ARTIFICIAL INTELLIGENCE IN
LOGISTICS & TRANSPORTATION
TO IMPROVE BOTTOM LINE
Artificial intelligence enable Logistics & Transportation businesses to identify the true potential of data and take insightful decisions to improve their bottom line.
Ombrulla has been delivering unique and comprehensive solutions in logistics and transportation industry. Our aim is to help the transportation and logistics industry to identify their pain areas and overcome the challenges using our AI and ML enabled solutions
AI in Logistics & Transportation - Smarter Supply Chain Decisions
Organizing and Scheduling
Any industry must plan in order to meet demand based on the market and its potential. As a continuous process and effective supply chain platform, it should synchronize the whole supply chain.
AI/ML identifies trends in supply chain data by examining the past and selecting relevant models based on the nature of the day. It also improves customer experiences and logistical procedures.
The key issues here include an imbalance between demand and resource availability, as well as poor area mapping/vehicle breakdown. AI/ML has the potential to allow the logistics industry to employ real-time data in demand and forecasts formulation.
Predictive Analytics
Predictive analytics is the practise of forecasting consumer demand by gathering and categorising historical data and then applying analytical tests to it. Suppliers eliminate risks and provide forecasts to avert potential failures by using AI for improved logistics. ML-solutions in conjunction with NLP may be used to collect relevant data from numerous social media sources, analyse unstructured text, do sentiment analysis, and identify potential dangers. Similarly, AI-powered systems may employ digital and satellite maps, as well as traffic data, to assist optimise routes. The system can take into consideration and process in real-time time, location, traffic conditions, and changing client requests. The firm may enhance its decision-making processes in the following areas by using various demand forecasting methodologies.
Warehouse Management (WHM)
Real-time monitoring data are offering superior visibility throughout WH by combining Machine Learning with IIoT sensors. This comprehensive architecture platform will forecast real-time data and produce insights and patterns, providing additional opportunity to automate and comprehend the logistics company and improve supply chain management at all levels. This smart warehouse may also be linked to the centralised data processing unit, increasing overall productivity as the amount of orders processed rises.Because warehouses are IoT-enabled, data processing will increase in terms of both speed and accuracy. This wireless cloud data communications links all aspects of our system and engages in a conversation that includes system monitoring and control. This would allow us to increase productivity in pick-and-pack procedures, package routing, and parking.
Shipping and Delivery
AI aids in the tracking of road traffic, the reduction of fuel use, the improvement of air quality, and the delivery of urban planning services. On top of that, it reduces traffic congestion, identifies the driver's free time, makes parking much easier, and many other benefits. It assists businesses in analysing existing routing, route optimization techniques using shortest path algorithms to identify the most efficient route for logistics trucks to deliver the product, thereby speeding up the shipping process and significantly reducing shipping costs, ultimately making the customer happy and maintaining and improving the relationship.
GPS units fitted in the truck offer information such as latitude and longitude, real-time vehicle position, and vehicle movements. As a result, carriers can immediately identify the state of a vehicle following an accident or incident, and swap arrangements may be arranged accordingly.
