The logistics industry reaches deep into our daily lives. Both domestically and globally, logistics is the engine that keeps the world moving, but it's also the industry most in need of innovation. Ongoing demographic shifts and rising labor costs around the world are putting pressure on the industry's costs. 하지만, Logistics is self-described as the most conservative industry, one of the last to digitize and one that has been slow to improve productivity. But the rise of AI is changing that. Leading logistics companies are now using AI to improve their logistics Robots and other logistics AI are actively pursuing process automation.
Among domestic logistics companies, NAVER is applying AI CLOVA forecast to forecasting logistics demand and optimizing order throughput, and By applying AI to the cargo tracking system, the company was able to increase the accuracy of cargo ship arrival times to 45% better than before. In addition, G-market applied technology to derive the optimal transportation route, even when consumers order different products from different sellers. In conclusion, the domestic logistics industry, faced with marginal costs, is experimenting with various technologies to enhance competitiveness and digital transformation.
In addition, the logistics industry is seeing advances in generative models and is looking to capitalize on them. Large Language Model (LLM) , like ChatGPT is gaining momentum. This allows for a passive and Time-consuming documents and scheduling Automate the process, and Data analytics and machine learning, they believe they will be able to predict delivery times. Also Automatically handle customer inquiries and complaints, as well as being able to quickly analyze large amounts of data to make better decisions. In this article, I will introduce the benefits of using AI in logistics, why it is important, the technologies and use cases applied, and the limitations and ways to overcome them.
Why logistics AI matters
Warehouse automation and robotics
Logistics artificial intelligence is a warehouse automation and robotics system that enables the automation and streamlining of tasks. Robots perform repetitive and mundane tasks, significantly increasing productivity by reducing labor costs and shortening work time. It also utilizes sophisticated sensor technology and computer vision to track the location of a box or product in real time and Detect misplacements or errors. This reduces errors and prevents problems in processing, and also improves customer service quality and response times.
Warehouse automation and robotics are a great way to reduce labor costs. As machines handle repetitive and mundane tasks, companies can reduce the number of workers. This plays an important role in boosting competitiveness with soaring labor costs. Hazards in warehouses can pose a variety of threats to people. However, robotic systems such as robots and autonomous vehicles can work safely in critical situations and help keep workers safe and healthy. Warehouse automation and robotics systems powered by logistics AI have great advantages in terms of flexibility and scalability. Their capacity can be adjusted as needed, and they can easily respond to changing requirements, such as new processes or changing constraints.
Supply chain monitoring
Logistics AI can monitor the health of your supply chain by collecting and analyzing various data sources in real time. Predictive capabilities can be leveraged to proactively detect potential risks and create contingency plans, forecasting information such as estimated time of arrival (ETA) or available capacity of assets and ports.
If your data is of low quality or lacks real-world data, you can use generative AI techniques to create a Generate the data you need. With this data, AI can build predictive models and provide valuable insights. This helps supply chain managers make accurate and fast decisions.
Volatility and uncertainty in the supply chain is one of the challenges faced by the logistics industry. However, the visibility provided by logistics AI is playing a crucial role, as real-time information and predictive capabilities can help organizations become more resilient and agile to react and adapt to changes. In addition, gaining visibility into the supply chain has a significant impact on overall business efficiency. Accurate information and forecasting results in optimized inventory management, order fulfillment, transportation route optimization, and many other tasks that contribute to cost savings and increased productivity. Ultimately, visibility into your supply chain enables accurate delivery schedules and status updates. This provides a seamless communication experience for your customers, increasing customer satisfaction.
Improve logistics efficiency with predictive analytics
Logistics AI analyzes historical data and real-time information to identify and predict demand patterns. This allows businesses to pinpoint the amount and pattern of demand for their products, which can be factored into inventory management and production planning to optimize stock levels and minimize shortages or overstocking. In addition, transportation routes can be optimized by taking into account various factors (road conditions, weather, traffic, etc.). This reduces delivery times and costs, and also improves interaction and coordination between warehouses.
Predictive analytics can detect potential disruptions or risks in advance. For example, predictive modeling of external variables such as natural disasters or social issues can help companies create contingency plans and response strategies, and reduce the risk of service delays or prolonged outages.
Accurate predictive analytics can help businesses address the costly consequences of under- or overstocking. Proper inventory management reduces unnecessary storage and transportation costs while maintaining product availability and service quality. With accurate forecasting, organizations can ensure that the right product is delivered at the right time and location. On-time delivery significantly improves customer satisfaction and helps build trust and loyalty. When insights are derived from reliable data and forecasts, organizations can use them to make strategic decisions. A data-driven approach and real-time information are essential for executives to make decisions and take action in a volatile market environment.
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Technologies used in logistics AI
Logistics is one of the most optimized fields for AI because it deals with a wide variety of goods and processes. It's also a convergence of many different AI technologies. Here's a look at some of the technologies being applied.
- Machine Learning
Machine learning is one of the key technologies in logistics artificial intelligence. It is used to analyze data and learn patterns to build predictive models. For example, it's used to analyze tons of shipping data to find efficient delivery routes or predict inventory levels.
- Deep Learning
Deep learning is a branch of machine learning that uses neural network structures to solve complex problems. It's useful for tasks like image recognition, speech processing, and natural language understanding. In logistics, deep learning is being used to develop things like smart wearable devices that automatically recognize images of products or process voice commands.
- Computer Vision
Computer vision is the art of interpreting and understanding visual information (video, photos). It enables robots to identify products or Track the location and movement of objects in space.
- Natural Language Processing, NLP
Natural language processing is a technology that converts information related to human language into a form that computers can understand. NLP is utilized in tasks such as order fulfillment, answering customer inquiries, and chatbot services.
- Optimization Algorithms
An optimization algorithm is a method for finding the best value of a decision variable based on constraints and an objective function. In logistics, optimization algorithms are applied to transportation route optimization, warehouse layout planning, and inventory management.
- Sensor Technology
Sensor technology is used to collect and monitor environmental data (temperature, humidity, location, etc.). In conjunction with the Internet of Things (IoT), data is collected in real time to help improve logistics processes and detect defects.
- Robotics is the convergence of robotic systems and artificial intelligence technologies, and is used in a variety of areas, from automotive assembly to warehouse operations, to help improve productivity and enhance safety.
Logistics AI Application Cases
Coupang's self-developed logistics system Warehouse Management System (WMS) oversees the entire process from receiving to shipping. As soon as a customer hits the checkout button on their smartphone or PC, the WMS system instantly knows where they're going and what inventory is in each of our warehouses across the country, and automatically decides which center to ship from. In addition to the Within the warehouse, it can provide real-time guidance on which workers (or robots) will be picking up items, what the shortest and fastest route is for picking up multiple items, what size packaging to put them in, and other details that workers need to know.
In addition, Coupang's Random Stow placement method is a system that takes into account the sales volume of each product and the timing of the sale, so that workers can travel the shortest distance. While it looks random on the surface, it's actually structured in a way that maximizes picking efficiency and optimizes worker movement.
By "intelligentization," we mean bringing advanced technologies to the warehouse, such as computer vision, Internet of Things (IoT), artificial intelligence, deep learning, and digital twins. Logistics companies around the world are working on a combination of technologies with the goal of optimizing their industries through intelligence.
LG CNS envisioned a fully automated distribution center that fused intelligence and optimization, and prepared a robotics as a service (RaaS). Enterprise customers can choose only the hardware and software support they need, and use it as much as they need, considering the size and purpose of the warehouse they operate. In particular, we plan to provide logistics automation robots on a subscription basis. Starting with the "Autostore," which can store more than four times as many items as before in a limited space, we have prepared a lineup of robot soluWhat is MLOps, an efficient way to manage the development and operation of machine learning models?tions, including picking robots that use AI technology to identify the characteristics of goods and accurately pick them up, unmanned goods transportation, and autonomous robots that use sensors to detect their surroundings.
LG CNS' cloud-based warehouse control system fuses AI and IoT. Workers can intuitively view every corner of a large and complex warehouse on a 3D screen, and if the warehouse needs to be expanded due to increased volumes, the digital twin can be used as a simulation tool to verify the introduction of new automation facilities in advance.
AI models that detect damage to products often need to be trained with a lot of data to perform well, but many companies struggle to train them because it's not easy to find images of damaged products.
In response, Amazon's research team created a We adopted a strategy of teaching them how to compare the product to its original image. In a nutshell, we used computer vision technology to scan every item that passed through the warehouse. A machine learning model then analyzed the scanned photos to create a discovered hidden patterns and trained it to continuously improve its ability to detect damage. We developed the AI's ability to subjectively determine whether a product is damaged.
This led Amazon to build a way to use AI to prevent damaged products from being shipped to customers. Christoph Schwertfeger, applied sciences manager at Amazon Fulfillment Technologies, explained that the AI system is three times more effective than manually identifying damaged products.
Limitations of AI in logistics and how to overcome them
While logistics AI offers a lot of innovation and efficiency, it still has some limitations.
Expenses and Resources
Warehouse automation systems and robotics technology have a significant upfront investment. Introducing robots or automation equipment requires a lot of money for hardware, software, system implementation, etc. Maintenance and upgrades of robotics systems also require additional costs and resources. Equipment breakdowns and software updates require constant attention, potentially causing downtime and additional costs.
In logistics operations, We need seamless interaction with people. However, AI systems developed to date often operate under human supervision and lack the ability to communicate and collaborate autonomously.
Lack of data and dependency
Logistics AI relies on accurate and reliable data. However, the absence, inaccuracy, or low quality of real-world data can make it difficult to achieve accurate predictive power and analytical results. Data collection and management processes need to be improved.
Even as the logistics industry becomes more automated, Information sharing and collaboration between multiple organizations is still needed. Compatibility issues between different systems and a lack of agreement on data sharing processes can limit the ability to achieve holistic visibility.
The difficulty of prediction
Predictive modeling can be difficult to fully account for external factors (economic conditions, political fluctuations, etc.). These variables can affect forecast outcomes, and accurately capturing and modeling external variables is a challenging task.
Predictive modeling can also be a constraint in environments with rapid change or uncertainty. Unexpected events have the potential to degrade the performance and reliability of a model, and models and algorithms need to be flexible and adaptable to respond to these changes.
How to overcome
These limitations must be considered when adopting and applying AI in the logistics industry. However, as technology advances, these limitations are likely to be overcome or minimized. The AI industry is continuously researching the following methods to overcome the limitations of AI in logistics.
- Get enough quality data
- Introduce more sophisticated and flexible modeling techniques to handle complexity
- Improve human-machine interaction
- Enhancing safety and ethical considerations
- Design a flexible system that can adapt to change
Conclusion: Accurate, fast decision-making with AI is critical to meet the rapidly changing distribution and logistics market landscape and customer demands.
Adopting AI is essential to realize the keyword 'hyper-automation' in Industry 4.0
Gartner's keyword has garnered a lot of attention. Hyperautomation is an approach that integrates different technologies, tools, or platforms to achieve a specific purpose. In other words, hyperautomation is a significant evolution of traditional automation processes with advanced technologies such as AI. As streamlining processes and improving accuracy has become a major challenge in recent years, hyperautomation has been recognized as a major opportunity for businesses to reduce operational costs by as much as 30%.
Global research firm Coherent Market Insight (CMI) explained that hyperautomation can be optimized in industries that collect and handle large amounts of data, such as manufacturing, logistics, and finance. The argument is that logistics companies can strategically adopt hyperautomation to maximize the efficiency of their supply chains. This is because logistics companies are a field where various technologies are concentrated, such as predicting service costs and future demand, optimizing delivery routes, and building a preemptive response system based on predictions.
For companies in the logistics industry to remain competitive and grow in the present and future, artificial intelligence is becoming essential. It makes a significant contribution to increasing the efficiency and accuracy of logistics processes, while reducing costs and improving service quality. AI enables real-time decision-making and strategic planning through high-quality data collection and management. Companies need to actively adopt and implement these technologies to optimize complex and diverse logistics tasks and prepare for future uncertainties.
It is also important to overcome the limitations of A.I. technology through continuous research and development and upgrade it in line with the latest trends. It is also increasingly important to secure visibility and build networks through information sharing and cooperation with various organizations. This will play a decisive role in securing management advantages and implementing growth strategies.
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- ChatGPT and the Like: Artificial Intelligence in Logistics
- The True Role Of AI In Logistics
- How LG CNS Suggests Creating a Smart Distribution Center
- How Amazon uses AI to prevent damaged products from arriving on your doorstep
- Logistics AI Use Cases in the Age of Digital Distribution
- WMS Logistics Technology Secrets That Power Coupang
- '6 trillion in logistics investment', 'Daegu FC' heirloom, the pinnacle of Coupang's logistics infrastructure
- [Exclusive] Coupang's Kim Bum-seok's AI logistics center experiment, this time in Dongtan | 아주경제
- Part 1. Hyperautomation, the automation technology that will shape the future of logistics