How Generative AI will power the supply chain

Savvy business leaders are already beginning to identify genetic AI use cases in their organizations. From customer service to marketing and HR, generative AI’s ability to analyze data and create content in various formats can add significant value to internal business processes. But what about the supply chain?

Artificial intelligence, in general, has been making waves in supply chain operations for some time now – particularly when it comes to demand planning and delivery optimization. So how can genetic AI build on this development and add additional functionality? Read on to learn how technology can further improve the supply chain.

What is Generative AI capable of?

Before looking at some specific supply chain use cases, let’s quickly recap what the technology popularized by ChatGPT can actually do.

· Generative AI can automatically generate content in a variety of formats, including text, images and video.

· Generative AI can also interpret requests in various formats – usually as chat text requests. This means you don’t need to be a data scientist or programmer to harness genetic AI – you just ask for what you want. Some AI building tools can also respond to visual cues (such as images) and verbal requests.

· Generative AI can analyze massive amounts of data (potentially in real-time), including text data, numerical data, and image data.

· Generative AI can also summarize data and produce easy-to-use reports and recommendations based on the data.

With these possibilities in mind, it’s easy to imagine how genetic AI could help supply chains. But let’s explore what it looks like in practice.

Demand forecasting and risk management

Generative AI’s ability to analyze vast amounts of historical and real-time data – and provide conversational responses – can make planning much easier. Because instead of navigating a complex analytics tool, you can simply ask conversational questions that help you predict demand. Questions like, “What are the biggest market trends that might affect the demand forecast?” or “How can we plan for alternate suppliers in the event of a major global outage?” or “What are our biggest risks in meeting customer demand?”

In other words, genetic AI takes some of the complexity out of using technology to predict demand. And don’t forget that AI tools can also suggest actions based on what the data suggests.

Sourcing and supplier management

Genetic AI can add value to the supplier selection process by analyzing factors such as supplier capabilities, pricing, potential risk and other factors. Additionally, by analyzing supplier data and communications, genetic AI can identify insights from supplier interactions and suggest new ways to improve relationships.

Automate negotiations with suppliers

A potentially surprising use of genetic AI is to use it for supplier negotiations – a chatbot, basically, that negotiates costs and other contract terms with suppliers. A major US retailer that automates supplier negotiations found that not only did it cut costs and reduce negotiation time, but more than 65 percent of salespeople actually prefer to negotiate with the bot over a man.

And if handing over negotiations to a robot makes you nervous, you can use genetic AI to analyze contracts, compare contract terms, provide recommendations and even identify contractual risks.

Logistics optimization

Organizations have been using AI tools to optimize logistics for a few years now (such as tools that improve picking routes in the warehouse or use AI to plan the most efficient delivery route for drivers). However, genetic AI brings a new level of functionality to AI-based logistics by enabling a conversational interface – meaning users can simply ask the tool for suggestions. In other words, this brings even more opportunities to customize logistics on the fly.

Strengthening the production process

Of course, genetic AI can also improve the production of goods. Two of the most impressive examples include speeding up the design process with AI-enhanced design tools and using predictive maintenance to identify which machines or production lines are most likely to fail (thereby enabling early maintenance and less downtime machine).

But isn’t this more disruption to already strained supply chains?

To say that supply chains have come under enormous stress in recent years is, well, a bit of an understatement. And while it may seem like the worst time to introduce even more change in the form of transformative new technology, the opposite is true. Because genetic AI can help supply chain professionals move forward with rapid changes and adjust operations more easily. It is, in short, a game changer for supply chains.

Supply chains have always been in a state of evolution. Generative AI is the latest technology to deliver improvements and innovations – potentially the most transformative – but it won’t be the last. As has always been the case, those organizations that can embrace transformation in a strategic and thoughtful way are more likely to succeed. Everyone else risks being left behind.