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ARTIFICIAL INTELLIGENCE (AI) IN LOGISTICS
Using AI in Logistics
Example uses of AI in the logistics industry
AI is employed in different segments of logistics to boost efficiency and effectiveness, offering a glimpse into its versatility across various departments within this domain.
Warehouse Management
AI ensures swift inventory turnover by dynamically adjusting item placement based on continuous analysis of historical order data and real-time demand, reducing the risk of obsolete products and maintaining fresh stock. Placing frequently picked items closer to packing or shipping areas minimizes worker travel time, enhancing efficiency as the system adapts to changing demand patterns.
Customer Service
Chatbots provide real-time updates on order and shipment status, offering customers transparency and reducing the need for direct customer service involvement. Addressing inquiries about product availability, shipping options, and return policies, AI-powered chatbots streamline customer support
Vendor Management
AI-driven Supplier Relationship Management (SRM) software aids in supplier selection by evaluating criteria such as pricing, historical purchase records, and sustainability measures. These tools track and analyze supplier performance metrics, systematically ranking suppliers based on contributions and reliability, fostering informed decisions and enhancing efficiency in supplier management.
Inventory Management
AI enables precise review of ideal stock levels, identification of slow-moving products, and forecasting of potential stock shortages or excess inventory situations. These empower businesses to optimize inventory management, improve order fulfillment processes, and reduce holding costs, ultimately enhancing overall supply chain efficiency.
Integrate AI into your logistics management to enhance efficiency, improve decision-making, and transform supply chain operations for greater speed and accuracy
How much does it cost for logistics companies to implement AI?
Different AI tools cost differently, and some of them have a "pay-per-use" model. The total cost would depend on the type of AI tool being used and the number of end-users. However, banks should also take into account the availability of base systems and their ability to connect to AI tools. This would also be an additional investment to those who do not have the necessary base systems in place.
