In a hyper-competitive business environment riddled with uncertainty and volatility, the application and uptake of Artificial Intelligence in logistics can help the logistics industry from making a fundamental shift from being reactive to proactive by harnessing predictive AI insights. By harnessing all the data from the supply chain, Artificial Intelligence can help enable analyzing data streams, in identifying patterns, and in generating tangible insights for the supply chain.
Streamlining the Last Mile
The last mile is the most significant overhead in logistics expenses. With the onset of Artificial Intelligence in logistics, the delivery vehicle routes can be optimized in real time leading to increased savings. With the aid of AI-based algorithms, historical trip sheets can be leveraged, alongwith real-time traffic statistics to significantly improve the accuracy of estimated delivery time for each shipment.
By applying artificial intelligence, deliveries can be streamlined, with the best route and delivery time for each shipment, taking into account new constraints or events, and shown on a live map. These insights are shared with the driver of the delivery van, via the navigational system onboard the vehicle.
For Logistics facility managers, such AI-based insights can help in informed decision-making. They can, for instance, monitor the performance of the logistics team in real-time, including insights on the speed of the delivery vehicle, total travel time, units moved per hour for each shipment, and help benchmark and improve service planning.
Optimizing the Delivery Network
Today’s enterprise needs a robust distribution network primed for anytime, anywhere delivery. By harnessing data from Artificial Intelligence-based tools, the distribution network can be streamlined, by mapping the customer demands at an aggregate level, with the capacity and availability of equipment as well as available manpower.
By deploying predictive AI tools, productivity and resource utilization can be improved at warehouses and distribution centers. Predictive maintenance of conveyors, forklifts, trucks, and trailers helps in simplifying expense management of warehousing and distribution. Real-time data from automated materials handling systems and equipment in the facility enhances operations of large warehouses. Optimizing the route for clamp trucks and forklifts handling inbound and outbound cargo expedites movement, saves fuel, and ensures safety.
Consolidating Shipments, Optimizing Costs
By harnessing data from AI-tools, rich insights can be gleaned on shipments by various parameters, including customer location, type of customer, type of freight, preferred delivery time frames, and other transport prerequisites such as ambient temperature or humidity.
AI can help enterprises in consolidating shipments and reducing transit time, improving cost efficiencies and maximizing capacity utilization. By deploying AI-based solutions, fraud and errors can be detected by tracking supply chain events and documents. Rate negotiations for high-risk cargo can be supported, and damage mitigation can be proactively addressed.
Sustainable Resource Utilization
Business decisions can be taken more proactively leading to tangible enterprise benefits. By deploying AI solutions, pick-up and delivery orders can be prioritized depending on weather, traffic conditions and crew availability. Constraints in the supply chain can be predicted, and bottlenecks can be addressed in real-time.
In Conclusion
AI has real world implications and impact on the entire supply chain, and those logistics companies that harness the power of AI, will be the ones in good stead, much ahead of the competition.