AI Services
Demand Forecasting For Retail Store Chain
- Objective: To improve inventory management and reduce stockouts and overstock situations by accurately forecasting product demand across multiple retail stores.
- Challenges High demand variability due to seasonality, promotions, and regional factors. Limited visibility into consumer buying patterns, impacting supply chain efficiency. Over-reliance on manual forecasting, leading to inaccuracies and stock-related losses.
Solution Approach
Data Collection & Preprocessing
- Aggregated historical sales data, weather patterns, promotions, and holidays. Integrated data from POS systems, CRM, and external market trend data sources
Model Development MLOps
- Developed a machine learning model using time series forecasting techniques (ARIMA, Prophet) and ensemble models for improved accuracy.
- Incorporated regional, store-level demand drivers and macroeconomic indicators
Implementation & Automation
- Implemented the model on a cloud platform with automated data ingestion and daily forecast generation
- Built a dashboard for real-time insights on demand forecasts across stores.