Finally , the collapse of several large national freight operators has opened doors for regional logistics providers to expand their businesses to serve enterprise shippers . But with new opportunities come new challenges . To meet the expectations of enterprise shippers , regional providers must offer capabilities that extend beyond point-to-point deliveries . They must also provide cross-docking , sorting , and multi-leg delivery options , as shippers expect full visibility and reporting throughout the supply chain . Last-mile platforms are responding by integrating features that support more complex logistics operations , including light warehousing and cross-docking capabilities . These expanded and evolving last-mile TMS solutions enable logistics providers to capture new business while maintaining the operational flexibility necessary to meet the needs of various customers in today ’ s supply chain .
Raman : On average , front and back o ! ce operations cost 10 % of revenue , while the industry operates with narrow profit margins . For that reason , carriers that can increase employee productivity by 10x will see increased profitability and expansion .
Carriers need to have order visibility because their shippers will demand this data to serve the end customer . Additionally , deploying AI will require carriers to collect this data in a structured , unified way .
Most carriers are still using technology that hasn ’ t kept pace with the changes in their business , leading to ine ! ciency and mistakes . For example , carriers often cobble together multiple TMS systems for different transportation modes , such as line haul and last-mile , because one system isn ’ t flexible enough to support both .
CLDA Mag : How will the use of AI increasingly impact last-mile providers in 2025 ?
Jagad : The use of AI in last-mile logistics has the potential to fundamentally transform logistics providers ’ operations through advanced data processing and decision-making capabilities . AI isn ’ t just the latest technology that is helping improve productivity . It goes beyond that . This innovation raises the bar for how last-mile providers optimize and customize every aspect of the delivery ecosystem and process to serve their customers . This goes from delivery location accuracy to route optimization , customer communication , billing , loss prevention , and more . As AI adoption becomes increasingly widespread , last-mile providers can and should leverage it to improve e ! ciency , accuracy , and customer satisfaction .
One critical area where AI will be especially impactful is delivery location accuracy . AI-driven systems can enable real-time adjustments that consider environmental factors , tra ! c conditions , and even last-minute delivery detail changes . AI will also significantly enhance route optimization , as algorithms continuously analyze current and historical tra ! c data , weather patterns , and fleet availability to determine the most e ! cient routes , saving time and fuel costs .
The rise of AI-based technologies and cloud solutions has made building applications with fewer resources significantly easier .
AI is automating a significant portion of the coding process , reducing development times and costs .
fall / winter 2024 I customized logistics & delivery Magazine 31