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StoreCast: Production-Grade Forecasting System

How I'd save a 45-store retail chain $9.61M using real transaction data and my 5-step consulting process

Project Type: Self-initiated demonstration using real retail data
Goal: Prove I can build production-ready ML systems that deliver measurable ROI
Context: Simulated a consulting engagement for a retail chain bleeding margin from forecast errors

The Business Problem (Simulated Context)

Using real transaction data from a 45-store retail region generating $2.45 Billion in annualized revenue, I analyzed a classic profitability crisis:

The Pain Points

  • Forecasting Chaos: Manual heuristics producing an 11.85% error—leading to stockouts and massive overstock.
  • Trapped Capital: $216M in safety stock sitting idle just to buffer against unpredictability.
  • Wasted Markdowns: $110M burned on blanket discounts without regional elasticity modeling.

The Dollar Impact

This level of forecasting error would cost the business $9.61M annually in operational waste and tie up $20.53M in working capital that could be deployed elsewhere.

My 5-Step Process (How I Approached It)

This project showcases my repeatable methodology—the exact process I'd bring to your business challenge:

1. Problem Discovery

I analyzed the supply chain constraints, inventory turnover targets, and margin requirements. Built a simple ROI model connecting forecast error to hard dollar losses.

2. Research & Solution Evaluation

Evaluated 3 approaches: Simple statistical heuristics, Random Forest, and XGBoost (chosen for best accuracy-to-complexity ratio).

3. Feasibility Report (Before Building)

Mapped mathematical error reduction (11.85% → ~7.76%) to projected business impact ($9.61M profit growth). Stakeholders align on value before production code starts.

4. Production-Grade Build

Implemented a Medallion Lakehouse architecture using Polars (10x faster cleaning) and DuckDB (SQL aggregations without cluster overhead).

5. MLOps Deployment

Built a Human-in-the-Loop pipeline with MLflow for tracking, Optuna for tuning, SHAP for explainability, and Evidently AI for drift detection.

Technical Highlights

Why This Stack?

  • Rejected: Legacy tools like Hadoop/Pandas that bloat cloud costs.
  • Chose: Modern engines (Polars, DuckDB) that deliver 10x speed at 60% lower memory footprint.

Key Engineering Decisions

  • Polars over Pandas: Rust-backed parallelism cut processing from 2 hours → 5 minutes.
  • DuckDB over Spark: In-process SQL perfect for 1M rows—no cluster overhead.
  • XGBoost over Deep Learning: Simpler, explainable, and production-proven.

Advanced Analytics Integration

  • Anomaly Detection: Isolation Forests on dimensionless ratios prevented volume-bias "alert fatigue".
  • Market Basket Analysis: Used Pearson Correlation on residuals to isolate true causal cross-selling, stripping out holiday spikes.
  • Store Segmentation: Automated K-Means clustering mapped 45 stores into actionable archetypes for targeted marketing.

Results & Impact (Projected)

$9.61M

Annual Profit Increase

$20.53M

Freed Working Capital

320 hrs

Saved/Month via Automation

Operational Wins:

  • Zero manual intervention after deployment.
  • Drift detection caught seasonal shifts 2 weeks before accuracy degraded.
  • Full explainability via SHAP—stakeholders understand why forecasts changed.

What This Proves About My Process

  • I Think ROI-First: Started with dollar impact modeling, not algorithm selection.
  • I Handle Real-World Complexity: 1M+ transactions, missing data, and seasonality—not a clean lab dataset.
  • I Build Systems, Not Notebooks: Production-grade infrastructure that runs reliably.
  • I Don't Overengineer: Chose the 80/20 solution (XGBoost + DuckDB) to maximize speed-to-value.

Your Problem Is Different—And That's Exactly Why This Works

I don't claim to know your industry better than you do. If your domain is new to me, I apply the same rigorous process:

  1. I Listen: We define the real pain and KPIs.
  2. I Research: I evaluate 2-3 approaches tailored to your domain.
  3. You Get Proof First: A feasibility memo with rough ROI estimates before we build.
  4. I Build & Deliver: End-to-end, monitored, and documented.

Same Process. Different Data. Proven Methodology.