4 use cases for machine learning in the supply chain | TechTarget

Oct 24, 2024

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Machine learning can help companies optimize their supply chain, and some specific use cases are best for ensuring companies are receiving the most benefits from the technology.

Machine learning is a subset of AI. An ML algorithm is trained on data, makes decisions based on that training, and learns from the results. Machine learning can sift through huge volumes of data and identify areas for potential supply chain improvement.

Learn more about the benefits of machine learning in the supply chain and some use cases for machine learning in the supply chain.

Here are three potential benefits of using ML for supply chain operations.

Machine learning is particularly effective at analyzing large, diverse data sets, so an ML algorithm can search for potential optimizations across the supply chain.

For example, a machine learning algorithm can fine-tune delivery routes to ensure that products arrive on time and in good condition. ML can also predict potential future problems with delivery routes, such as bad weather or traffic, and offer suggestions for ways to prevent those issues.

Machine learning can analyze data and present pertinent conclusions in easy-to-digest form, which can make supply chain planning easier to carry out.

ML can also help workers quickly find needed data. For example, a machine learning algorithm can help an employee identify the location of a specific product by sorting through all product inventory, then providing the location.

The improved supply chain efficiency and visibility brought about by ML benefits customers, because these improvements can lead to customers receiving products faster and getting more accurate tracking information.

ML can also help ensure customers are able to receive the products they want when they want them. Machine learning can analyze customer data and predict supply and demand shifts in the future. For example, ML may flag increased demand during the December holidays, signaling to employees to order more stock.

Machine learning is best for specific situations in the supply chain. Here are some use cases.

Machine learning can analyze the layout of a warehouse and suggest improved efficiencies. For example, an ML algorithm may analyze floor traffic data and discover that employees should store certain inventory items closer to the center of the warehouse so workers can move products faster. Speeding up these processes will help improve warehouse efficiency.

In addition, ML algorithms can identify optimal shelf layouts and use shelf sensor data to keep track of activity patterns and stock levels, which can provide insight into restocking needs.

Equipment failure can lead to downtime. Machine learning's predictive capabilities can identify when equipment will need servicing, then automatically schedule maintenance to help prevent breakdowns. Preventative maintenance can reduce downtime and extend equipment life.

ML can also help identify ongoing issues, such as frequent problems with certain equipment or components, and flag potential opportunities for buying new equipment or switching to a different equipment brand.

A strong vendor network is key for product and service quality, but selecting the right suppliers can prove challenging. Machine learning can analyze supplier performance data, such as compliance history and pricing levels over time, and provide insight into whether a potential new supplier will meet company needs.

ML algorithms can also help set and track metrics for suppliers. The data can provide insight into whether current suppliers are succeeding or may be candidates for replacement. Optimizing a company's vendor network can help improve overall supplier performance and supply chain operations.

Machine learning can help companies save money through its supply chain optimization capabilities.

ML's delivery route optimization capabilities can help companies save on gas because delivery vehicles will travel less far. In addition, machine learning's predictive maintenance capabilities can help save on equipment costs because equipment will get fixed before problems occur.

Jacob Roundy is a freelance writer and editor with more than a decade of experience with specializing in a variety of technology topics, such as data centers, business intelligence, AI/ML, climate change and sustainability. His writing focuses on demystifying tech, tracking trends in the industry, and providing practical guidance to IT leaders and administrators.