Generative AI Use Cases in Supply Chain

Use Cases & Projects, Dataiku Product Lynn Heidmann

From demand forecasting to inventory management, delivery dock optimization to warehouse management, classic machine learning has already transformed the supply chain. In the last five years, organizations have leveraged the technology to make more data-driven decisions. This includes streamlining processes as well as unlocking new opportunities for growth. So what does Generative AI bring to the table on top of these existing capabilities?

→ Read Now: Beyond the Chatbot — The Path to Enterprise-Grade Generative AI

The Two Tiers of Applications

Before we get into Generative AI in supply chain specifically, let's take a step back. Imagine the first generations of artificial intelligence (AI) were like the steam power of the first industrial revolution. That makes these new models the electricity of the second industrial revolution. Generative AI models will be transformative in ways that we do not yet anticipate.

Think about potential applications for Generative AI technologies in supply chain in terms of how they will be transformative:

  • Moonshots, or use cases that introduce fundamentally new capabilities.
  • Mundane, or use cases that augment hundreds or thousands of processes and decisions throughout the business.

You Need Mundane and Moonshot Use Cases to Succeed

The most commonly imagined moonshot application of Generative AI in the enterprise is the all-knowing, oracular chatbot. This always-on, always-accurate assistant can provide immediate answers or predictions about the current and future state of the business.

Could this work? Maybe, but there are many caveats and many reasons to doubt that such a system would ever be fully trusted. Receiving less attention is the potential for Generative AI to transform the business through the augmentation of countless mundane tasks. In this scenario, the business chooses different models for different applications, balancing considerations like performance, cost, and privacy.

Teams have the power to apply approved technologies to the challenges that they face. Democratized Generative AI leads to a massive increase in productivity.

Here's the thing — you need to be able to execute on both moonshot and mundane Generative AI use cases. Yes, you should start building the rocket for that moonshot. But even more immediately, you must start augmenting processes throughout your organization, finding the right balance between autonomy and control. 

LLM-Enhanced Demand Forecasting

Let’s walk through one example of a Generative AI use case in supply chain management that would have immediate value in augmenting your workforce. That is Generative AI-enhanced — or more precisely, large language model (LLM)-enhanced — demand forecasting

 

Imagine you already have machine-learning powered demand forecasting (perhaps even using Dataiku’s demand forecasting solution). A supply chain analyst interacts with data through a dedicated interface, allowing them to select specific SKUs, markets, stores, and timelines. 

What Generative AI can bring to the table is the ability to turn analytics into action. For example, say there’s a planned surge in demand for a SKU that’s in limited stock. This could not only be identified, but acted upon. Generative AI could provide the analyst with an accelerated capacity to follow up on this insight with an email to engage for a procurement activity. 

For this use case, Generative AI can bring:

  • Improved response time in purchase and markdown decisions with insights from real-time analytics.
  • Increased revenue thanks to continuous response to product demand.
  • Empowerment via improved communication and collaboration between teams, who are now able to turn analytics into action.

Automated Contract Intelligence

It’s not just going from data to action where Generative AI can bring value. Obviously LLMs shine in use cases with large amounts of data — massive amounts of unstructured text data, to be more precise.

Organizations seeking to optimize their sales and purchasing build rules-based analyses. Existing processes combine various natural language processing (NLP) techniques to index contracts and learn about their business. However, variability of contract language and format leads to complex, custom rules that are fragile and require ongoing maintenance by specialists.

Organizations can use an LLM to automate the extraction of important contract clauses, creating structured, tabular data out of chaotic documents. From there, they can automate the computation of, for example, compounded financial risks. Previously, this data would have gone untapped due to the expensive and specialized legal skill required to leverage it. LLMs create the possibility to scale that specialized knowledge.

wide angle shot of a warehouse showing potential use cases for generative ai in supply chain

Achieving Generative AI at Scale

Maybe you'll succeed in building your moonshot. A single chatbot that is accurate, safe, and cost-effective, serving the needs of the entire organization. However, it probably should not be your organization’s sole Generative AI strategy. It is too risky, and it misses the myriad improvements that are much closer at hand. 

How, then, can an organization succeed in scaling the use of Generative AI throughout their organization? There are three essential pillars to achieving this scale:

  1. Democratization. There is a massive gap between the potential applications and the number of experts who could build those applications. The solution is enabling more people to use and help build Generative AI applications.
  2. Acceleration. Competitive pressure means that time is of the essence. Applications or assets that are developed need to then be reused and repurposed. In other words, you cannot start from scratch for every new application of the technology.
  3. Trust. People must trust Generative AI, and it has to be low-risk.

Dataiku for Generative AI

With Dataiku, teams can move beyond the lab and build real and safe Generative AI applications at enterprise scale. Dataiku brings:

  • Enterprise-grade development tools. For example, build LLM-augmented projects with prompt engineering using Dataiku Prompt Studios.
  • Pre-built use cases, including a full use case collection with real-life, practical examples.
    AI-powered assistants (Dataiku AI Prepare) to help everyone do more with Generative AI.
Dataiku was conceived for this moment. Our founding vision was to democratize data and create a platform that allows organizations to seamlessly weave machine learning and AI innovations into their business. Our Generative AI capabilities deliver on this by unlocking the transformative potential of GenAI in every organization."
—Florian Douetteau, Co-Founder and CEO of Dataiku

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