How generative AI powers supply chains

Smart business leaders are already starting to identify use cases for generative AI within their organizations. From customer service to marketing to human resources, generative AI’s ability to analyze data and generate content in a variety of formats can add significant value to internal business processes. But what about the supply chain?

AI in general has been making big waves in supply chain functions for some time now, particularly when it comes to demand planning and delivery optimization. So how can generative AI build on that evolution and add additional capabilities? Keep reading to learn how this technology can further improve supply chains.

What can generative AI do?

Before looking at some specific supply chain use cases, let’s quickly summarize what can actually be done with the technology popularized by ChatGPT.

· Generative AI can automatically create content in various formats such as text, images, and videos.

· Generative AI can also interpret requests in different formats. The most common type is a conversational text request. This means you don’t need to be a data scientist or programmer to take advantage of generative AI; you just need to ask for what you need. Some generative AI tools can also respond to visual cues (such as images) or audio requests.

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

· Generative AI can also summarize data and generate actionable reports and recommendations based on the data.

With these capabilities in mind, it’s easy to imagine how generative AI can aid supply chains. But let’s see how it goes in practice.

Forecast demand and manage risk

Generative AI’s ability to analyze vast amounts of historical and real-time data and provide answers conversationally makes planning much easier. Instead of navigating through complex analytical tools, you can simply ask conversational questions to help predict demand. Questions such as “What are the biggest market trends that could impact our demand forecast?” “How do I plan for alternative suppliers in the event of a major global disruption?” or “What are the biggest risks to meeting customer demand?”

In other words, generative AI removes some of the complexity of using technology for demand forecasting. Remember, generative AI tools can also recommend actions based on what the data suggests.

Supplier sourcing and management

Generative AI can add value to the supplier selection process by analyzing supplier capabilities, pricing, potential risks, and other factors. Additionally, by analyzing supplier data and communications, generative AI can identify insights from supplier interactions and suggest new ways to improve relationships.

Automate vendor negotiations

One amazing use of generative AI is to use it in negotiations with vendors. This is essentially a chatbot that negotiates costs and other contract terms with vendors. One large US retailer who is automating vendor negotiations found that not only were costs reduced and negotiation time shortened, but more than 65% of his vendors actually preferred negotiating with bots over humans. I discovered that there is.

And if you’re not comfortable handing over negotiations to a bot, you can also use generative AI to analyze contracts, compare contract terms, provide recommendations, and identify contract risks.

Logistics optimization

Organizations have been using AI tools in recent years to optimize logistics, such as tools to improve picking routes in warehouses or using AI to design the most efficient delivery routes for drivers. tools, etc.). However, generative AI brings a new level of functionality to AI-driven logistics by enabling conversational interfaces. This means users can simply ask the tool for recommendations. In other words, this provides even more opportunities to customize logistics on the fly.

Enhancement of production process

Of course, generative AI can also improve the production of goods. Two of the most impactful examples include using AI-enhanced design tools to accelerate the design process and using predictive maintenance to determine which machines or production lines are most likely to fail. (which allows for timely maintenance and reduced machine downtime).

But wouldn’t this be yet another disruption to an already stretched supply chain?

To say that supply chains have been under tremendous stress in recent years is a bit of an understatement. And while it may seem like the worst time to introduce further change in the form of innovative new technology, the opposite is actually true. Because generative AI helps supply chain professionals respond to rapid change and adapt their operations more easily. In short, this is a game-changer for supply chains.

Supply chains are in a constant state of evolution. Generative AI is the latest technology to offer improvements and innovations, and while it may be the most transformative, it won’t be the last. As always, organizations that can embrace change in a strategic and thoughtful way are most likely to succeed. Everyone else risks being left behind.