DemandLens: Enhancing Forecast Accuracy Through Product-Specific Hyperparameter Optimization
This addresses the critical need for accurate forecasting in an industry reliant on third-party manufacturing, though it is incremental as it applies an existing method to a specific domain.
The paper tackles sales forecasting for mattress-in-a-box contract manufacturers by developing a Prophet-based model with SKU-specific hyperparameter optimization and COVID-19 metrics, achieving strong predictive capabilities to streamline supply chain operations.
DemandLens demonstrates an innovative Prophet based forecasting model for the mattress-in-a-box industry, incorporating COVID-19 metrics and SKU-specific hyperparameter optimization. This industry has seen significant growth of E-commerce players in the recent years, wherein the business model majorly relies on outsourcing Mattress manufacturing and related logistics and supply chain operations, focusing on marketing the product and driving conversions through Direct-to-Consumer sales channels. Now, within the United States, there are a limited number of Mattress contract manufacturers available, and hence, it is important that they manage their raw materials, supply chain, and, inventory intelligently, to be able to cater maximum Mattress brands. Our approach addresses the critical need for accurate Sales Forecasting in an industry that is heavily dependent on third-party Contract Manufacturing. This, in turn, helps the contract manufacturers to be prepared, hence, avoiding bottleneck scenarios, and aiding them to source raw materials at optimal rates. The model demonstrates strong predictive capabilities through SKU-specific Hyperparameter optimization, offering the Contract Manufacturers and Mattress brands a reliable tool to streamline supply chain operations.