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This article is part of an ongoing series on the uses for artificial intelligence (AI) in manufacturing, starting with our article introducing machine learning and AI, and their relevance to manufacturing. This article will cover how AI can be used for tracking and assessing a manufacturer’s many suppliers to keep up, in real time, with any shortages of raw materials needed for a certain product, and compensate as needed. Implementing AI can be a major key in generating and retaining a high level of efficiency in a manufacturing process, including the management of available resources. 

A Buyer’s and Supplier’s Market

Resources, or raw materials, are the fuel for the manufacturing process. Of course, fuel is also needed for many machines involved in the assembly of items, but that is besides the point here, which is that, without raw materials, AKA the fuel, there is no item produced. And let it be said that, unlike for automobiles, there is no electric alternative for the fuel that is raw materials. 

Of course, as all manufacturers know, the procurement of raw materials is always a sensitive matter. Daily cost fluctuations for materials can make a precious and, sometimes, precarious business of ordering materials. Now, AI can help with the prediction and choosing of which day to order materials, which we cover in our article on commodity price prediction

However, the price of the commodity hardly matters if there is a shortage, which can put a big dent in your manufacturing plans for the day, or, depending on the severity of or reason for the shortage, weeks or months or, though hopefully not, beyond. 

Unfortunately, disasters do occur that can put an indefinite stop to a suppliers’ operations, but often times it is the case that there is a less critical and dangerous reason for a shortage, and these things can be predicted. 

If you have read any of our previous articles about AI-powered prediction in manufacturing, then you will know that artificial intelligence, the field dedicated to creating autonomously thinking and independently working computers, is, at the core, a field about prediction. Read on to see how predictive AI systems, like those offered by Findability Sciences, can make sure you are prepared for any shortage of manufacturing’s fuel, which can sometimes literally be fuel. 

AI Predicts Shortages and Finds Replacement Suppliers

Artificial intelligence is adept at finding patterns in data, and much of the data related to predicting shortages has to do with historical records of shortages, both generally across a market and specifically related to any given supplier. 

Patterns found can include changes in the weather’s impact on supplies, to seasonal changes, and the like. 

By analyzing the surrounding circumstances in instances of shortages, and what is common across those many instances’ circumstances, can lead to well-informed and accurate predictions of when you can expect a shortage from your supplier. 

Sometimes, it can be supplier-specific, like when a supplier is based in a certain region that will get weather affecting the cultivation of important materials. Other times, it can be supplier-general. In the case of supplier-specific, AI can also find a replacement supplier that is unlikely to have a shortage.

The important thing to realize about this predictive work is that it is, well, predictive. This means that you will not have to make any crucial, production-affecting decisions when the shortage actually happens—you will be prepared, and have an alternative supplier identified and chosen already. 

All of this is done to prevent any unwanted shutdowns of operations, which invariably disappoints your buyers, who need your items to turn a profit. Having all but one or a few necessary materials for production can be frustrating, and preventing this is the aim of supplier-assessing AI systems. 

If you want to implement AI into your manufacturing process for the sake of staying on top of changes in the supplying of raw materials, then do not hesitate to reach out to Findability Sciences

Previous Articles in Our Machine Learning for Manufacturers Series: