Case Study: Mabe achieves AI-driven refrigerator performance optimization and energy savings with Altair RapidMiner

Altair RapidMiner User Behavior Predictions Help Optimize Appliance Performance

Mabe, a Mexico City–based appliance manufacturer selling refrigerators and other white goods in more than 70 countries, faced the challenge of turning large volumes of field data from connected refrigerators into actionable insights to optimize compressor and air‑circulation fan cycles based on door‑open frequency and duration. Mabe worked with Altair, using Altair RapidMiner capabilities within Altair AI Studio, to explore whether predicting consumer behavior could reduce energy use while keeping food fresher.

Altair built an AI workflow that extracts, cleans, transforms and models more than one million records to predict door‑opening patterns by day of week, ran a collaborative PoC with weekly feedback, and deployed the scoring workflow via Altair AI Hub in about 60 days. The Altair solution lets Mabe score new data, recommend changes to automated functions, and test models against historical datasets—enabling measurable handling of >1M records and a rapid 60‑day turnaround to drive energy savings and improved product performance.

Mabe

Martin Ortega

Design Leader