Infusing AI into Logistics: The armour for Freight Rate Optimisation


AI is no longer a buzzword- it has almost become a dire necessity after showcasing its prowess in logistic applications and usage. Like Thor’s hammer takes to the Greek God, AI is growing on logistics, and can be wielded to combat volatile freight costs and optimise overall operations.This feature examines the same.

The cutthroat competition in the industry has led to changing customer behaviours and sky-rocketing levels of customer satisfaction, making companies strive more than ever into etching a mark in the domain for stronger identity, reliability and business dominance. This is where Artificial Intelligence(AI) is now gaining ground.

AI is the simulation of human intelligence processes by machines, especially computer systems. AI can significantly redefine the entire logistics experience by boosting reliability, reducing the cost of transportation, faster processing, and deciding optimal routes for last-mile operations, thereby optiming freight costs.

By increasing reliability, reducing the cost of transportation and faster processing, it is emerging as a power factor to reckon with, in optimisation freight and freight costs within the entire spectrum of the logistics and supply chain.

Many companies are now investing hundreds of millions of dollars to speed up their delivery processes and ensure the next day or even same-day delivery. As a tool, AI can play a critical role in finding the best route, predicting accurate delivery times as well as determining the correct warehouse product mix all while reducing costs as well.

AI is now seen as a power factor for Optimisation of Freight Rates through better capacity planning, optimal route planning, dynamic charging, optimal allocation of resources and vehicles quickly to the in-demand areas to reduce customer wait times.

Managing Last Mile costs

The execution of first and last-mile services account for over half the total cost incurred of Logistics companies. By making the transportation process faster, more reliable, and more efficient, logistics companies are always on the lookout to churn down costs.

With the growing digitisation of logistics industry, more and more companies integrating AI into their logistics and to their supply chain in order to maximise their resources by reducing the time and money spent on figuring out how, where, and when to deliver the goods and services.

AI in Vehicle Routing Optimisation:

Vehicle routing is a complex logistics management problem and stands for a key class of problems to solve in order to lower costs and optimise logistics resources.

VRO helps in cutting down costs of lastmile services by optimising route and resources allocation. As more than half the costs associated with a typical logistics company is levied in the execution of the first mile or the lastmile, these can lead to huge savings.

The goal of VRO given these conditions is to compute a route which minimises the aggregate transport costs such as the total distance traveled number of vehicles used and/or the total transport time.

An AI/ML-enabled route optimisation solution can provide information about the optimal number of vehicles required and the shortest route to be taken to deliver the packages within the delivery time window. At the same time, the system can continue to learn from the already made deliveries every day and continue refining itself to meet maximum delivery windows while optimizing the transportation costs.

A route optimisation solution, powered by artificial intelligence, can go a long way in making logistics operations more efficient, resulting in cost reduction, improved customer experience, and better resource management. It can cut down the manual efforts required to adapt the routes and provide better visibility of the shipment to the planning managers as well as the customers.

Shipping volume prediction 

AI and predictive analytics have brought about a wave of changes in the transportation sector, particularly in converting traditional modes to data-driven tools. By transforming historical data from transportation management systems into a data-driven tool for forecasting the number of sales and shipping volumes, it has shown the way forward in forecasting.

You can receive forecasting for just one day or several weeks and optimise the operational process with a data-based plan in advance depending on your business’ needs and planning processes.

Predictive Fleet Maintenance

To increase the lifespan and cut down operating costs, AI has found critical usage in Predicting Fleet Maintenance. Predictive maintenance software can significantly help in full truck analysis of potential failures and scheduled repairs.

Such softwares imbibe IoT (Internet of Things) devices such as on-vehicle sensors that connect with each other which track vehicle location via GPS, and onboard diagnostics II (OBD II) sensors monitor the engine speed, accelerator and pedal position. 

This data is then sent to the local gateway over a mobile data connection as the vehicle moves.

With the help of this, fleet managers receive a detailed record of vehicle data, fuel usage, engine performance, route, location, driver, time, and delivery. Thus, your fleet management team can order parts and components in a timely fashion, plan vehicle availability, and avoid technical and logistical issues.

AI in India: The Road Ahead

Many organisations have now been benefited with investments in artificial intelligence. As for India, analysts predict that Ai can help add up to $957 billion to the Indian economy by 2035. The opportunity for AI in India remains massive, as is the scope for its implementation. By 2025, data and AI can add over $500 billion and almost 20 million jobs to the Indian economy.

The incorporation of artificial intelligence has powered warehousing firms towards agility and becoming market-oriented. AI has further allowed firms to strengthen last-mile optimisation, returns handling and smart batching.

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