Supply Chain Use Case

AI-Driven Demand Forecasting for Distribution

Transforming distribution with predictive accuracy.

How one company turned supply chain unpredictability into operational advantage using machine learning — reducing overstock, improving forecast accuracy, and ensuring high-demand items are always in stock.

Overcoming Forecasting Inaccuracy in Distribution Operations

A full-line wholesale distributor specializing in food products and kitchen supplies managed an inventory exceeding 20,000 products, including dried goods and kitchenware. They catered to a diverse clientele across various regions.

Seasonal Variations:

Fluctuating demand for certain ingredients during specific times of the year

Market Trends:

Rapid shifts in consumer preferences impacting product demand

Inventory Management:

Balancing stock levels to prevent overstocking or stockouts

Demand Forecasting Dashboard

1
Sales Trend Analysis
2
Seasonal Patterns
3
SKU Performance
4
Inventory Levels
5
Supplier Lead Times

Steps to Success

ATLAS AI initiated the engagement with a collaborative workshop, bringing together key stakeholders to identify pain points and opportunities. Demand forecasting stood out as a high-impact, automation-ready use case.

01

Use Case Definition

ATLAS AI led a collaborative workshop with the client's key stakeholders to identify automation-ready opportunities.

Aligned on business goals and operational pain points
Prioritized use cases by value vs. effort
Demand forecasting ranked highest in feasibility and strategic impact
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Use Case Definition
02

Data Review & Feasibility

Our data engineers analyzed three years of operational history to evaluate readiness.

Mapped order history, supplier lead times, SKU movement
Identified seasonal and promotional demand signals
Resolved data gaps and inconsistencies for reliable model training
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Data Review & Feasibility
03

Pilot Development

We built and tested a hybrid forecasting model tailored to the client's operations.

Used Facebook Prophet and XGBoost to capture time-series and trend data
Trained models using SKU-level history, seasonality, and external variables
Tuned hyperparameters and validated results through backtesting
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Pilot Development
04

Validation & Demo

The model's performance was benchmarked against the client's manual forecasts.

Compared outputs over several product categories and time windows
Delivered a live demo to stakeholders, showing measurable improvements
Enabled confidence in next-step implementation
Pilot Development
Validation & Demo
05

Recommendations for Scaling

We outlined a clear roadmap to move the pilot into production.

Integration with client's procurement & ERP systems
Dashboarding and alert features for planners
Ongoing model refinement and training based on feedback loops
Full Agentic AI Implementation
Recommendations for Scaling
Results Dashboard

Tangible Results & Operational Impact

Stronger Forecasts. Leaner Inventory. Smarter Decisions.

ATLAS AI's AI-driven forecasting pilot delivered measurable improvements for the client — helping them move from reactive planning to predictive decision-making.

22%
Improvement in forecast accuracy
15%
Reduction in excess inventory
100%
Higher availability of in-demand SKUs
50%
Planning time freed up
Predictive inventory planning
Reduced stockouts and overstock
Faster procurement decisions

Frequently Asked Questions

Common questions about AI-driven demand forecasting

Success Stories

Real Results, Real Impact

See how leading companies have transformed their operations with Captivix.

Ready to Transform Your Demand Planning?

From reactive forecasting to predictive intelligence — we help you anticipate demand and optimize inventory.