minimal involvement from the logistics manager.
The system takes the following parameters into account:
Cost-of-sales calculation for the selected delivery window on the designated route
Dynamic pricing to align demand with available delivery windows
Optimizing fuel consumption and thus reducing CO2 emissions
Real-time routing for a delivery pipeline means that the system closes off specific delivery windows only when the maximum fleet capacity has been reached. The optimization algorithm, however, allows the system to accept additional orders nearby already plotted routes.
2. Traffic-Based Optimization Scenarios
For doorstep deliveries that need to arrive in a 30-minute window, the TMS needs to account for traffic congestion (especially during rush hours), road closures, and repairs, as well as weather conditions. Third-party providers do not always offer detailed and up-to-date traffic information, but a proprietary traffic monitoring system can offer this option.
Delivery route optimization occurs based on real traffic conditions. Combining this data with historic traffic patterns, the system can accurately calculate street-specific travel times during certain hours of the day or day of the week. For contingency planning, the client-facing site may show 15-minute “stubs” between available delivery windows.
A data-driven approach to route calculation also provides additional value for virtual delivery simulations, which can be useful for fraud prevention or car accident investigations. Read more about this below.
3. Route Finalization
Automatic route optimization helps avoid the manual, repetitive, and ineffective work of logistics operators. However, in some cases, the system requires manual route adjustments and finalization, either as a backup for possible downtime or for other emergency cases when last-minute changes are required. Operators should have a friendly user interface to adjust delivery routes, track the impact of these changes asequations, and export finalized approved routes to the ERP system.
4. Real-Time Fleet Monitoring
By leveraging the Internet of Things (IoT), one can create a digital twin of the fleet and track the location of every vehicle in real time. A digital twin is a virtual replica of a physical object (such as a delivery truck), which digitally represents the data about the object and the processes it is involved in.
Operators can see the location of every vehicle on the map, track their arrivals and departures, and notify customers about any delivery schedule slippage. Complete visibility for the fleet and transparency of logistics activities allow operators to manage numerous vehicles as if they are a single organism, routing them interchangeably in full delivery areas, not just isolated zones. This way, the deliveries are made faster and in compliance with environmental regulations.
5. Simulation and ML-Based Optimization
Along with a fleet digital twin, one can build a predictive twin, an analytical or statistical model for predictions using machine learning (ML) based techniques. The predictive twin enables simulations of unique driving conditions and what-if analyses for possible corner cases. This data can be used for ML-based route optimization algorithms.
With the historical data about each vehicle's by-the-minute location and the driving patterns of the delivery staff, operators can simulate road accidents and other situations that require attention. This can be useful for car incident review after the fact or fraud prevention.
As customers' demands continue to tighten, the importance of speedy and accurate last-mile delivery grows greater. Another argument in favor of delivery route optimization is related to carbon footprint reduction. Because of environmental concerns, more and more retailers invest in green logistics and custom TMS solutions, which help decrease CO2 emissions.
By taking a strategic approach to order fulfillment, brick-and-click retailers now move to the next level of food delivery and seize new business opportunities for more flexibility and higher profit margins, while decreasing risk.
— Denis Baranov, Principal Consultant, Leader, Retail and Distribution Practice, DataArt
About the Author
Baranov has 15-plus years of experience in the IT industry as a developer, technical architect, solution architect, and IT leader. He specializes in designing and building business solutions in the retail and financial services sectors.
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