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January 04, 2022

Scenario-based supply chain optimization with AI


Edwin Lisowski

CSO & Co-Founder

Reading time:

5 minutes

A supply chain is a system made up of different entities involved in supplying an organization’s product or service to a consumer. Supply chain management serves the role of managing the flow of products and services, funds, and information from the beginning stage of sourcing raw materials to when it reaches the end-user. With the development of new technologies, there’s constant change in the way companies transfer goods and information, both internally and amongst partners across the supply chain optimization system.

Artificial Intelligence (AI) is a key emerging technology that’s reshaping the supply chain in different industries. Moreover, according to Gartner surveys[1], 64% of supply chain leaders have employed AI solutions in one way or another. 31% of them use AI to automate decision-making. In this article, we are going to examine how AI supports supply chain optimization. Read on!

As you already know from our other blog posts, AI refers to advanced solutions that can learn, understand, reason, and interact just like humans but without their supervision. These solutions employ technologies like natural language processing (NLP) and machine learning (ML) to understand and evaluate data at incredible scale and speed. They can correlate the data and dig out important insights, thus supplementing the brainpower of the operator[2].

But how can this technology be used to facilitate your supply chain optimization operations? Let’s have a look.

Artificial intelligence applications in supply chain management

In this article, we list four crucial applications of AI in supply chains. The vast majority of them are based on scenarios. In other words, the whole process is specified and organized from the starting point up to the expected outcome. That’s why we talk about scenario-based optimization because we try to optimize each step to make it as effective as possible.

What can be found on our list?

It might be interesting for you: AI, big data, and machine learning in transportation

Inventory management

The use of big data is integral in the inventory management of different industries. For example, the majority of original equipment manufacturing (OEM) companies rely on the transfer of data internally and externally with other companies across the supply chain to create products according to a prearranged schedule. Let’s see how it works in the automotive industry.

supply chain statistics

Here, companies use the so-called just-in-time (JIT) manufacturing process, which requires the manufacturing facility to keep a minimal supply of cars on hand and rely on routine deliveries by suppliers. The OEMs notify their suppliers about the number of each vehicle design they will be creating and in what order. Therefore, the suppliers can deliver the required number of components in the suggested order to satisfy the production schedule.

This entire process is data-driven, which means artificial intelligence can provide considerable help to OEMs to manage inventories and streamline the manufacturing process.

Language barriers

Due to the upsurge in international sourcing, there’s a high likelihood of a language barrier involving companies from different locations around the globe. For example, there’s a need for a translator every time there’s a meeting between Chinese and American manufacturing facilities and many Mexican or African suppliers.

supply chain optimization

Artificial intelligence comes in to get rid of these language impediments. You see, it is fairly easy to train AI to translate between 2 languages, e.g., during a meeting. You simply need to feed vast amounts of material into the neutral network in any of the languages you wish to translate between[3].

So, once the data is uploaded, AI comes in to help with the translation during business meetings.

Customer service

A significant section of the supply chain optimization process involves working with consumers to guarantee timely deliveries, quick responses to raised queries, and great supplier-customer relationships. Thanks to AI, many companies are adopting the use of smart assistants like chatbots and voicebots to help with customer service.

customer service

This translates to huge cost savings through the reduction of the customer service budget. ALso, chatbots can help build deep interactions with the customer, which translates to improved UX and customer service.

Besides chatbots, AI gives customer service representatives access to real-time status updates of all product orders. This information can be quickly shared with the customer or vendor if requested.

Therefore, the staff can then concentrate on other important matters.

Forecasting demand

AI can provide accurate forecasting of demand trends through the analysis of historical and external data. It uses data mining and predictive analysis to analyze the data and predict future sales.

It may be difficult to predict sales in sectors such as the automotive industry, particularly when it comes to newer technologies like electric cars. However, accurate demand forecasting through AI could be the difference between a profitable and loss-making quarter[4].

Thanks to AI, automotive manufacturers can enjoy more precise predictive analysis and minimize losses incurred from erroneous forecasting.

Final thoughts on AI in supply chains

Artificial intelligence is revolutionizing the supply chain in different industries. The technology helps companies minimize language barriers, improve forecast demand, customer service, and streamline supply-chain-related processes. The sooner your company starts adopting AI tools in your supply chain optimization operations, the quicker you will boost your revenue and efficiency.

If you’d like to find out how Addepto can help you with optimizing supply chains using AI-based solutions, take a look at our AI consulting services and drop us a line for details.


Artificial Intelligence