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Top 4 ways AI is changing the Logistics operations- Use Case

Artificial intelligence is a buzzword in the current scenario of logistics management. Its widespread implementation across various verticals is so apparent today. The exciting implementation of AI technologies by Amazon to opt for automated warehouse solutions, last-time delivery drones by leading retail stores like Walmart and Amazon- all have augmented applications. Complex skills required to accomplish logistics jobs can also be transformed using this technology. As logistics planners are geared up for leveraging this too to execute high-skilled activities, it contributes to active result-oriented business results through cost-reduction, time- saving, and elimination of manual errors. We can breathe a sigh of relief as Artificial Intelligence can transform the way freights move across the geographies. We cover five interesting and exciting use of AI in logistics.

Damage detection with computer vision

When it comes to transferring cargo around the world, paying attention to damage reduction is central to improving productivity in logistics. And it is computer vision-based AI has provided us with state-of-the-art technology to bring about a change to the vision of how we tend to serve customers. In context to damage identification in logistics, the technology has become so relevant to reduce damage and avoid customer churn. Renowned Logistics giants are using this technology to identify damage much before it is likely to happen. The computer vision-based AI technology enables damage identification in multiple ways. By leveraging this technology, you can trace the damage depth, the type of damage, and take an actionable approach to reducing further damage to your service. The entire process happens faster than ever before.

AI-integrated tools can improve the efficiency of the freight carriage train wagons and prevent damages to ensure uninterrupted service. To carry the process, tools are programmed to fetch data from the installed cameras along the train tracks. These cameras capture images of the wagons, process them using AI-vision capabilities. As a result, it improves the accuracy rate up to 90% and reduces the damage to the wagons.

Besides the recognition of damage, the computer vision AI can help unload a stack of inventory less than 30 minutes.

Logistics Robots to foster automation

Research established that the worldwide sales of supply chain and logistics are expected to grow $22.4 billion by 2021. And this is augmented by the use of robotic process automation. Using AI-based robotic process automation, it is easier to trace and move inventories in the warehouse. It also improves the efficiency of moving and sorting oversized packages at the warehouse facilities. In a process to put robots into action, they are programmed with deep learning algorithms to make autonomous decisions for a dozen works with locating, identifying, picking and optimizing work. Robots can ease the process of picking and accomplish the task in less than .2 seconds and move the parts to the expected location.

Improved demand and network planning

Networking and predictive demand planning is key to boosting the efficiency of the logistics using the capabilities of AI. Leveraging this tool enables a better understanding of accurate demand forecasting and networking planning and helps execute proactive operations across the logistics channels. As you get to predict the expected future occurrences much earlier, it can improve the optimization of the vehicles by directing them to the locations where the demand is higher. With AI, it is easier to analyze data to its fullest potential and improve the assessment of future risks and enhanced techniques to avoid risks, and reduce operational costs. Logistics can better use resources and maximize benefits using AI.

These days, advanced logistics services are using a wide range of parameters to air freight. Building a model with the machine learning-based internal data can help predict the average duration of daily transit. This helps gauge the exact duration of delivery per week in advance if they are going to fall or rise. The internal-based machine learning system can improve the prediction of the air freight delivery status depending on key factors like weather and operation failure.

AI capabilities are a good technique to safeguard against the risks as well. A system orchestrated with machine learning and natural language processing can better understand the conversation taking place online platforms and social media channels. This helps analyze data and discover the real meaning of the sentimental elements of the matters, resulting in a better understanding of future risks. Thereby, AI capabilities help logistics assess material shortage on time and find real issues in the site.

Small logistics operations can benefit from the machine learning-based systems by using them with their existing solutions.

Optimization of the future performance

Logistics can execute highly accurate predictions and optimize future performance better than ever before using AI capabilities. Leveraging big data in combination with AI, they can bring transparency to the overall logistics operations. It also helps improve the future performances of the supply chain and logistics. As per the study, 81 percent of logistics operations and 86% of third-party logistics prefer using big data as a core competency tool for their supply chain operations. This is a complex and diverse sector that depends on a wide variety of parts and vehicles. Big data helps improve the supervision capabilities across all the operations.

As this technology helps improve route optimization, it is expected to help save millions of fuel annually. Besides, it is a more efficient tool to ferry last-miles deliveries to the destination without affecting the customer experience. Most of the time, logistics do not have access to user data or figure to implement. There come algorithms to extract structured or unstructured data from different sources to enable the identification of issues and establish better transparency across the business. For instance, shipment data can be handy to predict precise deductions about unknown quantity. AI is competent to work only with 5-10% of accurate shipment data to establish some metrics. Using this data, it helps detect accurate amounts of quantity to be loaded in the vehicles. Thus, it helps optimize the use of vehicles to the fullest potential.

This industry-changing phenomenon is simply groundbreaking. The most interesting fact about AI in the industry of logistics and supply chain is that they can do more than these expected activities explained in this blog. SynergyLabs is a tech company that uses machine learning and natural language processing to offer AI consultancy services. To transform your business in the overall operations of logistics, you can get in touch.

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