How AI will Deliver Never-before-seen Logistics Efficiencies

Artificial Intelligence (AI) is empowering businesses to deliver never-before-seen logistics efficiencies. It’s helping a leading 10-minute grocery provider to scale its on-demand delivery businesse by 25x in six months and fulfill on-time delivery promises every time. Thanks to AI and predictive analytics, a Fortune 100 conglomerate saves up to 80% in incidental costs. Numerous use cases exist where AI and related cutting-edge technologies have significantly impacted companies’ bottom lines by increasing efficiencies and maximizing resource utilization.

McKinsey report also found that early adopters of AI supply chain solutions reported a 15% reduction in logistics costs. Let’s see how AI is and will revolutionize the future of logistics.

Generative AI will Fuel Autonomous Supply Chains

Logistics operations depend on multiple variables. What if one of the variables falters and topples the entire supply chain? Well, AI works as an antidote for logistics-powered businesses here. It accurately identifies faults in these disparate variables. Having said that, Generative AI will eventually help not only zero in on optimization pockets and issues and improve data quality but also guide the user to the right call to action and often make the decision by itself.

Businesses can leverage technology to precisely predict demand, in a specific area, for a future date/event. This forecast data will further help suggest or even plan the exact mix of own and freelance courier partners to meet the elastic delivery demand with utmost certainty.

System-Driven Personalization For Delightful Digital Experiences

As they say, data is the new oil; courtesy of rich historical data and real-time analytics, delivery personalization will undergo a massive makeover. Over time, we will likely see delivery platforms automatically learning from customer data to align deliveries at specific timeslots to ascertain a successful first attempt. Moreover, the system will be able to gauge from feedback if the customer had a less-than-optimal delivery experience, let’s say, due to a delay in delivery. Therefore, it will automatically prioritize that customer’s order and optimize workflows to ensure timely fulfillment the next time.

Enhancing Route Planning and Optimization Using ML

Advanced Machine Learning (ML) algorithms can further optimize routes to improve delivery productivity and lower overall logistics costs. The system’s ability to learn from previous data gradually but surely fine-tunes delivery processes over time, every time. As a result, it gets better at identifying the exact customer location, understanding traffic patterns to plan, and executing highly efficient deliveries.