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This article is part of an ongoing series called Machine Learning for Manufacturers, which focuses on the uses for artificial intelligence (AI) in manufacturing. It began with our article introducing machine learning and AI, and continues to spell out the many ways that AI solutions are relevant to manufacturing and supply chain operations. 

What is a Supplier Relationship Assessment, and Why is AI Relevant to It? 

We have mentioned in a recent article in this series that AI can play a big role in helping you choose a supplier, but AI’s use does not have to stop there. 

In fact, you can have an AI agent at your side while you can dive into supplier relationship management, or SRM. 

SRM, ideally, will be a forum for you and your supplier, or (more likely) suppliers, to strategize and figure out a more efficient way of working to further improve your partnership, not to mention profits. 

But, if things are not working out between you and your supplier, then you can also figure out or communicate why you are not satisfied and what should be done to repair the partnership. 

Though it could be an attractive option to simply drop an underperforming supplier and move on, that can often be more costly than simply sitting down and figuring out how to make things work. 

Trouble in Paradise? 

In manufacturing, not a week goes by where everything goes exactly as planned. 

There is always an unforeseen issue that, with swift action and a little bit of luck, can be ironed out to meet an end-goal. 

However, when there are consistently recurring issues, this should be a call to attention, especially if those issues are related to a materials supplier. 

In a supplier relationship review, the manufacturer and supplier figure out what is working, and what is not, but it was not until recently that artificial intelligence had a seat at the table. 

AI can do a variety of SRM-related tasks such as conducting audits, analyze assessments, and credit scoring.

To find out how AI does these things, read on. 

Automating SRM Tasks to Assist Your Strategy-Making

Let us first focus on the task of automated auditing as an example of how AI improves SRM. 

Any accountant can tell you that money-making brings forth a lot of paperwork, or documentation. 

Our previous article on NLP and industry regulations covered AI’s ability to scan through long texts to find essential info, and the same thing can be done here with the many documents involved in an audit. 

AI can make predictions, but it can also find patterns within a data set (documents can be a data set, by the way.) Inconsistencies and errors in a supplier’s financial records can be found quite quickly by an AI agent. 

That same AI agent can make predictions, based on your supplier’s historical records, about whether you can expect to find these sorts of inconsistencies and errors in the future. 

It is easy to misstate materials on transaction statements, and honest errors are honest errors. However, a look through a history of transactions can let you know whether some errors are something deeper. 

Hopefully, you do not find anything too suspicious in your AI-assisted audit of a supplier, but regardless, automating such tasks saves everyone a lot of time and energy. 

Summary and Conclusion

Supplier relationship management consists of things like assessments and audits, which can be automated by an AI agent that can find meaningful patterns in data sets related to your partnership with a supplier. 

AI-Powered Data Solutions for Your Company

To learn more about AI-based solutions, manufacturing-related or not, reach out to Findability Sciences, a leading AI service provider. From predicting customer churn to installing chatbots in your supply chain, you are sure to find what you need at Findability Sciences. 

Read other informative articles in our ongoing Machine Learning for Manufacturers series: