Demand Forecasting in Retail

Industry Challenges & Trends

Organizations in retail find it challenging to accurately forecast demand for products and services, which results in increased waste and frequent stockouts. Statistics-heavy and data-rich, they can avoid profit loss caused by overstock and waste, and increase revenue by mitigating the cost of stockouts through AI/ML-powered predictive analytics capable of generating accurate demand predictions.

Demand Forecasting Solution

The Provectus Demand Forecasting Solution applies a well-architected predictive analytics platform to generate forecasts and gain insights from current inventory data, historical sales data, and growth projections. The solution mitigates technology risks of a full-scale AI/ML implementation across the organization while meeting its unique demand forecasting needs. The solution proves to be impactful for FMCG businesses dealing with perishable goods, the eCommerce sector with explosive demand in peak sales seasons, and other industries highly reliant on cycle time.

Real-world Case

The Client, a US-based coffee retailer and roaster, sought to reduce stockouts and cut waste of coffee beans, pastry, and culinary products across its chain of coffee shops. ML-driven predictive ordering system delivered by Provectus allowed for more accurate estimates of future supplies and ensured more efficient coffee and food utilization leading to reduced waste and associated costs by 3x, and increased the total revenue by 5%.

Key Features

• Pre-built machine learning architecture, including infrastructure for model design, training, and deployment in production
• Pre-trained machine learning algorithms that learn, adapt, and improve demand forecasts based on data
• Removal of tedious and time-consuming manual guesswork through demand forecasting automation
• Scalable across retail, ecommerce, and other industries to cover sales- and inventory-specific tasks
• Cloud vendor agnostic – The architecture is certified to be used with different cloud providers.

How It Works

Demand Forecasting Solution applies Time Series Analysis to power ML demand forecasting models. Computational workflows and data processing pipelines are orchestrated by Airflow and Amazon ECS. The solution is based upon three pipelines — to convert and transfer data to Amazon S3; to train ML demand forecasting models and store the training results in Amazon S3; to generate and store the predictions in Amazon S3 and Amazon RDS. Amazon SageMaker Training and Amazon SageMaker Batch Transform are implemented to train and execute on the models using the latest data. The data science team receives a specific Model Development Pipeline to enhance, test, and compare existing models, and to select the best-performing ones to be used in production.