Artificial intelligence in supply chains

Getting started on your AI journey is mainly about identifying the first (or next) use case. In most categories of AI today, there are a multitude of supply chain use cases to be inspired by. With a simple five-step approach, you can be well on your way in as little as five weeks.

Broadly speaking, when we talk about AI, there are eight different categories – one of which is generative AI. All of these categories have use cases within supply chain management that can lead to increased efficiency and reduced costs. 

With use cases available within all eight AI categories, it has never been easier to start implementing AI on your journey to a fully automated supply chain.

Computers are performing tasks normally performed by humans

To put it simply, artificial intelligence (AI) is a term for a computer system that is designed to perform tasks which typically require human intelligence, such as learning, problem-solving and decision-making. An AI may apply machine learning, a method that uses data to learn how to make decisions based on new observations.

According to this – admittedly very broad – definition, AI is not only large language models (such as ChatGPT or Gemini) but also algorithms specifically designed to optimise routes in a logistics network, schedule work shifts or predict machine breakdowns. These are all tasks that can be effectively solved using current technologies and variants of AI.

Eight categories of AI 

When considering the application of AI, it can be useful to think beyond technical terms such as reactive AI, narrow AI, generative AI and machine learning and instead consider different archetypal categories of AI. 

Therefore, consider the eight archetypal categories of AI below that all mimic human processing power in different ways. Each category is followed by a few examples of use cases, all of which are from a supply chain point of view (i.e. more use cases can be found in a broader perspective):

A generative AI model, such as ChatGPT and Gemini, is in itself impressive. However, when combined with other models, generative AI models become even more powerful when given access to business data, the ability to execute calculation models or the ability to execute code. These AI agents are currently undergoing rapid development, and here, supply chain use cases are also starting to emerge.

Getting started – or accelerating your journey

Getting started on your AI journey is now more about identifying the first (or next) use case. Depending on your scope, identifying, assessing and prioritising can be done in as little as five weeks. The approach can be based on a simple five-step process that has been tested time and time again and proved to result in high-impact use cases.

Concluding remarks

The supply chain landscape is becoming increasingly complex and dynamic. Here, AI can provide a powerful competitive advantage for those who harness it, with tangible and measurable benefits. 

With a plethora of use cases available, the decision to start implementing AI in your supply chain is more a question of will rather than sense.