AI & Data Science10 min
20%+
Revenue Impact
80/20 Validation
Forecast Accuracy
Weekly
Model Frequency
3+ Years History
Data Window
Challenge
The Problem
Standard ERP/DMS systems provide historical snapshots but fail to predict future volatility. The client struggled with high stockouts and inefficient sales rep routing because they lacked a way to process massive DATA volumes into actionable Saturday-morning targets.
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Growth Compass: Agentic Demand Forecasting
Strategy
The Objective
The primary objective was to move beyond static reporting into Predictive Intelligence. We needed to solve three specific business hurdles: SKU-level demand forecasting to reduce lost sales, identifying at-risk retailers before they churned, and scoring sales rep productivity based on efficiency rather than just volume. The goal was a Test & Learn environment where Region A (Test) could be statistically compared against Region B (Control) to prove the ROI of AI-driven decisions.
Implementation
Technical Execution
I architected a Hybrid AI Pipeline. For numerical forecasting, we avoided the high cost of LLMs and instead built custom Prophet and XGBoost models. We performed rigorous Feature Engineering, adding external shifters like holidays and weather patterns to 3 years of DMS DATA. For the user interface, we implemented an Agentic Reasoning layer. Instead of traditional RAG (which is inefficient for numbers), we used a Text-to-SQL approach. When a manager asks a natural language question, the LLM references the Table Schema, writes a targeted SQL query to the BigQuery warehouse, and analyzes the result in real-time.
Results
Business Impact
The deployment transformed the sales cycle. By running the ML Pipeline every Friday night, sales reps received fresh, optimized Daily Targets every Saturday morning. This removed the guessing game from distribution. The A/B Testing Analytics dashboard confirmed that the growth in the test regions was statistically significant and not just noise. This modular approach-using specialized models for numbers and LLMs for reasoning-ensured a high-performance system with a manageable total cost of ownership.