North America, China, Japan, India are leaders in logistic automation and inventory management innovations as they are countries of biggest port-cities that receive enormous amounts of delivery from all over the world. Not only these countries but many others use AI in Logistics to find quick solutions for operations and make the right decisions not losing to competitors. AI replicates intelligent behavior in autonomous robots. It uses algorithms with minimal human intervention to leverage security at warehouses, plan routes wisely and forecast product demand to win sales.
What is Artificial Intelligence in Logistics?
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Artificial intelligence is known since 1956 and was firstly used in inventing board games like checkers or backgammons. Then researches in AI have come closer to Machine Learning in Logistics and Deep Learning that have a lot to do with neural networks. More precisely deep learning is trying to mimic the human brain – see and differentiate objects based on their attributes. So, AI in Logistics has all chances to classify images concerning one another to identify the content. Let’s say the content would be what’s placed on a shelf at the warehouse.
Deep learning as a subset of machine learning has become a breakthrough technology in image recognition and allowed logistics companies to get rid of slow-moving handwork. Two of the key branches of deep learning technology are:
- Object detection
- Instance segmentation
Deep learning led to significant improvements in the quality and speed of the supply chain. AI systems can learn from human decisions, copy data from one system to another, fill in the web forms, make judgments, and interact with humans. Thus, Artificial Intelligence in Logistics is designed to see things just like humans which allows substituting manual time-consuming repetitive work with automatic machines and reach several times higher performance.
Robotic systems to modernize warehouse management
Warehouse management faces challenges of limited space and an increasing amount of transfers at existing manual operations. Warehouse businesses can maintain efficiency with the help of robotics and AI. Imagine autonomous 4-meter robots that move along the aisles, stop at necessary places and load shelves with goods. AI is used to create robots for warehouses which help with:
- Faster pallet transporting and handling
- Reduction and optimization of aisles width
- Easier double deep packing
Such robots are AI-based and are designed to pick multi-size packages, use visual recognition to identify goods and control them via custom software. Just look at Amazon robots warehouses – they handle billions of goods each year and this is all due to smoothly driving robots that move autonomously without crashing into each other as if being real humans who understand where they are going and what row of shelves they need to place packages. Computers track the performance of robots and their actions, which excludes risks of “losing” goods and uncontrollable inventory.
AI in Logistics for Demand Forecasting
How to estimate the number of products that users will want to purchase? Analyzing in stock and out of stock goods AI can predict demand, provide accurate forecasts for markdown and help with discount optimization. Decisions on inventory and sales can be influenced by trends, so AI prediction is part of predictive analytics. It takes some marketing data and real-time data of inner sources of organizations to analyze, compare and give out some estimations.
Logistics organizations operating big data can benefit from predictive analytics in making data-driven and well-thought decisions. They will experience accurate sale estimations for the future, know risks in shipment, or discover shortages of which products can happen over a while.
MCkinsey reports that AI can reduce errors by 30% in the supply chain and decreases warehousing costs by 40%. Consequently, it leads to better sales due to predicted out-of-stock situations.
Organizations that use big data tools and AI avoid ending up with stocks of goods that are no longer fashionable, needed and stay unsold for months. Even analyzing short periods of sales, AI systems can help companies successfully manage unexpected spikes in demand.
Recall how the youth all over the world was captivated by spinners and every young boy wanted this toy in his hand after watching a viral video. Imagine the number of increased demand. Merchants would not be able to invent something to accelerate production and delivery. Once again AI was the first to predict the popularity of spinners and prepare companies for unexpected news. According to DHL Logistics of Things:
According to a report on artificial intelligence (AI) by DHL and IBM, computers already knew that the demand for fidget spinners was about to explode before the video even became viral.
AI helps organize freight shipping
Cargo or air freight shipping across oceans, let’s say from America to Europe, are not easy to organize. You have to find a transportation company at the best price, prepare proposals with custom expert agents, negotiate with sales representatives, provide route planning, avoid additional costs and plan movements with sea or air carriers in opposite directions. AI cloud-based platforms generate in seconds transport providers and display data such as price, time, option. AI optimization helps select the most profitable journey and regulate situations such as delays or cancellations.
Machine Learning in Logistics
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Machine learning in logistics is used for intelligent route optimization, intelligent sorting and predictive network management. The latter means machine learning models process internal data, understanding different parameters and temporal indicators like departure days, airline performance and predict time delays in air freight shipment. Airfreight companies always have flights planned and exact scheduling when and which planes are the first to freight. Logistics startups use AI to reduce trucking costs.
TNX company says tier software reduces trucking costs by 7-12%. The key idea of a developed product by TNX is to apply machine learning to decide on a tendering strategy. Time of making offers, and whom they are addressed, pricing and other processes are automatized.
Intelligent sorting is the greatest example of AI and ML usage in logistics. It is the effective sorting of parcels by high-speed AI robots. These systems move on the conveyor by the algorithm and have in-built cameras, sensors to scan things nearby and receive necessary data: logos, labels, forms and sizes of parcels. The Finnish company Zenrobotics have developed AI-powered robots which can sort 4000 items per hour.
AI in robot-delivery services
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Daily, supply chain companies generate a high volume of data. Enabling applications to predict changes as well as a vehicle to perceive environments is impossible without Artificial intelligence. Startups, effectuating last-mile delivery robots, have already developed battery-powered autonomous vehicles.
Starship company creates autonomous small robots on six wheels to reach urban areas and deliver food to people. Amazing Nuro startup launched by former Google engineers is also worth your attention: mini electric vans with sensors carrying your food at pretty high speed. AI-powered systems are taught to predict the speed of autonomous vehicles and fuel consumption based on historical data from other vehicles’. AI systems understand weather conditions and differentiate obstacles on routes such as pets, people, cars, etc.
Inventions even know how to comply with road regulations and avoid speed limits. The demand for autonomous delivery vehicles has largely increased during the global pandemic caused by covid-19. Robots in Logistics certainly enlarge the capabilities of companies who struggle with satisfying clients with fully personalized and fast delivery.
AI-driven voice assistants
Misunderstanding in daily communication between two people is estimated to be 6%. AI shows a word mistake rate of 5%. It means AI is starting to surpass the intellectual capabilities of humans making fewer errors in ongoing processes. Text processing is the most mature capability of AI. Speech-to-text tools and speech recognition apps are used widely in logistics in automatic documentation filling and other automated processes.
Warehouse management benefits a lot from AI voice assistants allowing personnel to receive data any time they need it, communicate issues on the spot, react to challenges without handling paper documents and spending hours on finding a solution.
AI monitors performance and security
Predictive risk management decreases the likelihood of occurring issues at warehouses or during transferring goods. Technology, engineering, manufacturing leaders have to be sure of the sufficiency of processes and know in advance problems with material or equipment shortages or poor labor allocating. Predictive risk assessment can tell about traffic in cities, explore weather conditions or any breakages at systems used by workers. So, this is one more application of AI – to control the state of equipment for production. Usually, AI robots or systems have in-built sensors that define the state of equipment and provide visual or audible notifications in case of problems.
Benefits of AI in Logistics
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How does AI in Logistics work? AI performs a computing technique that selects necessary data pieces and conducts logistics data analysis. In the case of Logistics, data is coming from inventory management operations, transportation processes, etc. Logistics analytics turns typical logistics data into useful insights.
Companies have recently understood that Big Data in Logistics can help improve logistic automation. Under Big Data in logistics, we understand historical data gathered from processes over years. Big data tools can not tell exactly what to look for in massive amounts of information as typically they collect and sort the data not presenting the final solution. Logistics companies have already realized how much structured data they have and how many benefits they could take from data analysis.
Human intervention is not enough to establish the right correlation between large sets of filtered data, and continuously analyze them together with daily changing trends, and external conditions coming. AI turned out to be a progressive and highly effective fast solution to perform these tasks in logistics. There are plenty of conditions like good or bad weather, the impact of geographical location on sales, and logistics. Let’s take the COVID-19 threat, an unexpected turning point in businesses. AI-based applications help evaluate a lot of data, compare it to external conditions of the same period, and use its intelligence to recommend some actions.
AI should be praised for better logistic automation. Anyway, we as customers who buy things online can not see the scales of modern warehouses with robotic systems, for example, but they all influence how quickly things are delivered to you, in what state, and even whether you’d want to order via that store once again.