In the retail industry, accurate demand forecasting is critical to ensure that retailers can maintain optimal inventory levels and avoid stockouts. Stockouts lead to lost sales and dissatisfied customers, while excessive inventory levels increase costs and reduce profitability. Accurate demand forecasting enables retailers to order the right quantities of products at the right time, reduce inventory costs, and improve customer satisfaction.
In this case study, we will discuss how Sumo Analytics, a data science and AI solutions agency, helped a large grocery retailer in Europe improve their demand planning significantly with an advanced AI-driven forecasting method.
Background:
The grocery retailer had been using traditional forecasting methods that relied on historical data and basic statistical models. However, these methods were not able to capture the complexity of the retailer's business and the changing market conditions. The retailer experienced frequent stockouts and excess inventory, which resulted in high costs and lost sales.
The retailer approached Sumo Analytics to help them improve their demand planning and forecasting. Sumo Analytics proposed an advanced AI-driven forecasting method that uses machine learning algorithms to analyze a wide range of data sources and predict future demand with high accuracy.
Solution:
Sumo Analytics implemented a customized forecasting solution for the retailer, which involved the following steps:
Data Collection: Sumo Analytics collected data from various sources, including sales data, marketing campaigns, weather data, and social media trends. The data was cleaned, processed, and stored in a centralized data warehouse.
Feature Engineering: Sumo Analytics performed feature engineering to extract relevant features from the data. This involved identifying key variables that influence demand, such as promotions, pricing, seasonality, and customer behavior.
Model Development: Sumo Analytics developed a machine learning model that could forecast demand based on the identified features. The model was trained on historical data and validated on a hold-out set to ensure its accuracy.
Deployment and Integration: The forecasting model was deployed into the retailer's existing demand planning system. Sumo Analytics worked with the retailer's IT team to ensure seamless integration and testing.
Results:
The advanced forecasting method implemented by Sumo Analytics enabled the retailer to improve their demand planning significantly. The retailer was able to reduce their monthly inventory from EUR 28 million to EUR 16 million, resulting in significant cost savings. They also reduced stockouts, which improved customer satisfaction and increased sales.
The AI-driven forecasting method provided the retailer with more accurate and granular demand forecasts, which allowed them to optimize their inventory levels and reduce waste. The retailer was also able to identify trends and patterns in customer behavior and adjust their marketing and pricing strategies accordingly.
Conclusion:
In conclusion, accurate demand forecasting is critical to the success of retailers in the highly competitive and rapidly changing retail industry. Sumo Analytics' advanced AI-driven forecasting method provides retailers with the ability to analyze a wide range of data sources and predict future demand with high accuracy. This case study demonstrates the significant impact that accurate forecasting can have on inventory levels, costs, and customer satisfaction.
