Supply chain management has always been a complex process, involving multiple players, levels of transport, and resources. Over the years, tremendous progress has been made in streamlining the process of transportation, communication, and tracking. However, there are still many challenges faced by supply chain management professionals. One of the most significant problems is tracking the quantity and movement of goods and materials across the chain. This is where artificial intelligence (AI) comes in. With AI, supply chain management can be automated, and efficiency can be improved by identifying and resolving issues quicker, providing real-time data, and enabling companies to stay on top of the inventory management process.
Artificial Intelligence is the branch of computer science that deals with the creation of intelligent machines that work and think like humans. AI can be used to automate and optimize many supply chain functions, from demand forecasting and inventory management to transportation planning and delivery. In this article, we will discuss how AI is used to improve supply chain efficiency.
Machine Learning in Supply Chain Management
Machine learning is a type of artificial intelligence that involves the development of algorithms and systems that highlight patterns in data. In the context of supply chains, machine learning can be used to find insights and add intelligence to the logistics processes. By using machine learning in supply chain management, companies can get access to a large volume of data and analytics.
Machine learning is used to track a variety of data such as demand patterns, inventory levels, sales forecasts, and lead times. Through larger data sets, machines are able to track patterns and attain greater accuracy and forecasting abilities. Companies like Amazon and Walmart are taking advantage of Machine Learning by predicting and tracking shopping trends. Walmart also uses machine learning to predict the products that their customers are going to buy regularly. They are tracking customer behavior and making better predictions based on the results.
Optimizing Supply Chain Operations with Robotics
Robotics, another application of AI, is perfect for the physical and dangerous tasks in supply chain management that require precision and efficiency. Many logisticians use robotics as an automated assistant for inspection, warehousing, and transporting purposes. Robotic automation helps eliminate low-value warehouse work and tedious labor tasks that can be time-consuming.
For instance, in the manufacturing phase, trucks carrying consumer products were manually loaded and unloaded by a crew of workers. Today, advanced robotics and warehouse automation technologies along with machine learning mean that trucks are loaded and unloaded autonomously. This saves labor costs and reduces the chance of human error.
Predictive Maintenance
Predictive maintenance is an IVR (Intelligent Vehicle Routing) algorithm that determines the best route for delivering a package or goods, allowing couriers to make fewer trips, cut delivery time and save fuel costs. With predictive maintenance, the machines and equipment used in the supply chain are monitored continually to gain insight into maintenance needs before breakdowns and system failures. This method is a recent development in the supply chain management world. This method allows companies to anticipate breakdowns, save money on maintenance costs, and reduce downtime by minimizing the amount of equipment required in case repairs are required.
Smart Warehousing
The warehouse is an integral part of the supply chain management process. In recent years, smart warehousing robotics has evolved to automate the warehousing and logistics process. Many companies have implemented smart warehousing using AI applications such as Robotics, IoT, RFID (Radio Frequency Identification), and modern warehouse management systems (WMS). Here are just a few examples of how AI is improving the functionality of warehouses:
• Picking and packing – the robot is trained to pick specific products from shelves and place them in mobile carts which are then delivered by an attendant to loading docks where the goods are shipped.
• Efficient management systems – RFID tagging is used to track product location, item information, and warehouse details. This ensures that inventory levels are accurate, and workers don’t have to carry out manual inventory counts.
• Environment and health and safety– Using real-time data allows predictive analysis using AI that can help diagnose and correct problems before becoming issues.
Using AI for Carrier Selection
One of the significant challenges faced by logistics managers is carrier selection. The challenge lies in the selection of carriers that deliver the products efficiently, on time with minimal errors and damage rates. This selection process involves analyzing several different factors, such as cost, customer location, capacity, and delivery timetables to determine which carriers will optimally align with the company’s goals.
AI technology allows shippers to choose carriers in a more data-driven and optimized manner. AI tools use different algorithms to analyze past performance data and other factors to determine the best carrier for each shipment based on the data. This ensures that the right carrier is selected, improving delivery speed, quality, and efficiency.
Conclusion
Artificial Intelligence and automation have brought significant improvements to supply chain management, including cost reduction, increased efficiency, and better visibility across the entire supply chain. Machine learning, robotics, predictive maintenance, smart warehousing, and AI-based carrier selection all offer an opportunity to revolutionize the sector. There is, however, still a long road ahead in developing these technologies further, and it is vital for companies that want to enhance their supply chain management processes to continue exploring them further. The application of artificial intelligence in supply chain management has limitless potential and has already transformed many sectors.