Smart Inventory Forecasting

AI-driven demand planning to optimize stock levels and prevent stockouts.

Published April 1, 2024

1 min read

Technologies Used

Python

Facebook Prophet

AWS Lambda

Tableau

PostgreSQL

Dashboard showing predicted demand vs actual stock levels

The Right Product, at the Right Time

Retailers walk a tightrope between overstocking (tying up capital, risking waste) and understocking (losing sales). We built a Smart Inventory Forecasting engine that uses machine learning to predict future demand with high accuracy. By analyzing historical sales, seasonal trends, and even local weather patterns, the system recommends the optimal reorder points for every SKU.

The Challenge

Manual spreadsheets leading to inaccurate forecasts.

Frequent stockouts during peak seasons causing revenue loss.

Excess inventory of slow-moving items requiring deep discounts.

Inability to account for external factors like holidays or promotions.

The Solution

Ingested 3 years of historical sales data into a cloud data warehouse.

Implemented Facebook Prophet for time-series forecasting.

Integrated external signals (holidays, weather, marketing calendar).

Built an automated reorder alert system for the procurement team.

Key Features

Demand Prediction

Forecast sales for the next 30-90 days with 90%+ accuracy.

SKU-level granularity
Seasonality adjustment
Trend detection

Dynamic Reordering

Calculate optimal reorder points based on lead time and demand.

Safety stock calculation
Lead time variability
Bulk order optimization

Scenario Planning

Simulate the impact of promotions or supply chain disruptions.

What-if analysis
Price elasticity
Supplier risk

Technology Stack

Python

Facebook Prophet

AWS Lambda

Tableau

PostgreSQL

Project Timeline & Results

Week 1-3

Data Cleansing

Standardized data across 50 stores and 10,000 SKUs.

Aggregated sales logs from the POS system. Cleaned missing data points and handled outliers (e.g., one-off bulk buys). Categorized products by hierarchy (Category -> Sub-category -> SKU).

Week 4-6

Model Tuning

Reduced forecast error (MAPE) from 25% to 8%.

Trained the Prophet model. Tuned seasonality parameters to account for weekly and yearly cycles. Added 'holiday' regressors to capture spikes during Black Friday and Christmas.

Week 7-9

Deployment

Reduced inventory holding costs by 15%.

Deployed the model on AWS Lambda to run daily. Connected the output to Tableau for visualization. Trained the purchasing team to trust and use the AI recommendations.