How Generative AI Will Improve the Supply Chain
Savvy business leaders are already beginning to identify use cases for generative AI in their organizations. From customer service to marketing and HR, 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 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 this development and add additional functionality? Read on to discover how technology can further improve the supply chain.
What is generative AI capable of?
Before we look at some specific supply chain use cases, let’s quickly recap what the technology popularized by ChatGPT can actually do.
· Generative AI can automatically create content in a variety of formats, including text, images and video.
· Generative AI can also interpret queries in a variety of formats, most commonly as conversational text queries. This means you don’t need to be a data scientist or programmer to harness generative AI: you just need to ask for what you want. Some generative AI tools can also respond to visual cues (such as images) and voice queries.
· Generative AI can analyze large amounts of data (potentially in real time), including text data, digital data, and image data.
· Generative AI can also summarize data and produce easy-to-action reports and recommendations based on the data.
With these capabilities in mind, it’s easy to imagine how generative AI could help supply chains. But let’s explore what this looks like in practice.
Forecast demand and manage risks
Generative AI’s ability to analyze large amounts of historical and real-time data – and provide conversational responses – can make planning much easier. Because instead of navigating a complex analytical tool, you can simply ask conversational questions that help you forecast demand. Questions like “What are the key market trends that may impact our demand forecast?” or “How can we plan for alternative suppliers in the event of a major global disruption?” or “What are our biggest risks in meeting customer demand?”
In other words, generative AI removes some of the complexity of using technology to forecast demand. And don’t forget that generative AI tools can also recommend actions based on what the data suggests.
Sourcing and supplier management
Generative AI can add value to the supplier selection process by analyzing factors such as 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.
Automation of negotiations with suppliers
One potentially surprising use of generative AI is to use it to negotiate with suppliers – a chatbot, basically, that negotiates costs and other contract terms with suppliers. A large US retailer that automated supplier negotiations found that not only did it reduce negotiation costs and time, but more than 65% of suppliers preferred to negotiate with the bot on a human.
And if handing over negotiations to a robot makes you nervous, you can always use generative AI to analyze contracts, compare contract terms, make recommendations, and even identify contract risks.
Optimize logistics
Organizations have been using AI tools to optimize logistics for several years (such as tools that improve order picking routes in the warehouse or using AI to design the most efficient delivery route for drivers). But generative AI brings a new level of functionality to AI-driven logistics by enabling a conversational interface, meaning users can simply ask the tool for recommendations. In other words, it provides even more opportunities to customize logistics on the fly.
Improve the production process
Of course, generative AI can also improve the production of goods. Two of the most prominent examples include accelerating the design process with AI-enhanced design tools and using predictive maintenance to determine which machines or production lines are most likely to fail (enabling thus rapid maintenance and less machine downtime).
But isn’t this yet another disruption to already strained supply chains?
To say that supply chains have been under enormous strain over the past few years is an understatement. And while this may seem like the worst time to introduce even more change in the form of transformative new technologies, the opposite is true. Because generative AI can help supply chain professionals adapt to rapid changes and scale their operations more easily. In short, it’s a game changer for supply chains.
Supply chains have always been evolving. Generative AI is the latest technology to deliver improvements and innovations – potentially the most transformative technology – but it will not be the last. As has always been the case, organizations that can undertake transformation strategically and thoughtfully are more likely to succeed. Everyone risks being left behind.