Machine learning allows everyone in the supply chain to save costs through reduced operational overhead and risk management. Allows it helps with supply chain forecasting, delivers results and insightful information quickly, and improves customer service.
5 Supply Chain Machine Learning Examples
There are many ways a business can use machine learning when it comes to the supply chain. These are just a few examples of how supply chain machine learning can help your business:
Any business needs to have full control and management of its inventory. However, this can be a costly process. Luckily, machine learning can help with inventory management. It can help solve any under or overstocking problems your business might be facing. Additionally, machine learning can predict demand growth. This allows you to prepare your inventory in advance.
For an accurate demand forecast, you will need to provide a wide range of data. However, if you do not have that much data, machine learning offers several methods of how to solve the issues. This can include data augmentation, which allows you to increase the diversity of data available for training models without collecting new data. Additionally, you can use incremental learning, which allows the machine to slowly learn about your business without a huge amount of data. Lastly, there is reinforcement learning, which uses rewards and punishments as signals for positive and negative behavior.
When it comes to warehouses, machine learning can be used to automate manual work, predict possible issues, and reduce paperwork for warehouse staff. For instance, computer vision can control the work of the conveyor belt and predict when it is going to be blocked. Additionally, machine learning can be used to program autonomous vehicles and robots. With the help of guides built into the system, vehicles and robots can receive, pack, transport, and even load or unload boxes.
Through machine learning, it is possible to identify quality issues in the line of production early in the game. With computer vision, a manufacturer can check if the final look of the product corresponds to the required quality level. Additionally, you can use machine learning for predictive maintenance of equipment. Machine learning allows for preventative maintenance of equipment based on real-time asset data, instead of predefined dates on a calendar.
A chatbot can understand keywords and phrases, triggering appropriate replies. They are often used in supplier relationship management, sales, and procurement management. This allows the staff to focus on value-added tasks instead of getting frustrated with answering simple questions.
Many customers want to keep checking on their delivery status daily. Luckily, machine learning can predict the delivery of their package by taking into account all the changing conditions. This allows customers to feel more confident that their package will arrive when they need it. Additionally, this can help retailers find any packages that are prone to issues, automate notifications depending on customer interaction, and even determine when to communicate with customers for maximum engagement.
Originally posted 2022-12-06 11:10:49.