Case Study: AI-Driven Demand Forecasting

Retail

AI-Driven Demand Forecasting

The Challenge: Balancing Inventory in a Dynamic E-commerce Landscape

A large, rapidly growing online retailer faced persistent challenges with inventory management across its extensive product catalog. Inaccurate demand forecasting led to a damaging cycle of problems: frequent stockouts on popular items resulted in lost sales, frustrated customers, and potential abandonment to competitors. Conversely, overstocking less popular or seasonal items tied up significant working capital, increased warehousing costs, and heightened the risk of obsolescence and markdowns, eroding profit margins. Existing forecasting methods, often based on simple historical averages or manual adjustments, failed to capture complex demand patterns influenced by seasonality, promotions, competitor actions, and emerging trends in the fast-paced e-commerce environment.

Our Solution: Granular Forecasting with Machine Learning on AWS

Lydatum developed a sophisticated, automated demand forecasting system built on AWS, designed to provide accurate predictions at a granular level (e.g., SKU/region) and integrate seamlessly with the retailer's inventory planning systems. The solution involved:

• Centralized Sales Data Repository (Amazon Redshift): Historical sales data, promotion schedules, pricing information, website traffic, inventory levels, and relevant external factors (e.g., holidays, competitor pricing) were consolidated into Amazon Redshift. This provided a scalable and performant data warehouse optimized for analytical workloads.
• Advanced Machine Learning Models (Amazon SageMaker): We utilized Amazon SageMaker to build, train, and deploy a suite of machine learning models tailored for demand forecasting. This included time-series models (like ARIMA and Prophet) as well as more complex algorithms (like Gradient Boosting and Deep Learning/LSTM networks) capable of capturing intricate relationships and non-linear patterns. Feature engineering played a crucial role, incorporating variables for seasonality, holidays, promotional effects, price elasticity, and product lifecycle stages. Models were trained and retrained regularly to adapt to changing market dynamics.
• Automated Forecasting Pipeline: An automated pipeline orchestrated the entire process: data ingestion and preparation in Redshift, feature engineering, model training/retraining and hyperparameter tuning in SageMaker, generating forecasts at the required granularity, and evaluating model performance.
• Integration with Inventory Systems: The generated forecasts were automatically fed into the retailer's existing inventory management and replenishment systems via APIs. This allowed for data-driven optimization of safety stock levels, reorder points, and allocation strategies across the distribution network.

The Impact: Optimized Inventory, Reduced Costs, and Efficient Supply Chain

The AI-driven demand forecasting system delivered significant operational and financial benefits:

The increased forecast accuracy led to a substantial reduction in stockouts, directly translating to increased sales revenue and improved customer satisfaction. Simultaneously, by avoiding unnecessary overstocking, the retailer significantly reduced inventory holding costs, freeing up capital and minimizing losses from obsolescence. The reliable forecasts enabled more efficient supply chain planning, better coordination with suppliers, optimized warehouse utilization, and streamlined logistics operations. This holistic improvement in inventory management provided a strong competitive advantage in the demanding online retail market.

Technologies Used: Amazon SageMaker, Amazon Redshift, Amazon S3, AWS Glue, AWS Step Functions, Python (Pandas, Scikit-learn, forecasting libraries), SQL