How to Optimize the Supply Chain with AI?

Just a few years back, artificial intelligence meant adaptions like Jarvis. Who would have thought that AI would soon become an application of our daily lives?

Artificial intelligence has the potential to streamline several business processes, analyze data for insights, and help in building fruitful business strategies. Hence, globally, it is being used to remediate old processes, invent new methods, and improve productivity.

Fortunately, the supply chain is no different than other business departments. This section of your business can benefit immensely with AI.

Let’s see how.

But, before that, let’s understand what is artificial intelligence.

Artificial intelligence is a method of imparting intelligence to machines, due to which machines perform repetitive and action-based tasks just like humans, sometimes better than humans.

The simplest method to understand artificial intelligence is through Gartner’s AI categories:

  • AI has the power to work in practically any field without human intervention. This process is called automation of activities, which are effectively performed by machines without human guidance.


  • AI can augment tasks by performing daily activities, which are augmented or controlled by humans. These are commonly known as virtual assistants that have the ability to conclude a set of tasks in a certain manner.


Various businesses across the globe spend 55 hours in manual tasks, 39 hours on tracking invoices, and 23 hours on responding to inquiries of the supplier. All this on a weekly basis. AI can streamline and optimize all these tasks, ultimately reducing the time spent on management.

Here’s how AI can optimize supply chain:

Operational Procurement

  • It can detect and send compliance and regulatory actions to the suppliers.
  • It can alert when a new purchase is required.
  • It can offer research for internally solving procurement issues.
  • It can document and analyze invoices, order requests, and payments.

Supply Chain Planning

Machine learning can analyze demand, supply, and inventory. By synchronizing all these factors, you can optimize your supply chain decisions. Often a human analysis of demand can be faulty. However, ML analysis of demand and need for supply according to inventory is based on data, which reduces errors.

With this particular feature, you can reduce cost and resource utilization.

Warehouse Management

Supply chain planning is intertwined to warehouse management. Under-stocking and overstocking are both disastrous steps which can severely impact the organization. Overstocking can waste a lot of your money and under-stocking can lose you, several customers.

ML’s data forecasting abilities can allow you to predict the right warehouse demand. Depending upon the previous years’ trends, market trends, and current status of the warehouse, ML can predict future demands.

Shipping and Logistics

Slow and faulty shipping and logistics can plague the whole supply chain. Not knowing the movement of your goods and losing track of the delivery time can hamper your reputation.

To avoid that, you can use AI-based tracking of your logistics. It will tell you the exact position of your goods, which would help you in accelerating the shipment. You can reduce operational costs, resource utilization, and decrease time to destination.

Data cleaning

By exploring the power of data, AI can unravel several aspects of the supply chain. Having full knowledge of your end-to-end process can help you make better business decisions.

Supplier Relationship Management

AI and ML can together be used to conduct a risk analysis for supplier management. Using the available data and sources, you can know the risk involved in collaborating with the supplier. This would help you select a supplier who can prove most useful to the organization in various circumstances.


Artificial intelligence can improve a lot of activities related to supply chain management. Using AI and its related technologies such as ML and NLP would only help you streamline your day-to-day tasks, reduce operational time, and decrease cost overheads to a great extent.