With an increase in the digitization of logistics, there is an increased use of data to carry out decision-making processes.
Businesses that plan logistics by relying on a human understanding face a lot of challenges relating to time and efficiency.
Logistics involve coordination among a lot of different parties such as manufacturers, transporters, store-keepers, and end users, making it next to impossible to have a single point of contact and control, at every step of the process.
Moreover, with stock management being carried out manually, companies are likely to incur huge losses due to errors.
The end goal of any logistics company is to retain their employees while at the same time, maintaining a connection with their end customers. For that, quick product delivery, developing innovative solutions, and maintaining an omnichannel are necessary.
This is where the abilities of AI in Machine learning and Natural Language Processing would help in processing multiple amounts of data and help the company in achieving its end goal.
AI’s ability to automate processes like network analytics, demand forecasting, and capacity estimation help in optimizing logistics.
AI also helps transporters plan their routes by predicting surges in demand which helps the company ensure timely deliveries.
How AI and ML Can Optimize Logistics
If properly leveraged, Artificial Intelligence (AI)and Machine Learning (ML) can boost production and delivery. That are multiple ways that implementing AI and ML in Logistics can optimize it.
Improved Data Quality
AI, ML, and Natural Language Processing (NLP) can comprehend commonly repeated terms and jargon and use them to create context.
Automated Warehouses
Automated warehouses employ the use of technology to minimize human interference. For instance, robotics and sensors can be used to automate repetitive tasks.
AI can be used to make demand forecasting, order modification, and re-route products that are in transit. Through this, goods that are in demand can be delivered to the nearest warehouse.
AI can also identify the best option to transport inventory when there are multiple warehouses in the chain.
Transportation Forecasting
AI and ML algorithms can be used to predict future shipping capacity needs, such as the availability of logistics capacity and price fluctuations in the market.
Unlike traditional transportation workflow that only allows for a few days window after the order has been placed to ship it, AI can be used to extend the delivery time before the order is placed, thus minimizing last-minute freight costs.
Demand Forecast
Companies can lose a large amount of money when the supply of goods does not meet the demand.
AI-based algorithms can make predictions of future trends and demands. These algorithms can also be used for personalized product suggestions to customers based on their buying habits and preferences.
Delivery Drones
Delivery drones can be useful when the business wishes to deliver to a place where ground delivery is not possible.
Delivery drones can be useful for products that have a shorter shelf life, thus helping the business to reduce waste costs.
Smart Routes
Smart Routes are another application of AI in the logistics industry that not only improves road safety but also reduces the delays caused in the supply chain, thus ensuring faster deliveries.
These routes are equipped with solar panels and LED lights. Solar panels produce electricity and prevent roads from being slippery while LED lights send alerts to drivers about changes in road conditions.
Routes are connected to the internet through fiber optic sensing can sense traffic volumes and patterns, through which they can warn the drivers regarding upcoming traffic jams.
Not just that, these sensors are able to detect when the vehicle leaves the road or an accident occurs and send alerts to the drivers.
Back Office Working
A combination of AI and Robotic Process Automation, cognitive automation technology can be used to improve the speed and accuracy of back-office operations.
It can be used to automate repetitive tasks for back office employees such as accountants or HR specialists which can help in improving their efficiency and reduce the risk of human error.
Improved Fleet Performance
AI enhances fleet performance and management, which reduces the cost of Logistics.
AI-powered logistics can be used for enabling accurate load-vehicle matching and optimizing onboarding capacity.
This helps in reducing the number of non-essential vehicle hires, while at the same time avoiding vehicle overloading.
Role of AI in Logistics Management
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Inventory management
AI can prove to be very useful in inventory management. It can handle large amounts of data to manage an accurate inventory to ensure that the products enter and are dispatched from the warehouse in the exact sequence.
It manages all inventory-related variables like processing, picking, and packing, which can help avoid overstocking and understocking.
Product Inspection
AI uses computer vision technology to identify damaged items and evaluate the depth of their damage.
This helps the organization take action to prevent future damage.
Timely Delivery
With the implementation of smart routes and predictive demand, there is less reliance placed on manual work.
Apart from efficiency, it also ensures on-time delivery of products.
Conclusion
To maintain efficiency and mitigate errors encountered in the process of logistics, it is necessary to adopt technologies powered by Artificial Intelligence.
Artificial Intelligence has a plethora of applications in logistics and supply chains. It not only helps in streamlining the workflow, but also, automates repetitive, time-consuming tasks.
Adopting Artificial Intelligence and Machine Learning in logistics is capable to increase the productivity of your organization by 40%. AI-powered algorithms and automation software make it easy to collect and analyze large amounts of data.
Moreover, it also enhances customer satisfaction and experience.
