Smart Inventory Management with AI

DataPro partnered with the client to implement an end-to-end AI-based inventory optimization platform. Our approach combined time series forecasting, external data enrichment, and optimization algorithms to improve decision-making across the supply chain.

1. Unified Data Pipeline

We began by centralizing the client’s data sources:

• Historical sales data (SKU-level, regional breakdowns)

   

• Inventory levels and reorder history

   

• Supply chain lead times and vendor performance

   

• Promotional calendars and campaign impact

   

We also integrated external signals to enrich the model:

• Weather forecasts (to predict seasonal demand shifts)

   

• Local events and holidays

   

• Macroeconomic indicators (e.g., inflation, unemployment)

   

• Competitor pricing data from scraping APIs

   

This unified data pipeline ensured the model had a holistic view of demand drivers.

2. AI-Based Demand Forecasting Engine

Using historical and real-time data, we built a suite of machine learning models trained to predict product demand across categories and regions. The models included:

• Time series models (ARIMA, Prophet, XGBoost)

   

• Deep learning models (LSTM for long-term patterns)

   

• Ensemble methods to improve accuracy across product types

   

Our system dynamically retrained models weekly to capture recent trends and anomalies, ensuring that predictions stayed relevant.

3. Stock Optimization Algorithm

Beyond forecasting, we developed a prescriptive layer that optimized reorder quantities based on:

• Predicted demand

   

• Safety stock requirements

   

• Supplier lead times and variability

   

• Holding costs and shelf life constraints

   

This algorithm automatically suggested replenishment quantities, helping planners make more data-informed purchasing decisions without needing to review thousands of SKUs manually.