Problem 3
Detect the shortage, choose the quantity and vendor, and send the purchase order.
For any business dealing with physical goods, inventory management is a massive headache. Out-of-stock items lead to lost revenue and angry customers, while overstocking ties up valuable cash.
Current dashboards show inventory levels, but they still require a human to notice a shortage, calculate how much to order, and send an email to a vendor. The challenge is to shift from monitoring to autonomous execution — creating an AI that manages the warehouse itself.
This problem is relevant to organizations and teams managing physical goods and vendor relationships, including:
The solution should have the potential to become a reusable product or platform capable of managing inventory across different organizational domains.
Build an AI agent that monitors inventory databases, predicts shortages, drafts purchase orders, and interacts with suppliers to restock goods automatically.
The system should analyze inventory data, identify items at risk of stocking out, determine appropriate order quantities, and take the correct action with the correct vendor.
Participants are not required to support every inventory type. Teams may choose the data sources and difficulty levels they want to address and are encouraged to innovate beyond the examples provided.
Data Analysis
Read and interpret tabular inventory data.
Proactive Execution
Trigger actions (like sending an email or generating a PDF Purchase Order) based on logical thresholds.
Negotiation/Communication
Interact dynamically with vendor APIs or simulated vendor emails.
Level 1 (Basic Agency)
The agent reads a CSV of inventory, identifies items falling below a minimum threshold, and generates a plain-text report of what needs to be ordered.
Level 2 (Tool Integration)
When stock is low, the agent autonomously calls a mock "Email API" to draft and send a Purchase Order to the correct vendor based on predefined supplier lists.
Level 3 (Multi-Agent Collaboration)
The "Inventory Agent" realizes it needs 500 laptops. It passes the task to a "Negotiator Agent," which simulates emailing three different vendors, compares their price quotes, and autonomously selects the cheapest option.
Level 4 (Advanced/Predictive)
The agent doesn't just react to low stock; it uses external APIs (like a Weather API or Calendar API) to predict demand. For example, it autonomously orders extra umbrellas because the forecast shows heavy rain next week.
An autonomous background system that maintains inventory levels and produces verifiable purchase orders/communications.
A generated restock action may include:
Participants are free to decide:
Teams may use real, synthetic, or publicly available inventory data where appropriate.
1. Meaningful AI Usage
The solution should demonstrate why intelligent processing is required for the selected problem. Solutions should go beyond simply applying a fixed threshold check.
2. Analysis and Action Should Be Connected
The extracted inventory signals should meaningfully influence what is ordered, how much, and from whom.
3. Order Accuracy
The solution should attempt to minimize over-ordering, under-ordering, or selecting an inappropriate vendor.
4. Appropriate Processing for Different Inputs
Teams supporting multiple inventory types or vendor formats should demonstrate how their approach adapts to differences in structure and information.
5. Machine-Usable / Actionable Output
Generated purchase orders and communications should be structured so that they can be meaningfully used by a vendor or downstream system.
6. Efficiency
Participants should consider LLM consumption, token usage, model selection, processing time, cost, and scalability. Using an LLM for every processing step without considering efficiency should not automatically be considered a better solution.
7. Innovation Freedom
Participants are not restricted to a specific model, framework, forecasting algorithm, architecture, or technology.
What It Measures
Accuracy of identifying real shortages versus noise
Correctness of quantities and vendor selection
Proactive Execution Ability to trigger the right action automatically
Ability to handle different inventory structures or data sources
Whether AI provides meaningful value beyond basic automation
Architecture, implementation, and robustness
Effective use of models, computation, cost, and latency
Novelty of the forecasting or negotiation approach
Potential to manage large or multi-warehouse inventories
Ability to evolve into a practical organizational product
Ability to clearly demonstrate the working solution
Participants are free to choose their own technical approach.
Solutions may use any relevant combination of:
The listed technologies are examples only and should not limit innovation.
Given inventory data, autonomously detect shortages, predict demand, and execute accurate, cost-effective restocking actions with minimal human intervention.