The Challenge with Traditional Forecasting
This section outlines the inherent limitations of traditional demand forecasting models in today's fast-moving consumer landscape. These models often rely on historical sales data and pre-defined variables like promotions or holidays, which causes them to miss the sudden, powerful impact of new social trends, viral content, and changing consumer sentiment, leading to inaccurate predictions.
Reactive, Not Predictive
Models react to sales after they happen, struggling to anticipate shifts in demand driven by new, un-tracked phenomena.
Misses Social & Cultural Trends
A viral recipe on TikTok or a new health trend can create a demand surge that historical data alone cannot predict.
Data Latency
Reliance on weekly or monthly sales data creates a lag, making it hard to respond to daily or even hourly shifts in consumer interest.
The LLM-Powered Solution
Here we introduce a novel framework that uses Large Language Models (LLMs) to read and understand the digital world in real-time. By processing vast amounts of unstructured text from social media, news, and blogs, this system identifies emerging trends and quantifies them into new, powerful 'signals' that are then fed into forecasting models to dramatically improve their accuracy and responsiveness.
1. Data Ingestion
Collect real-time unstructured data from sources like social media, news, and food blogs.
2. LLM Analysis
LLM processes text to identify trends, sentiment, and emerging topics related to food products.
3. Signal Creation
Quantify insights into numerical signals (e.g., 'Viral Recipe Index', 'Health Trend Score').
4. Model Integration
Feed new signals into the demand forecasting model to generate more accurate predictions.
Uncovering New Signals: An Interactive Demo
This interactive dashboard demonstrates the power of LLM-generated signals. Select a food product from the dropdown menu to see how the LLM-enhanced forecast (in blue) more closely tracks actual sales compared to the traditional forecast. Below the chart, you can explore the specific real-time signals, such as social media trends or health news, that the LLM identified to make its more accurate prediction.
Weekly Sales Forecast: Oat Milk
Key LLM-Generated Signals Driving the Forecast
The Quantifiable Impact
By integrating LLM-generated signals, businesses can achieve significant, measurable improvements across the supply chain. This section visualizes the impact on key performance indicators. The charts below compare the accuracy of different forecasting models and highlight the resulting reductions in waste and stockouts, demonstrating the clear return on investment from adopting this advanced technology.
Forecast Accuracy (Lower is Better)
Reduction in Spoilage & Waste
Increase in On-Shelf Availability
Implementation & Considerations
Adopting an LLM-based demand sensing system is a strategic move that requires careful planning. This final section outlines the key components and challenges to consider for a successful implementation, from sourcing the right data to building the necessary technical infrastructure and team expertise.
What You Need
- Access to diverse, real-time data streams.
- Cloud computing infrastructure for data processing.
- LLM APIs (e.g., Gemini, OpenAI) or self-hosted models.
- Data science and ML engineering talent.
- Integration with existing forecasting and ERP systems.
Potential Challenges
- Ensuring data quality and filtering out noise.
- Managing the computational cost of LLM inference.
- Model explainability and building trust with planners.
- Keeping up with the rapid evolution of LLM technology.
- Data privacy and ethical considerations.