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AI-Driven IoT Solution Enhances Product Quality and Customer Service for Mabe
Technology Category
- Analytics & Modeling - Machine Learning
- Application Infrastructure & Middleware - Data Visualization
Applicable Industries
- Electronics
- Equipment & Machinery
Applicable Functions
- Product Research & Development
- Sales & Marketing
Use Cases
- Intelligent Urban Water Supply Management
- Smart Campus
Services
- Data Science Services
- System Integration
The Challenge
Mabe, a Mexico City-based home appliances manufacturer, recently launched a high-end washing machine that generates over 20 signals to measure various parameters such as water temperature, water levels, vibration, torque, noise, pressure, rotor position, etc. This smart, connected product allows Mabe's product team to analyze real-time streaming sensor data to understand in-service use-cases and predict potential failures. It also enables them to aggregate and analyze data collected from many in-service machines over long periods of time to inform next-generation design improvements, material selections, and supplier options for subassemblies and components. However, Mabe faced a challenge in efficiently and automatically managing the vast amount of data, including its velocity and complexity.
About The Customer
Mabe is a leading manufacturer of home appliances, including stoves, refrigerators, washing machines, dryers, water purifiers, and more. The company is headquartered in Mexico City and markets its white goods under its own brand as well as several others, including GE Appliances, in more than 70 countries. Mabe is an early leader in the development of connected products that allow its customer service personnel to monitor the health of its appliances in the field. The company recently launched a new high-end washing machine that generates more than 20 signals to measure various parameters, enabling real-time monitoring and predictive maintenance.
The Solution
Mabe collaborated with Altair to implement a three-stage solution: data pre-processing, machine learning, and data visualization. Mabe set up a high-performance SQL database to collect sensor data. Altair's Knowledge Studio, a machine learning and predictive analytics solution, was used to access the data and select the optimal machine learning models. Several models were built to automate performance analysis and failure predictions. Mabe engineers and Altair personnel developed a complete data analytics workflow that gathers sensor data from units in the field, applies a series of machine learning algorithms to that data, generates alerts about possible failures when discovered, and visualizes the data for in-depth analysis. Real-time data visualization was achieved using Altair's Panopticon, a comprehensive data visualization and streaming analytics platform. The entire workflow was built, tested, and deployed in less than 60 days.
Operational Impact
Quantitative Benefit
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