Sales Forecast Solution For Retail

The Client

Client is a leading retail establishment, renowned for its expansive footprint and comprehensive product selection. The company is distinguished by its commitment to accessibility, exceptional value, and customer-centric service.

Key Outcomes

The solution employed advanced data preprocessing, classification, and feature engineering techniques to enhance the precision of demand prediction for diverse retail products.
We leveraged Azure-based tools like Azure Databricks and MLflow, for a scalable and efficient model deployment process

Business Challenges

The client struggled with managing noisy and inconsistent retail data for reliable sales forecasting. The client faced a major challenge in getting accurate historical sales data, assessing promotion impacts and capturing seasonality trends. Additionally, they faced difficulty in considering external factors like holidays, economics, weather, and local events. Therefore, the client required a robust solution that integrated seamlessly with existing systems while maintaining scalability and real-time performance.

Business Impacts / Key Results Achieved

Our AI/ML and data analytics experts partnered with the client to create a comprehensive solution. It tackled data quality issues, ensuring the accuracy of predictive models by eliminating data inconsistency. Additionally, our solution empowered the client to discern seasonality, trends, and promotion dynamics accurately, leading to a better understanding of promotion interactions. We integrated external factors, enhancing the model's adaptability to changing consumer behavior. We also provided expertise in nuanced evaluation metrics. This solution smoothly transitioned from controlled to real-world retail settings, managing integration complexities while ensuring scalability for seamless, real-time processing, ultimately delivering a robust and holistic solution to our client's data and forecasting challenges.

Strategy and Solutions

We initiated with collecting raw parquet files from the client, which were likely to suffer from noise. These files were securely stored in the "Bronze layer" via Azure Data Factory, where we meticulously pre-processed the data by filtering out irrelevant columns, handling missing values, and normalizing it. Before refining the data in the "Silver layer," we conducted Exploratory Data Analysis (EDA) to glean insights.</p><p id="">Subsequently, we categorized the data into sales behavior types: Fast moving, Average moving, Slow moving, and Seasonal, while also identifying outliers representing low-sales items. Feature engineering followed, with careful selection of essential attributes such as Fourier terms, unit lag, and the influence of holidays, days of the week, and months.</p><p id="">Our model training involved various AI models fine-tuned for specific parameters and optimized for accuracy using ensemble techniques. We aimed to achieve precise sales forecasting for baseline and promotional scenarios, forecasting incrementally by leveraging past predictions.</p><p id="">Finally, for model deployment, we efficiently logged and stored models using MLOps tools like MLflow, ensuring scalability and reliability. Our Azure-based solution harnessed Azure Databricks, MLflow, Azure Data Factory, and blob storage to create a seamless and scalable pipeline. This end-to-end solution empowered our client with the necessary tools and insights to excel in retail demand prediction and sales forecasting, offering unparalleled accuracy and a strategic edge.

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