Case Study: Improving Sales Forecasting for a Retail Client

How DSIGHTS helped a leading retail company improve inventory management and increase profits through predictive analytics.

The Challenge

A large retail client with hundreds of stores across the country was struggling with inaccurate sales forecasting. This led to frequent stockouts of popular items and overstocking of others, resulting in lost sales and increased carrying costs. Their existing forecasting methods were based on historical sales data and manual adjustments, which were time-consuming and unable to account for the complex factors that influence sales.

Our Solution

DSIGHTS was brought in to develop a more accurate and automated sales forecasting solution. Our team of data scientists and engineers worked closely with the client to understand their business and data. We then designed and implemented a custom predictive analytics solution that leveraged machine learning to forecast sales at the individual store and product level.

Technology Stack

The solution was built on a modern technology stack that included:

• Python: For data analysis, model development, and automation.
• Scikit-learn: For building and training machine learning models.
• TensorFlow: For developing deep learning models to capture complex patterns in the data.
• AWS: For cloud infrastructure, including data storage, processing, and model deployment.
• Apache Spark: For large-scale data processing and feature engineering.

The Process

Our process involved several key steps:

1. Data Collection and Preparation: We collected and cleaned historical sales data, as well as external data sources such as weather, holidays, and promotional events.
2. Feature Engineering: We created new features from the existing data to improve model accuracy, such as moving averages, seasonality, and trend indicators.
3. Model Development and Training: We developed and trained a variety of machine learning models, including linear regression, random forests, and gradient boosting, as well as a custom deep learning model.
4. Model Evaluation and Selection: We evaluated the performance of each model using a variety of metrics and selected the best-performing model for deployment.
5. Deployment and Automation: We deployed the selected model to a production environment and automated the entire forecasting process, from data ingestion to prediction generation.

The Results

The new sales forecasting solution delivered significant improvements for the client, including:

• 25% reduction in stockouts: This led to a significant increase in sales and customer satisfaction.
• 15% reduction in overstocking: This resulted in lower carrying costs and improved profitability.
• Improved forecasting accuracy: The new solution was able to forecast sales with over 90% accuracy, compared to less than 70% with the previous methods.
• Increased efficiency: The automated forecasting process freed up the client's team to focus on more strategic initiatives.

Conclusion

By leveraging the power of predictive analytics and machine learning, DSIGHTS was able to help the retail client overcome their sales forecasting challenges and achieve significant business results. This case study demonstrates the transformative impact that data science and AI can have on the retail industry.