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Today, artificial intelligence has changed the perception of the logistics business by making supply chain management more smooth.   With cognitive automation, AI plays a critical role in saving time, lowering expenses, enhancing productivity, and improving accuracy. It allows us to save time and money by automating various time-consuming operations and assisting with demand forecasts.

 In addition, AI aids in logistics route optimisation, which helps to reduce shipping costs, which in turn helps generate more profits.  Logistics companies may use AI to produce datasets and use data platforms to rule trends and anomalies. 

In this audio series, we explain how AI-led solution play a paramount role in logistics. 

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Artificial intelligence and computer vision have radically changed the warehousing and logistics industries today. AI has played a critical part in the industry’s success as a result of the digital revolution.   

The logistics industry’s networked nature lends itself well to adopting AI technologies, increasing efficiency from beginning to finish. These technologies may bring about various changes, including process optimisation, cost reduction, increased productivity, reduced manual errors, and the prediction of future possibilities and difficulties.  

In this logistics insight video series, We explain the role of artificial intelligence and how it benefits the logistics sector. 

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Several warehouse and logistics are striving hard to improve efficiency by streamlining workflows, automating procedures, and minimising the time and resources.   Warehouses and logistics centres have various complicated and iterative tasks, each coinciding. While there are numerous opportunities to enhance productivity, determining where your efforts will pay off can be difficult. 

Warehouse productivity is significantly impacted by advances in logistics technology, such as autonomous robots, innovative AI and machine learning solutions, sophisticated warehouse management software, and other technological solutions.  

This AI-enabled logistics insight audio series provides you with three simple ways to improve shipping efficiencies and profitability in warehouse and logistics.  

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An efficient warehouse is critical for customer satisfaction because it helps ensure quick, accurate shipments. Improving warehouse efficiency is as much about common sense and the proper storage equipment as it is about fancy picking algorithms.  The amount of time, effort, and money you spend getting things into and out of your warehouse is referred to as distribution expenses. The delivery cost is simply one part of the total distribution cost.   

Distribution expenses are influenced by the value of the goods you send, as well as losses due to product expiry or incorrect storage, as well as your facility’s operational expenses. To save shipping expenses, look at all distribution parts and see where you can save money.  

In this audio series, we have provided you with some potential tips to boost shipping efficiencies in your warehouse.  

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Due to the rising number of online orders, numerous warehouse owners are rushing to deliver products to customers’ doorsteps. When most people think of warehouse automation, they envision it in the far future.   

However, automation technologies are already in use in today’s sophisticated warehouses and distribution centres. Automation technologies offer enormous potential to assist operations in becoming more efficient, precise, and productive, in addition to just lowering costs.  

Through this audio series, we help you understand how warehouse automation aids business to increase profitability.  

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One critical section of edge AI-based computer vision products is the development process, which dictates that the product will succeed or fail. This approach comes from five separate phases: selecting the right hardware for development, selecting an existing AI model, training and tuning the model, the proof of concept (POC), and the final product/solution design.   

You get a glimpse through this video of how the standard edge AI product development cycle looks like.  

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VisAI labs is focused on developing practical Edge AI-focused Computer vision and Machine Learning algorithms successfully. VisAI labs’ is right now building edge-based face recognition to guarantee the privacy and most significant level of granular insights on the individual followed by people tracking to identify, monitor, and recognise people indoors and outdoors by providing improved data protection by delivering inference on edge. VisAI Labs also provides vehicle monitoring algorithms to detect and monitor the vehicle counter and the parking lot.  

In this video, you get an insight into How the VisAI platform helps in fast prototyping for Edge AI-based computer vision applications? 

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Nvidia DeepStream is an efficient and versatile software development kit (SDK), suitable for a wide variety of applications across a wide range of industries;  It can help you build an efficient pipeline for fast-transfer image data as well as computer vision on edge with its transfer learning toolkit.  

In this video, you get a deep insight into How Nvidia DeepStream SDK helps in fast prototyping for Edge AI-based computer vision applications? 

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Distinguishing the right algorithm incorporates a variety of combinations. During the algorithm development phase, most engineers would choose an existing algorithm or model. Instead of starting from scratch, it all depends on the requirements and needs and how we train and prepare the datasets before applying them to our final solution. For this, we incline Nvidia's deep stream architecture to supply a set of algorithms that can help you move forward.   

At the end of this video, you get a better idea to define the best algorithm for fast prototyping edge AI-based computer vision applications.  

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Analyzing the type of processor based on the customer’s prerequisite is the crucial phase in picking the right processor that bolsters the Edge AI application. The Nvidia Jetson processors set is designed to accelerate the application for Edge AI in particular. These Nvidia Jetson processors deliver performance and efficiency for the speed and power consumption of the computer software.   

This video gives you a precise clarification of which Nvidia processor is used for which Edge AI-based computer vision applications.  

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