For SageGlass, an architectural glass manufacturer and subsidiary of the $45 billion dollar multinational corporation Saint-Gobain, glass is customized to reduce costs for building owners, increase energy efficiency and improve occupant comfort through electrochromic tinting. Our data science work increased yield and provided SageGlass with smarter, more actionable data to make better business decisions.
Due to the highly-customized nature of SageGlass’ product, waste scrap is unusable. The SageGlass team approached Nerdery wanting to leverage data science to improve the production planning process, reduce overrun and increase the on-time/in-full (OTIF) rate. With a complex challenge and a quick deadline — two months — we quickly ramped up to understand their business, determine OTIF rate and overrun targets, and define a solution incorporating a fully-featured, custom-built prediction engine.
Nerdery performed a thorough analysis of their data to understand how it related to the problem at hand. The data was used to build and validate several machine learning models. Those models were integrated into a user interface the SageGlass team could use immediately, providing opportunities for fast business impact.
With data science driving real-time production forecasts, profitability was increased by accurately predicting yields, reducing waste scrap, and gaining actionable insight into the OTIF rate.
Our data science work increased yield by 85% and provided SageGlass with smarter, more actionable data to make better business decisions.