A comprehensive exploration of applications of artificial intelligence in manufacturing

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.

Email:mailto:plcsale@mooreplc.com

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