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What are Gen AI Agents, and How Do They Impact Supply Chain Planning?

May 24, 2024 | Premangsu Bhattacharya

Blog / What are Gen AI Agents, and How Do They Impact Supply Chain Planning?

When exploring AI, you’ll often encounter terms like “Generative AI Agents” and “Large language models” (LLMs). Both are powerful tools, but they serve different purposes and are tailored for specific tasks, including those in the supply chain.

Large Language Models, like Google’s Gemini Pro, specialize in understanding and generating text. They excel at digesting extensive text data and responding to queries with accurate textual outputs. For supply chain applications, LLMs can automate documentation, generate reports, or facilitate communication across different parts of the supply chain by processing inquiries and providing information efficiently.

Generative AI Agents are designed to interact dynamically with their environment and users. Think of them as the workers on the factory floor or in warehouses who not only perform tasks but also react to changes, learn from interactions, and even initiate actions based on real-time data. In the context of the supply chain, these agents can manage inventory, handle customer service interactions, and oversee logistics operations, adapting as conditions change.

Generative AI Agents build on what LLMs can do by automating entire workflows. These agents excel in responding to changes in real-time by integrating external signals like market trends directly into the operational workflow. For instance, if a product suddenly becomes popular on social media, the agent can quickly adjust inventory forecasts and supplier orders to meet the increased demand. This means stores have just the right amount of stock without needing a person to make changes manually. 

In the following example, Pluto7’s Gen AI agent connects realtime data across multiple departments, as shown in the image. This includes sales, marketing, finance, supply chain, and manufacturing. They help users find quick answers to natural language questions such as:

  • What are current inventory levels compared to last month?
  • How are our sales performing this quarter across different regions and product categories?
  • What is the return on investment for our latest marketing campaign compared to the previous one?
  • What potential disruptions are there in the supply chain, and what are their expected impacts on production schedules?
  • How has the manufacturing output trended over the past six months, and what are the projections for the next quarter?

For a deep dive into Pluto7’s Gen AI Agents, head to this page.

Gen AI Agents in Demand Planning: How Do They Work?

Image Attribution: Image by drobotdean on Freepik.

Let’s look at the following example. 

A leading tech company experiences critical stockouts of new fitness trackers during January—a peak purchasing month driven by New Year’s resolutions. Despite operating 200 stores across the U.S., their forecasting models fail to predict the sudden surge in demand accurately, especially in urban areas where health trends quickly gain traction.

Core Issues:

  • Rapid Demand Changes: The company’s forecasting tools rely too much on past sales data and can’t keep up with sudden spikes in interest, particularly after aggressive social media campaigns.
  • Mismatched Data Syncing: Despite having access to real-time sales data from POS systems across 200 stores, the retailer hasn’t effectively utilized this information for predictive analytics.
  • External Demand Drivers: The impact of social media trends on consumer purchasing decisions is substantial yet underrepresented in forecasting models. 

Capturing the Signal and Acting on It

The challenges this company faces are all too common in the retail industry. Now, let’s imagine a different scenario. What if this company had integrated Pluto7’s CX Sense Gen AI Agent into their demand planning process? This intelligent agent could capture real-time signals, such as spikes in social media activity, and integrate them into the company’s forecasting models. Let’s discuss how CX Sense would address and solve the identified issues.

  • Real-Time Data Ingestion: CX Sense monitors retail POS data from all 200 stores and social media trends.
  • Data Integration: It seamlessly integrates these diverse data sources, creating a comprehensive view of current demand.
  • Predictive Analysis: Using machine learning, the agent analyzes this data to forecast demand accurately, even accounting for sudden spikes driven by social media campaigns.
  • Automated Adjustments: Based on the forecast, the agent automatically adjusts inventory levels and supplier orders in real-time, ensuring optimal stock availability across all stores.

The biggest upside to Gen AI agents is the productivity gain. With this agent, planners can get more done with less effort, allowing them to focus on strategic decision-making and innovation. 

  Manual Work CX Sense Gen AI Agent
Collecting POS and Social Media Data 6 hours daily, multiple team members Real-time, completely automated
Merging Data from Different Sources 1-2 days per week, 2 data analysts Real-time, completely automated
Adjusting Demand Forecasts 8-10 hours per forecast cycle, 1 data analyst Instant, completely automated
Reviewing & Adjusting Inventory Levels 4-6 hours per week, 1 inventory manager Real-time, completely automated
Monitoring Market Trends 3-4 hours daily, 2 marketing team members Real-time, completely automated

How to Get Started with Gen AI Agents

Great in concept, but will it actually work in your current setup? If this question is on your mind, I invite you to a free workshop with me and my team. We will bring in all the tools and tech; you bring just the business problem you are trying to solve. In a single 1-hour session, we will brainstorm and arrive at a solution that fits your needs.

Sign up for the Workshop Here

Let’s work together to make your operations more efficient and your business more profitable.


Premangsu B, is a digital marketer with a knack for crafting engaging B2B content. His writings are focused on data analytics, marketing, emerging tech, and cloud computing. Driven by his passion for storytelling, he consistently simplifies complex topics for his readers, creating narratives that resonate with diverse audiences.

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