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Artificial Intelligence in logistics business

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venkat k
Artificial Intelligence in logistics business

How AI can solve logistics problems and generate value.
Artificial Intelligence has begun to impact the logistics industry along the supply chain. We are seeing innovations such as smart roads and autonomous vehicles. In this article, we will look at five good AI use cases in logistics. The potential value that can be obtained is very large. Research shows that it can earn anywhere from 3 1.3 trillion a year to tr 2 trillion.

The primary purpose of many AI implementations in the logistics industry is to automate time-consuming actions and save money. Most tech enterprises (eg Google and Amazon) are investing heavily in this technology and are leading this sector.

Automated Warehouses

Artificial intelligence technology changes the operations of many warehouses, e.g. Data collection, inventory processes and more. As result companies increase revenue. AI in warehouse automation is being used to predict the demand for specific products. Based on this data, orders can be edited and shipped on demand to the local warehouse. Estimating demand and planning logistics in advance means lower transportation costs.
Warehouse automation systems offer the opportunity to conquer the most common tasks. The most popular technology used in these systems is computer vision, which helps to identify and manage inventory. Another good use case is autonomy quality control.

These systems can also optimize inventory and shipping between warehouses.

Autonomous vehicles

Self-driving cars get a lot of press today and for obvious reasons. The use of automated vehicles in the logistics industry promises to save time and money and reduces the accident rate. There is still much work to be done, and right now, drivers need to be on the autonomous vehicle cycle, and technology and regulations take some time to drive fully autonomous vehicles on the roads without human supervision.

Autonomous vehicles are generally the beginning of autonomous business processes that run robotic process automation (RPA), from warehouse ground vehicles and drones across the supply chain. Some companies even advance to an autonomous supply chain, which automates many tasks in the supply chain, which are traditionally performed by planners and managers.

Smart roads

Another AI use case in logistics is smart roads. Examples of this technology are highways with solar panels driven by LED lights. Solar panels can help generate electricity while using LED lights to alert drivers about road conditions. Additionally, solar panels prevent the road from slipping in the winter. Another application is fiber optic sensors, which can detect traffic volumes and patterns and alert drivers to road conditions. They can also sense when vehicles leave the road or are involved in accidents and alert appropriate emergency services and authorities. This makes for faster deliveries and safer road conditions.

Back Office

Artificial intelligence, along with Robotic Process Automation (RPA), gives workers the opportunity to improve their work quality. For example, daily repetitive tasks can be automated. This reduces costs and improves the accuracy and timeliness of data for logistics companies.

Demand Prediction

A critical business need that affects most businesses is to estimate the number of goods and materials that will be needed in the future. Lack of inventory means lost sales, loss of revenue, and customers who are defective of a competitor’s product.

AI offers a variety of algorithms that can predict trends. According to Deloitte, in most cases, these algorithms can predict better outcomes than human experts.

Nowadays, AI can track and measure all inputs and variables and can do so quickly and accurately, improving the accuracy of predictions. This means less inventory and simpler warehouse management.

AI can enhance the customer experience through personalization, with products and customizations specifically tailored to their buying habits and preferences.

There are many ways to implement AI in the supply chain and the logistics sector. This improves logistics processes and reduces costs. It plays a key role in automating simple tasks to improve speed and accuracy in many back-office applications.

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