Top 10 AI Computer Vision Applications Used in Supply-Chain and Logistics.

In the intricate network of supply-chain management and logistics, the incorporation of Artificial Intelligence (AI) and Computer Vision is ushering in a new era of efficiency, precision, and resilience. This article delves into the top ten ways in which AI and Computer are currently being applied in supply-chain and logistics.

1. Inventory Management:

The application of AI and Computer Vision in inventory management represents a transformative leap in supply-chain alongside its efficiency. The dynamic duo provides real-time visibility into inventory levels, to automatically monitor and track stock volumes and prevent potential issues of overstocking (surplus) or shortages. It does so by visually scanning and counting products on shelves or storages, to acquire up-to-date information; thus streamlining the ordering process whilst minimising the carrying costs associated with excess inventory and the risks of stockouts.

Source: TechTalks.

2. Demand forecasting:

In demand forecasting, AI algorithms coupled with Computer Vision capabilities, can help in analysing large datasets, including historical sales data, current trends, and extraneous factors, to generate highly accurate predictions, anticipating fluctuations in demand and allowing for more precise inventory planning and management. Therefore enabling businesses to optimise inventory levels, reduce stockouts, and improve overall supply-chain planning.

3. Product Sorting and Tracking:

AI and Computer Vision are also immensely paving the way for more efficient and precise warehousing operations, as they provide machines with visual perception, enabling them to navigate and interact within warehouse environments. Let us take AI-driven robotics for example. When guided by Computer Vision, these robots can perform a variety of tasks such as: tracking, and sorting of parcels or packaged items with remarkable accuracy. 

Watch these robots in action as they sort hundreds of parcels per hour:

 

4. Automated Palletisation:

Similarly, robots propelled with AI and Computer Vision systems can automate the palletisation process by identifying and arranging items on pallets efficiently, in preparation for shipments or transportations. This has proven to reduce the time and labour required for manual palletization and enhances overall warehouse productivity.

Amazon Case: Vision-Guided Robots

On October 18th, 2023, Amazon decided to take upon an initiative in Houston, Texas in the United States of America, to upgrade their robots with ‘Sequoia’ – an integral technology between AI and Computer Vision with the aim of discovering new strategies in assisting employees in the palletisation process and in the delivery of products to their end users.

That being said, with over 750,000 robots working hand-in-hand with their employees, they were capable of storing inventory in their designated places 75% faster, as well as reducing order processing time by 25%; thus improving their overall customer delivery services alongside shipment predictability.

See how Amazon is applying their newly Computer Vision equipped robots:

 

5. Barcode and QR Code Reading:

The integration of AI and Computer Vision additionally, aids in the continuous reading of barcodes and QR codes on packages as a means of keeping records on the products that are moving in and out of the warehouse, and hence streamlining the order fulfilment process and reducing manual errors.

6. Quality Inspection:

Quality inspection can be perpetually automated and sublime through the Computer Vision and its capabilities. The technology allows for visual scrutiny on each item that it scans and benchmarks against industry standards, to identify defects, damages, or discrepancies in products, ensuring that only high-quality items are shipped. This is especially crucial in industries with strict quality standards.

7. Vehicle and Fleet Management:

Furthermore, Computer Vision systems can be utilised to monitor and manage the condition of vehicles and fleets; allowing the live tracking of vehicles’ location, assessing the vehicles for possible maintenance requirements, and ensuring their compliance with the safety and regulatory standards.

Source: iStockPhoto.

8. Security and Theft Prevention:

Security devices such as surveillance cameras can become a lot smarter when equipped with AI and Computer Vision, as they can now intelligently analyse images and videos for any suspicious activities, recognise unauthorised access and theft, as well as other forms of potential security threats; thus safeguarding warehouses and logistic facilities.

Source: VentureBeat.

9. Cold Chain Monitoring:

In industries such as food and pharmaceuticals, AI and Computer Vision technologies can be very useful to monitor temperature-sensitive shipments in real-time. This ensures that products are transported and stored under the required temperature conditions, maintaining quality and compliance throughout the entire shipment trip.

10. Document Recognition and Processing:

Last but not least, Computer Vision systems are proven to be quite beneficial when it comes to recognising and processing documents such as invoices, shipping labels, and customs documentation; as such automation reduces manual data entry, minimises errors, and accelerates document processing.

Bridging The Transformation With Tuba.ai:

Interested in implementing any of the above mentioned applications?

Or better yet, why not customise your very own AI and Computer Vision application as a means of refining supply-chain and logistics?

Do so now, with Tuba.ai!

Tuba.ai is a No-Code/Low-Code automatic machine learning tool that allows users, especially those with minimal AI or coding experience, to effortlessly design and develop AI Computer Vision applications that will transform various areas of the supply-chain.

Select one or more of the listed, top 10 AI Computer Vision applications, and start personalising it for unique and efficient logistics and supply-chain practices.

Its user-friendly platform and modular feature, ensures a step-by-step guidance throughout the entire machine learning process, encompassing tasks from labelling and training datasets to seamlessly deploying your model.

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