McKinsey’s global pathways report for road freight 2022 reports that road freight now accounts for 53% of CO2 emissions. Given the fact that the road freight market is expected to demonstrate a growth rate of 6.03% CAGR till 2027, and sustainability is becoming the focal point of logistics operations planning, businesses are increasingly looking for ways to make it more efficient and sustainable. Further, the need for rapid, agile, and elastic transportation is assuming a dire stance as 49% of online consumers make purchase decisions keeping same-day delivery in mind. Further, more than 51% of retailers are offering same-day delivery services currently, and the number is expected to be 99% by 2024.
However, implementing Vehicle Routing Problem (VRP) in real-life comes with multiple challenges and tends to become an operational problem of evolving complexity. This is mainly because there are multiple types of route optimization constraints in terms of vehicle capacity, locations (for pickup and delivery), riders, etc. Here is a quick overview of the key challenges posed by the Vehicle Routing Problem in real life and how intelligent technology can help businesses overcome them.
VRP implementation requires standardized data that is reliable and correct. For example, physical consignment parameters, such as weight, volume, height, etc., and vehicle capacity data, location data, and rider/asset data. However, businesses lack a standardized data repository as the majority of the operational components of the logistics system are siloed. For instance, the transportation data stays in the TMS, the order/consignment data stays in WMS and OMS, and rider data stays in the vendor management system or 3PL interface.
This disparate nature of data makes it impossible for businesses to feed correct, fresh, and reliable data in the vehicle route planning software and requires extensive manual effort. This is where intelligent route optimization platforms prove beneficial. These platforms integrate disparate systems and present a highly centralized data view across all of them. This ensures data quality, reliability, and availability at all times.
Another critical challenge faced during real-life implementation of VRP is the inability to consider and respond to uncertain and unpredictable events and environments. Some common examples include real-time traffic and road conditions, roadblocks, accidents, weather conditions, etc. The inability to plan for such unpredictable events in real-time and generate an alternative route while keeping the route optimization parameters intact affects the efficiency and SLA adherence.
Ensuring this manually or digitally in a static routing system is also impossible. On the other hand, intelligent vehicle routing platforms help businesses accommodate such events as well. They consider real-time events and generate alternative routes without disrupting operational efficiency or delivery SLAs. The riders are given app-based permissions and intelligent workflows to continue the delivery operations in the most optimal manner in extreme cases, such as accidents, parcel loss/theft/damage, and more.
Implementing VRP requires accurate location data to create delivery routes for multiple locations in an efficient manner. However, manually-managed systems and static routing tools are unable to offer this functionality and require some sort of manual address directions from the end customer leading to increased delivery times. This affects the overall operational efficiency and degrades the CX as well as SLA adherence.
Automated vehicle routing and route optimization platforms come with advanced functionalities, such as the automated conversion of the address to latitude and longitude, submitting address-related corrections, and adding local landmarks in the system for increased delivery accuracy. These platforms also offer efficient customer-rider communication flows for time-saving address resolution and rapid delivery completion.
Real-life VRP involves planning routes and optimizing them for hundreds or thousands of customers, multiple depots/pickup or drop-off locations, and various types of vehicles. This makes the VRP problem instances large-scale and complex, and this complexity grows exponentially with the scale of business operations. Thus, manual and static routing applications no longer produce desired optimal results, and businesses require sophisticated algorithms and extensive computational resources to overcome the challenges.
AI/ML-powered route optimization platforms come with in-built intelligent algorithms and highly advanced capabilities that scale up or down with the business requirements in an elastic manner. Thus, businesses can easily set specific routing constraints for multiple types of consignments, vehicles, riders, etc., in an effortless manner.
Advanced route optimization solutions boost delivery planning and deliver an exceptional customer experience by reducing order-to-delivery times and improving SLA adherence. Intelligent load planning features help businesses plan a delivery for bulk shipments, planned consignments, and on-demand consignments from a single dashboard based on specific constraints.
This minimizes leasing and delivery times while in-built algorithms help analyze, evaluate, and allocate consignments to the right vehicle and unlock a 12% reduction in transportation costs, a 31% increase in vehicle capacity utilization, and up to 4% savings in route planning and optimization time.
Hence, investing in such advanced solutions that evolve with the business requirements and leverage highly intelligent technological infrastructure is the right step forward to higher efficiency and improved profitability.