How Machine Learning Optimizes the Supply Chain

The supply chain as we know it continues to evolve, and that is due in part to learning from the effects of the industry impacts over the last few years. As a result, more advanced supply chain technologies are focusing on the use of artificial intelligence — primarily machine learning (ML) — in various areas of their operations, and it is proving to be one of the most profound technologies enabling significant improvements in supply chains.  

ML allows technology to teach itself over time, so that it can improve predictions, recommendations and decisions, but how does it relate to the supply chain exactly? In theory, it is nothing new, but in terms of the supply chain, it continues to prove beneficial in the advancement of digitization through data cleaning and supply chain planning, procurement and execution.

Why does machine learning matter to the supply chain?

ML can deliver several benefits for supply chain management, one regarding an issue plaguing many companies today — too much or too little inventory. The pandemic limited inventory due to backlogs, and now, many are finding they have too much inventory. With ML, organizations do not need to hold as much inventory because it optimizes the flow of products from one place to another. As a result, costs are reduced due to quality improvement and waste reduction, and products arrive in the marketplace “just in time” for sale as a result of upstream optimization. 

Dealing with suppliers is one of the most challenging parts of supply chain management (SCM). With ML, supplier relationship management becomes easier due to simpler, proven administrative practices. ML can be implemented to analyze the types of contracts, documentation and other areas that lead to the best outcomes from suppliers and use those as a basis for future agreements and administration. Stakeholders get more insight into meaningful information, allowing for continual improvement and easier problem solving.

Quality is vital to good SCM as waste and faulty products create unnecessary rework and increase costs. ML can monitor how quality varies over time and suggest improvements. This doesn’t just apply to materials and products. It can track other areas such as shipping, supplier and third-party quality. 

What are the challenges of using machine learning in the supply chain?

ML isn’t perfect, of course. It depends on reliable, high-quality and timely information, and lack of access to good data can cause significant issues. Supply chain managers need to have a robust approach to collecting and analyzing their data.

All organizations in the supply chain should provide information in a consistent way, and, where possible, SCM software should integrate with supplier and manufacturer systems to automatically collect and process data. There will need to be some human interaction with ML, especially with the quality of the data being collected. Supply chain information should be checked and audited periodically to ensure quality.

Machine learning models should be tested and checked to make sure outputs and suggestions are aligned with business needs and expectations.

What are the use cases for machine learning in retail and manufacturing supply chains?

For retailers, stock level analysis through ML can identify when products are declining in popularity and are reaching the end of their life in the retail marketplace. Price analysis can be compared to costs in the supply chain and retail profit margins to establish the best combination of pricing and customer demand. Also, upstream delays can be identified, allowing for contingency planning or alternative sourcing, and retailers can lower storage costs due to not having to hold as much stock.

Food manufacturers can use ML to conduct an analysis of commodity prices and weather patterns to optimize harvesting. Manufacturers can also increase speed to market by optimizing contracts and reducing turnaround times with upstream organizations.


The industry continues to focus on supply chain technology’s role moving forward, and leaders will only benefit by riding the wave that ML is creating.

Glenn Jones is SVP of Product Marketing at Blume Global. He has a proven track record of growing businesses by building and leading R&D and product marketing organizations to define, develop, position and sell highly innovative and high value enterprise solutions delivered in the cloud. He was formerly the COO of Sweetbridge, the CTO of Steelwedge Software and also held leadership positions at other supply chain software companies including Elementum, E2Open and i2 Technologies.