Case Study: a mid-sized furniture retailer reduces inventory costs with Concurrency's AI demand planning

Mid-sized Furniture Retailer saves $16M annually with Concurrency, Inc.'s AI demand planning

A mid-sized furniture retailer was facing significant supply chain challenges, including $34 million in excess inventory carrying costs and missed revenue from stockouts. Their supply chain analysts struggled with inaccurate forecasts and poor visibility, which led to erratic inventory levels and eroded trust in their processes. They partnered with Concurrency, Inc. to develop an AI-driven demand planning solution to address these issues.

Concurrency, Inc. implemented a fully automated demand planning system built on Azure, Python, and Databricks that accurately predicts 10-to-26-week demand for 10,000 products. This solution improved forecasting accuracy by 15%, resulting in annual savings of $16 million across the retailer's 120 stores. The system also provided improved visibility and helped the supply chain team rebuild trust within the business, leading to better strategic decision-making and enhanced operational efficiency.