Machine Learning in Supply Chain Management — What Possibilities Exist?

Machine learning is a subset of artificial intelligence (AI). It features algorithms that get more accurate without constant training from humans. Exposure to data helps them improve. Applying machine learning in supply chain management is one of this technology’s most disruptive use cases.

Operating a streamlined supply chain is one responsibility that machine learning could improve, especially due to the unpredictable nature of marketplace conditions, product availability and consumer demands. How could companies depend on machine learning in supply chain management?  Let’s explore what’s possible. 

Better Visibility With Machine Learning and Connected Sensors

Many supply chain managers decide to use Internet of Things (IoT) sensors to continuously gather data about things like total output, available inventory and employees’ productivity. If they also depend on machine learning algorithms to analyze the compiled information, those professionals could get insights much faster than they could without such technological assistance.

For example, well-trained algorithms can consider tens of millions of options in fractions of a second. Humans still make the final decisions about which actions to take. 

The significance of using machine learning in supply chain management is that the technology shows them aspects and trends they may have otherwise missed. Such information allows for a more proactive approach to operations. 

Improved Procurement Efforts From Enhanced Decision-Making

Professionals also rely on machine learning in supply chain management when they want to make smarter procurement-related choices. For example, predictive analytics could help companies anticipate delays and accommodate for them before those slowdowns cause major issues. 

Machine learning could also show which suppliers historically provide the most reliable service. It then becomes easier to assess those sources to decide which ones are appropriate to fulfill the needs associated with an upcoming project. 

Intelligent algorithms can even send out bid requests to groups of suppliers that meet a defined set of criteria. That approach reduces the manual work supply chain managers must do to get the goods they need to keep operations running smoothly. 

Stronger Competitive Advantages Thanks to Optimization

Supply chain brands must continually evolve to show that they are aware of customer demands and able to meet them. Applying machine learning in supply chain management can help companies achieve competitive advantages by optimizing their processes and seeing where room for improvement exists. 

Companies often keep going through business processes with the same techniques that worked well years earlier. They may not change their ways unless something disastrous happens. 

The beauty of machine learning is that it can detect how to make things better before a catastrophe strikes. For example, it might recommend a relatively simple change that saves time and money. 

No business can see what’s ahead with complete accuracy. Machine learning technology can make it so that a company stays competitive by avoiding surprises, though.

Faster and More Accurate Inspections

Careful inspections are crucial parts of successful supply chains. Those examinations check that finished products are free from defects or that parts arriving from elsewhere are genuine and meet stringent quality standards. 

Estimates suggest that as many as 6% of imports in the United States and the European Union are counterfeit. Supply chains face various risks from fake products, ranging from product delays to product failures and consumer harm. 

Researchers developed a machine learning data set containing three million images of common kinds of counterfeit and genuine goods. This method is more than 98% accurate in differentiating between the two groups. 

Machine learning in supply chain management also applies to quality control at factories. For example, machine vision cameras can evaluate products on assembly lines faster and more precisely than humans. 

Happier Customers Due to More Efficient Deliveries

The delivery time frame is often a tremendous concern for potential customers. If they can’t get desired items fast enough from one source, those parties will likely shop around for the companies that can give them the fastest service.  It’s also crucial that businesses meet the requirements they set about shipping speeds.

Depending on machine learning in supply chain management could help with those factors, meaning that customers feel satisfied and ready to do business with an enterprise again. For example, algorithms can assess numerous aspects that could impact delivery speed. Those include traffic and weather conditions. 

In one real-world example, an algorithm detects whether an address is likely a residence or a business. It then recommends the timing for the associated delivery so that the driver has a higher chance of the recipient being available to accept the package. 

Machine Learning in Supply Chain Management Takes Businesses to Greater Heights

This overview of machine learning in supply chain management shows that professionals have plenty of opportunities to make the technology work for them. However, there is no guaranteed path to success.

Company representatives should take the time to identify the most significant challenges or obstacles faced, then explore how machine learning might help deal with them. It’s also smart to track metrics so that business leaders have a clear understanding of what benefits the innovation caused, and what they still need to work on to maximize the return on investment.