5 Noteworthy Ways Artificial Intelligence Helps Logistics

Technology has been positively impacting all sectors. The logistics and shipping industry is no different. AI in logistics enables streamlining and optimising business to provide futuristic solutions without the complexities and the possibilities of error. Helping transportation and logistics businesses to work smart for fast delivery solutions, AI assumes great significance in every process concerning the movement of goods. It reduces human intervention, right from the process of demand prediction through workflow automation, warehouse automation and last-mile delivery.

AI in logistics makes way for monitoring the progress of all resources facilitating quick decision-making with hardly any scope for errors. AI also enhance customer experience through customisation, helping them with accessing tailor-made service based on their requirements. Ultimately, it improves efficiency and facilitates businesses to receive savings on cost. The adoption of AI in logistics enables the automation of everyday tasks. It helps save not only cost but enhance the accuracy of operations also.
With plenty of roles, it has to play artificial intelligence is an indispensable part of logistics and transportation management. AI coupled with the Internet of Things (IoT); Machine learning (ML) provides greater flexibility in the logistics management system. Let’s see a few distinct roles of AI in logistics management.

1. Supply Chain Planning
Supply Planning refers to the planning process that decides the course of action to be implemented after considering demand forecasting. The very purpose of supply planning is to assess the supply and demand and to gain maximum efficiency. Supply planning is an inevitable part of logistics and the adoption of AI helps demand analysis as per the data collected on a real-time basis. When AI in logistics favour demand predictions, businesses can successfully alter the supply planning framework for supply chain optimisation. It also improves return on investment through enhanced productivity while maintaining cost at a minimum.

2. Demand Prediction
Demand prediction is the interpretation of historical data and multiple external variables to forecast the upcoming requirements. It facilitates the process of order management, distribution and logistics even under uncertain situations. AI helps to analyse both past as well as real-time data for accurate demand predictions. Accurate demand forecasting helps shippers streamline inventory requirements, movement of shipment alongside the workforce needed for handling the activities. Besides, using logistics demand predictions models help in reducing the instances of repositioning the assets in terms of distance travelled, improve vehicle capacity utilisation. Studies in this regard show that the use of AI in logistics eliminates errors and ensures absolute optimisation of supply chain networks. Without accurate demand forecasting, it is hard to know if logistics firms properly utilise the capacity and space of the cargo vehicles.

3. Warehouse Automation
Today, logistics is a business that resonates with Intelligent Automation (IA), where it uses both Artificial Intelligence and Robotic Process Automation (RPA). Modern-day warehouses necessitate the need for advanced automation facilitating contactless process implementation. The use of AI in logistics is transforming warehouse automation by leaps and bounds. AI coupled with robotics, the present-day warehouses use robots capable of tracking and finding inventory. Further, it facilitates picking and packing processes from the facility, which in the past were done manually. With the advent of automation and AI, in other words, Intelligent Automation is helping in resource allocation and optimisation. Besides, it facilitates the workforce to perform services that require intensive attention than these trivial manual jobs. As in the case of AI, Deep Learning (DL) techniques also enhances the capabilities of robots. It further enables them to take independent decisions in the areas where they are used.

4. Predictive Analytics for Logistics
Predictive analytics through the use of AI in logistics help easy transportation of the shipment. Since it provides accurate forecasts about demand and capacity planning after considering past data along with other external variables. It favours intelligent route optimisation where the system can help truck drivers to dispatch the shipment to the nearest areas in the first place and then to other adjacent locations. As discussed above, predictive logistics also enables a warehousing set up that uses sorting and storing products on the shelves by way of robots along with the use of AI and Machine Learning (ML) algorithms. By helping capacity planning, demand forecasting and optimising networks, AI for logistics provides utmost efficiency in operations through enhancing the comprehensive supply chain performance. Apart from route optimisation and load capacity utilisation, AI is also useful for logistics for optimising fuel consumption. It also enables last-mile delivery solutions.

5. Accurate Tracking of Shipment
To keep the logistics processes intact, end-to-end visibility is of paramount significance. AI-enabled tracking provides accurate forecasting of the estimated time of arrival as well as estimated time of departure. Besides, the adoption of AI in logistics help receive alerts about any disruptions in the supply chain and uncertainties in shipping routes. As we all know, these factors significantly affect the quality of logistics service while increasing the costs drastically. Knowing in advance about the potential risks and delays in the processes divert the instances that impact the service quality and cost alongside helping to take proactive steps. AI and ML implement the analysis of previous shipping patterns after considering the common factors causing the delay. For instance, fluctuations in demand, weather conditions, port congestions. Nevertheless, these concerns have become a thing of the past for logistics services using AI, ML, IA and DL.

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