Can AI run a supply chain?

Can AI run supply chains?

Supply chains may be complicated systems, but modern machine learning techs and artificial intelligence have allowed us to consider the possibility of AI running supply chains. Today we will explore what benefits, monetary as well as non-monetary, businesses can obtain by automating their supply chains.

What is artificial intelligence

The definition of artificial intelligence(AI) is one that has evolved with the passage of time. As far as its definition in academic circles is concerned, opinions differ. The argument as to when a particular piece of code can be deemed intelligent is debatable. Passing the Turing test is considered a benchmark for many in academia. However, as far as its definition among the general public is concerned, any automated process or decision is considered by many as an example of artificial intelligence. The general public usually does not consider the intelligence of the process or whether the algorithm is able to adopt machine learning or not. For the purpose of this article, we will consider this lay man’s definition when talking about AI.

What is supply chain?

Supply chains have become complicated over time and there is much more to them now than just shipment of goods. Some aspects of a supply chain include:

  • Regulatory compliance
  • Supply planning
  • Shipment
  • Customer service
  • Vendor management
  • Warehouse management
  • Demand projection

A.I in S&OP

Multinational companies increasingly use S&OP (Sales & Operations Planning) to stay abreast of supply and demand and manage their inventory in an optimal manner. Juggling demand and supply is tricky and often requires weekly recalibration to cater for short term changes. Some of the decisions that are agreed upon during the planning stage can easily be optimized thanks to machine learning. This can be achieved with the help of historical data and statistical inputs. The algorithm can be trained to become intelligent enough to respond to supply and demand changes timely.

A.I in supply route determination

How much a shipment will cost to the shipper is a variable that keeps changing at all times. Some of the factors that influence it include forex rates, route fares, and oil prices. On top of these, unexpected events like natural disasters, closure of ports, and transport availability can influence the price of the shipment. The shipper has to keep track of all these variables to come up with an optimal way of shipment. It can be daunting for individuals to keep track of these variables and then determine the best shipping route day in day out. However, if all these variables are provided to an algorithm that can then calculate the best possible shipping method, it will save the company a lot of time and resources.

A.I in HS Classification

Companies use trade specialists to classify their products with the correct HS code. These specialists need to be familiar with the World Customs Organization regulations in order to carry out their job. One would think that automatically classifying products across hundreds of different categories is a hard task to automate, and rightly so. However, many companies only deal with certain types of goods, and automating these HS classifications is possible. As long as the person trying to classify the product is familiar with it and can provide relevant inputs, an algorithm can determine which HS code to assign to that product.

A.I in warehouse management

Warehouse management is one area where AI can help even small businesses optimize their workflow. Intelligent algorithms can determine which items need to be placed on the lower shelves because they need to ship more often. Bulkier items that aren’t moved often enough can be placed out of the way of other items so that they cause the least disturbance to warehouse workers. As long as the warehouse management system has the right machine learning capabilities, it can be used as a great AI system to better manage warehouse items.

A.I in financial compliance

Cost efficiency can easily be enhanced between the seller and buyer with the help of AI. The AI can make decisions that will allow it to chalk out the most favorable trade terms for both parties. Considerations in this step can include transportation costs or clearance times as per the arrangements between the two parties.

By when can we see mainstream AI use in supply chain?

There are many hurdles as far as mainstream use of AI in supply chains in concerned. What people need to understand here is that AI is driven mainly by data. It needs to be fed a lot of historical data for it to be able to make decisions on its own. This is difficult to achieve for a variety of reasons. First of all, gathering such data in a time-consuming process and requires both financial and human resources. In the absence of such data, it is hard for companies to trust the AI results. The simulations carried out in tests need to be way better than what will happen in reality and with lack of data, those simulations cannot be trusted, especially for larger organizations that cannot afford to waste valuable resources on such a massive shift.

Even when companies go ahead with collecting such data, they have to pay for it way before the cost of the AI system comes in. This means they have to make an investment so that they are able to consider whether AI is viable or not. Once the data is available and a system in place, it will have to be 100% accurate. Any failure in the system at any point may result in finger-pointing and failure verdicts from employees who will be quick to point out that the new system doesn’t work.

It is clear that we won’t have AI running supply chains any time soon. One of the biggest examples of this can be seen in warehouses where AI can clearly provide huge benefits. Even then, warehouses being run by AI are an exception in today’s world. With further development in technology and hence possible reduction in implementation costs, businesses may slowly more towards supply chains that are fully managed by AI. However, collecting the data that helps build AI systems will itself take a lot of time, giving us a good idea that AI systems aren’t coming anytime soon.

Leave a Reply