Amit Prasad, VP of Supply Chain & Data Science at Coyote Logistics, has spent the past decade living and breathing supply chain data. From running Coyote network analyses to consultative services for some of our largest global shippers, he has applied his MIT Supply Chain Management background to helping advance the industry through smarter applications of data science.
With the rise of e-commerce and consumer behavior evolving at a rapid pace, last mile logistics is becoming increasingly critical for efficient supply chain operations.
This article addresses some of the forces driving the evolution in the last mile logistics, the inherent challenges, recent trends in the last mile delivery, and how technology and science are allowing for new advancements.
Forces Driving the Evolution of Last Mile Logistics
There are three main factors driving the evolution in the last mile logistics space:
As more and more people move into cities, which is increasing consumer density, the resultant constraints in capacity and infrastructure further complicate the overall shipment process and constantly change the way orders get fulfilled and delivered to end consumers.
As more people adjust to on-demand consumption, which allows consumers the instant gratification of receiving their orders within mere hours, shippers are consequently forced to re-evaluate their last mile supply chain logistics and fulfillment policies.
The COVID-19 pandemic forced a lot of traditional brick and mortar shoppers to buy products online, which has changed the perception of the e-commerce experience for many.
This is likely driving more traffic to e-commerce channels, which is leading many shippers to invest in auxiliary facilities and last mile deliveries to service new customer needs.
Key Challenges, Most Likely Complications
Effective optimization of cost, service, and assets are the inherent challenges within last mile logistics.
We know that urbanization brings uncertainty in supply and demand by creating traffic congestion, random disruptions (such as road closures, diversions and accidents), and many other unplanned variables.
These factors, along with constrained capacity and infrastructure (such as parking regulations, more frequent stops, etc.), make modeling network design and routing for the last mile extremely complex.
Further complications persist as lead times run into hours, making it less flexible and cost efficient to service the customer within a short delivery window.
Emerging Trends for Shippers and Carriers
As these variables drive more shippers towards last mile logistics, many have been moving towards an omni-channel strategy.
The COVID-19 pandemic resulted in a surge in online demand, which further accelerated this shift.
In addition to traditional warehouse locations and auxiliary facilities in the urban areas, many shippers who depend on service level and lead time are utilizing their existing brick and mortar retail stores to fulfill online orders through the storefront or backroom.
As a result, last mile network design is moving from a traditional centralized approach to a more decentralized model.
Many carriers who have been reluctant to last mile runs in the past are now embracing its importance and strategically allocating their assets to accommodate.
In urban areas, deliveries are now being carried out through multiple modes using various vehicle types, including crowdsourced, ranging from motorbikes to electric vans – both of which provide agility in the constrained urban infrastructure.
However, this also poses challenges to integrating all sub-processes for full control and visibility into the delivery process to meet and exceed customers’ delivery expectations.
Therefore, tracking and tracing are becoming a critical part of last mile logistics. Shippers and carriers, especially after the ELD mandate, are now utilizing smart technology and sensors to track shipments at each step in the delivery process through better connectivity.
How Data Science Supports Last Mile Logistics
Since connectivity and visibility are critical for the digitalization of the supply chain and logistics operations, data science plays an important role. Here are three ways:
1. Optimizing Solutions
As supply chains become more complex and difficult to model using traditional optimization methods (due to inherent uncertainty and unpredictability of last mile logistics), data science can help by modeling more accurate, cost-effective solutions using machine learning and a simulation-based stochastic approach.
2. Predicting Demand
Predictive modeling and AI/machine learning methods can help anticipate consumer demand and help move those SKUs to the local warehouses in advance. It can also help predict ETAs and service disruptions, which are essential in proactively notifying end customers and solving problems.
3. Dynamic Route Planning
With this data being readily available from several sensors and automated systems, it can be utilized to dynamically adjust routes based on moving demand, real time status of existing orders and routes, and lead times.
How Coyote Can Help
Coyote recently announced the launch of its dynamic route optimization program that aims to streamline supply chain operations and reduce uncertainty for carriers by maximizing the efficiency of their fleets and delivering load consistency through optimized weekly routing plans.
This program includes enhancements to solve for last mile route planning that includes route learning, a data-driven approach, and adding more flexibility to take on dynamic orders within constrained windows.
If you’re a shipper looking to increase your market share in the transportation industry, it’s important that you understand, evaluate, and optimize your last mile logistics operations.
If you’re a carrier interested in taking advantage of dedicated weekly loads, find out how to participate in Coyote’s dynamic route optimization (DRO) program.
Want to find out how Coyote’s DRO program and specialized solutions are bringing shippers better coverage, service, and flexibility? Watch this on-demand webinar to learn more.