How Machine Learning Optimizes Supply Chain?
The Role Of Machine Learning In Supply Chain
Machine learning (ML) is a revolutionary technology that helps industries optimize their day-to-day processes. ML technology has made its mark in supply chain optimization and maintenance.
Supply chain optimization or maintenance requires the business to examine the data in real time and explore sales opportunities constantly. Whether it is a semi-automated or manual process, Machine learning technology now does not require manual intervention in the supply chain.
ML algorithms regularly analyze the data of the supply chain to derive new patterns. It helps businesses determine new opportunities to optimize their supply chain management processes. ML algorithms process data using limit-based modeling to find the set of factors affecting the supply chain with data presence-accuracy.
According to the research reports, 79% of companies with well-optimized and high-performance supply chains achieve higher revenue growth. Machine Learning in the supply chain offers optimized supply chain operations and saves operational costs.
In this blog, we have compiled the best information that lets you be aware of why is machine learning important in the supply chain and how the use of machine learning in supply chain management ensures business benefits.
Why Is Machine Learning Important In Supply Chain?
With a focus to optimize the efficiency of supply chain and logistic operations, manufacturing, retail, real estate, healthcare, e-commerce, and many more industries are switching to ML applications.
Let’s look at how machine learning will address the best solutions for solving complex challenges in logistics and supply chain industry.
- ML applications assist companies in maintaining adequate quantities of products as per the demand
- Faster and reliable deliveries
- Delivers insights into sales data and helps companies in exploring business opportunities
- The role of Machine Learning in supply chain in reducing costs and improving business efficiency is incredible.
Top ML Use Cases For Supply Chain Management
The use of Artificial Intelligence and ML can streamline entire supply chain operations. A few of the top use cases of machine learning in supply include production planning, inventory levels management, quality checks, orders management, demand forecasting, payment collection, logistics visibility, etc. Like these, the adoption of ML will be the best solution for many challenges in logistics and supply chain industry.
Hence, the role of machine learning in supply chain and logistics will not permit these applications. Monitoring fleet movements is also the best application of machine learning in supply chain industry.
Advantages Of Using Machine Learning In Supply Chain
Here are a few top benefits of machine learning in supply chains.
- Accurate demand forecasting using the predictive analytics feature of ML technology
- Using AI, ML solutions are used for automated quality inspections to find product defects
- Great visibility across the supply chain and distribution
- Reduces the complexity in production planning and optimizes demand forecasting operations
- Minimizes delivery times and improves customer experiences
- Mitigates the risk of overstocking and ensures better warehouse management
- Using IoT power, Ml apps would help in tracking the fleet 24*7
These are a few significant advantages of using machine learning in supply chain operations.
Here are the best examples of how companies are benefiting from ML solutions.
XPO Logistics Deploys Machine Learning To Optimize Supply Chain:
As the corona virus epidemic continues to arouse interest in e-commerce purchases, XPO Logistics (NYSE: XPO) is promoting technological solutions to meet the demands of carrier speed and agility, along with reverse logistics and inventory management.
The honorable chief information officer of the transportation and logistics giant, Mr. Mario Harrick had recently discussed the company’s technical strategy during a fireside chat with the president of Freight Waves named ‘George Abernathy’. This tech conversation took place at the American Shippers Global Trade Tech Summit.
Harrick said that “The way you implement on a rapid supply chain, on a more efficient and effective supply chain, is by using our technology”. He also said, XPO Company invested 500 million dollars on years on its proprietary technology systems, focusing on four categories.
The company uses AI and ML technologies to analyze consumer demands and estimate inventory for all its retail customers. So there’s a better way for drivers, offering end-to-end shipment visibility and sequencing optimization in other applications, he said.
XPO Connect, the digital freight market of the company has over 60,000 carriers, and the organization is using machine learning technology to become smarter in helping carriers, and shippers sell and buy capacity. The latest COVID-19 dashboard enables customers to visualize the impact of the epidemic on the supply chain.
Another field of interest is warehouse optimization. As shippers switch from moving pallets for retailers to products purchased individually for consumers, they require more efficient warehouses. Autonomous robots are one of the best solutions that make item picking safer and more efficient.
Also Read: Uses Cases of AI in Supply Chain Management
Hence, the use of machine learning in supply chain management is also increasing across leading brands like Microsoft, Alphabet, and automotive car manufacturers to improve the efficiency of their supply chain and logistic operations.
Machine learning is a booming technology that laid the foundation for the next-generation logistics and supply chain ecosystem.
As we discussed in this article, the benefits of machine learning in supply chains and transportation visibility are incredible. ML offers insights into improving supply chain management performance through flawless delivery management, improved customer experience, enhanced inventory planning, and optimized cost.