How Pavoi Streamlined Inventory Management as a Leading E-commerce Fashion Brand

Accurate demand forecasting is critical for operational efficiency and profitability in the fast-moving e-commerce sector, particularly within the fashion industry.

Pavoi, a leading jewelry and clothing online retailer, faced challenges in forecasting demand for their extensive product catalog, leading to overstocking of products that are no longer trending, and stockouts of products that are increasing in popularity.

To address these complexities, Pavoi partnered with Resonancy in building a sophisticated demand forecasting solution. This case study explores how Resonancy helped Pavoi optimize their inventory management and improve overall profitability.

Challenge: Balancing Inventory in a Trend-Driven Market

Pavoi's business is characterized by a high volume of products influenced by rapidly evolving seasonal and social media trends. This dynamic environment makes it difficult to accurately predict demand, resulting in two key problems:

• Overstocking and High Storage Costs: Without clear demand forecasts, warehouses were often filled with slow-moving or unsold items, leading to increased storage expenses.
• Stockouts of Trending Products: Conversely, when a trend would gain popularity, Pavoi struggled to meet the sudden surge in demand, resulting in lost revenue and dissatisfied customers.

The challenge is further compounded by long supplier lead times requiring accurate long-term forecasts for effective procurement. The lack of a unified view of marketing and sales data also hindered their ability to understand demand signals effectively. Given Pavoi's extensive product catalog and the abundance of data, running forecasts for each SKU near realtime, requires well-designed and reliable data pipelines on top of model accuracy.

Solution: Leveraging Advanced Time Series Modeling and Scalable Software Architecture

Resonancy addressed Pavoi's challenges by implementing a sales forecasting and reporting model based on an additive time series approach. This method excels at analyzing data that exhibits strong seasonal patterns and trends over time. The core of the solution involved:

• Historical Sales Pattern Recognition: Analyzing past sales data at the individual product level (SKU) to identify recurring demand patterns and long-term trends.
• Seasonality and Holiday Effects Modeling: Incorporating calendar-based influences, such as holiday sales spikes, to predict fluctuations in demand. The model automatically accounts for yearly, weekly, and daily seasonal patterns.
• New Product Demand Estimation: For new products without historical data, initial forecasts were generated using category-level sales trends as a baseline, which were then refined as sales data became available.
• Integration of Marketing Insights: The solution aimed to unify marketing and sales data, allowing for the incorporation of marketing campaign performance to further enhance accuracy.

The implemented forecasting pipeline, delivered speedy and reliable product-level demand predictions. The underlying model decomposes time series data into trend, seasonality, and holiday components, providing interpretable forecasts. The data infrastructure was built for scalability and robustness to effectively handle Pavoi's extensive product catalog and large data volume.

Outcomes: Enhanced Efficiency and Reduced Costs

The implementation of Resonancy's forecasting solution yielded significant improvements for Pavoi:

• Improved forecasting accuracy led to having the right products at the right warehouses, minimizing storage and transport expenses.
• Reduced guesswork for seasonal pre-orders with long lead times minimized the risk of overstocking or stockouts.
• The solution provided a scalable framework capable of adapting to Pavoi's evolving product catalog and incorporating new data as it becomes available.

Why It Worked: A Blend of Granular Data, Intelligent Modeling, and Scalable Software Architecture

The success of this project stemmed from several key factors:

• Forecasting demand at the individual product level provided a detailed understanding of specific product performance and unique demand patterns.
• Leveraging historical data from similar products and observed trends, allowed for reasonable demand forecasting of new items added to Pavoi’s catalog.
• The forecasting model was designed to specifically handle time series data with strong seasonal variations and adapt to changing trends, crucial for the fashion industry.
• A scalable software solution is essential for efficiently processing the large volume of data and running forecasts for Pavoi's extensive product catalog.

Resonancy took the time to thoroughly understand our specific business needs and operations. Their thoughtful approach resulted in a custom solution that has reliably scaled with our growth – something our previous off-the-shelf software couldn't achieve.

Conclusion

Pavoi successfully transformed its approach to demand forecasting. The implementation of an advanced time series model, coupled with a scalable software architecture, provided accurate and actionable insights, leading to significant improvements in inventory management, cost reduction, and overall operational efficiency.