A comprehensive exploration of applications of artificial intelligence in manufacturing

Take a thorough look at the realm of artificial intelligence (AI). Particularly focusing on its transformative applications of artificial intelligence in the manufacturing industry. AI's myriad unpacks forms, such as machine learning, natural language processing, computer vision, robotics, expert systems, and multi-agent systems. This aims to shed light on how these technologies work and their potential applications in supply chain management.

From problem-solving algorithms that mimic human-like decision-making to advanced AI models that can understand and synthesise human language. Learn how each type of AI can improve your supply chain, enhancing efficiency, predictability, and scalability.

Is machine learning a form of AI?

Machine learning and AI algorithms can learn from input data, and respond based on that learning. They play a large part in many other, more specific forms of artificial intelligence software. There are three main approaches to machine learning - supervised, unsupervised, and reinforcement learning. 

Supervised learning is where a machine learning algorithm trains with both the input data and the expected result. The algorithm learns what it is about the input that causes the expected response. An example of this in supply chain management is using image recognition to reject irregular products on a manufacturing line.

Unsupervised learning is where an algorithm only receives input data, which it then has to identify patterns in. This technique can identify patterns in very large sets of data from the supply chain. Examples may include, predicting future product demand, and detecting anomalies in production processes.

Finally, reinforcement learning is where an algorithm learns how to respond based on a reward function. The algorithm responds to input data and receives positive or negative feedback based on that response. Reinforcement learning is useful in applications that manage supply chain inventories.

AI and Natural language processing

The aim of natural language processing is for an artificial intelligence application to understand or create human language. Modern artificial intelligence software implementations, such as OpenAI’s ChatGPT and Google Bard, depend on large language models.

These models use neural networks with many parameters trained on a large set of structured text. In the future, they could be ubiquitous across global supply chains. Such applications could automate customer interactions, including answering pre-sales queries, and after-sales support. But natural language processing is not just for talking to people.

The automated mapping of supply chains using only pre-existing written information is also becoming a possibility.

AI and Computer vision

Computer vision applications are algorithms that can analyse images or videos. You can consider these algorithms as AI when they can respond to their inputs in the same way to a human. The inputs may come from one or more cameras or other sensing devices.

This technology detects and classifies objects and events in supply chains. For example, a drone equipped with a camera and enabled with artificial intelligence software could monitor large fields of crops. It could analyse the video input to map the spread of pests, diseases, and nutrient deficiencies.


Robots are often the first thing that people think about when they consider artificial intelligence applications in supply chain management. These are robots that do more than follow a pre-defined set of instructions programmed into a logic controller.

AI can empower them to undertake much more difficult tasks that require rapid and complex decisions. Decisions that a human worker or operator would normally make. These robotics sensors can incorporate artificial intelligence technology. Such as those that allow robots to manipulate irregular, motile, or fragile objects.

Expert systems

Expert systems are one of the earliest forms of artificial intelligence. They use a knowledge base and a set of logical rules to resolve problems in a pre-defined way. Such a system may ask the user a series of clarifying questions to determine the correct response.

Supply chain managers can use an expert system to support decisions related to supplier selection. A human expert designs the system to ask the necessary questions to assess the quality of a supplier.

The expert system can rate a supplier. It does this by using decision logic after asking a manager some questions. This allows the system to decide if the supplier is good or not.

Multi-agent systems

These are groups of many different artificial intelligence applications acting at the same time. They work together to achieve goals that may be complementary or conflicting. Often, they will tackle problems that will be either difficult or impossible for a single program to solve.

An example of this is several chatbot’s working together to negotiate the best price for a product or service. Each chatbot may be negotiating with a different supplier, and have unique background information, and a tailored negotiating strategy.

Artificial intelligence presents plenty of opportunities to transform supply chain management in the manufacturing industry. From machine learning algorithms enhancing predictive capacities to expert systems assisting in supplier selection. From robotics improving efficiency to multi-agent systems optimising negotiations, AI opens new doors to operational excellence. However, the key to harnessing the full potential of these technologies lies in a nuanced understanding of their workings and applications.

For a deeper dive into the applications of AI in supply chain management, we encourage you to read our guide!