The Potential of Artificial Intelligence to Manage the Supply Chain

Artificial intelligence has made major changes to technology around the world. However, perhaps the most significant potential of AI is its role in the supply chain industry.

FREMONT, CA: Artificial intelligence has changed the supply chain process from passive to active, which will produce greater changes in the way data-driven processes operate in the future. The real role of artificial intelligence in the supply chain is to enhance and enhance human intelligence and decision-making capabilities. Experts at Supplyframe believe that this is a far cry from human intelligence that some people consider obsolete.

Artificial intelligence plays a dual role in the supply chain. The first is to automate repetitive tasks and processes in the entire supply chain function. The second is to realize new forms of strategic decision-making and collaboration.

As technologies such as AI and ML (machine learning) become more common in the supply chain, supply chain management software provider Kinaxis believes these tools can help, but only if the company must determine the source of the business problem. Otherwise, investment in artificial intelligence will not be rewarded.

This epidemic has forced companies in almost every industry to rethink their supply chains. This push has shifted industry from relying on other countries to the new goal of improving its ability to produce materials. Therefore, the value of shrinking and localizing supply chain processes through the use of AI is more obvious than ever. This makes AI a vital tool.

Artificial intelligence has great potential to influence global supply chains. It can take over time-consuming and error-prone manual work. This allows AI to predict demand more effectively, shorten delivery times, reduce costs, and take over customer support roles.

Artificial intelligence seems to be a hybrid solution to solve supply chain problems. Artificial intelligence is sometimes used to predict logistics patterns and even customer behavior-but it is rarely used to bring real value in achieving better returns, faster product iterations, and a sense of security.

For example, from the perspective of planning space, machine learning can greatly improve the prediction of consumer demand. But forecasting is not an end in itself. Then, intelligent automation can execute the best production or replenishment strategy.

The same technology can be applied to supply management in transportation, warehouses and stores. For example, in transportation planning, artificial intelligence can understand the uncertainty associated with the movement of goods from changes in delivery time to perishable products.

The technology also helps the smallest supplier behind the scenes. The process of sending invoices and receiving payments makes full use of AI to automatically extract data from invoices, verify and match approved orders, and solve problems. As a result, the manual work of suppliers for accounts payable and timely payment is greatly reduced.

There is a tendency for consumers and companies to blame other people's bad plans for product shortages. The reasons for supply chain disruption are deeply ingrained, and the pandemic will only make the situation worse.

The reason why the current supply chain operation is difficult to meet the needs of suppliers and consumers is very simple.

Take the frequently mentioned paper products as an example. Consumers have increased their demand for these products, just as they have changed where they are needed.

AI can break these silos to achieve end-to-end visibility across the entire supply chain. This allows the company to better meet the expectations of suppliers and consumers. Their supply chain can operate more efficiently and is flexible enough to meet the expectations of consumers and suppliers, even in the face of daily changes or unforeseen fluctuations.

The key that AI may solve is that the supply chain itself is optimizing for the same goal, not just every member of the chain. The point is not to blame the supplier or the nodes in the production line, but to make the AI ​​agent work on corrective actions to correct the error.

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