Altair Case Studies AI-Driven IoT Solution Enhances Product Quality and Customer Service for Mabe

	AI-Driven IoT Solution Enhances Product Quality and Customer Service for Mabe Altair

AI-Driven IoT Solution Enhances Product Quality and Customer Service for Mabe

Altair

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
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. Read More
About 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. Read More
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. Read More Log in to view content
Contents

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

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 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.

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.

Impact #1	The implementation of the AI-driven IoT solution has provided Mabe with a deep understanding of real-world use conditions. It has enabled them to identify components and subassemblies that require design, supplier, or manufacturing process changes, supporting continuous product improvement. The system has also improved Mabe’s customer service, shortened complaint response times, and reduced the number of warranty service calls required. The solution has also added value by being transferrable, allowing Mabe to replicate the complete workflow with other product lines. This has resulted in more reliable products, reduced costs, improved competitiveness, and increased customer satisfaction.

Impact #1

The implementation of the AI-driven IoT solution has provided Mabe with a deep understanding of real-world use conditions. It has enabled them to identify components and subassemblies that require design, supplier, or manufacturing process changes, supporting continuous product improvement. The system has also improved Mabe’s customer service, shortened complaint response times, and reduced the number of warranty service calls required. The solution has also added value by being transferrable, allowing Mabe to replicate the complete workflow with other product lines. This has resulted in more reliable products, reduced costs, improved competitiveness, and increased customer satisfaction.

Benefit #1	The entire workflow was built, tested, and deployed in less than 60 days.
Benefit #2	The solution allows real-time monitoring of over 20 different parameters.
Benefit #3	The solution is transferrable and can be replicated with other product lines.

Benefit #1

The entire workflow was built, tested, and deployed in less than 60 days.

Benefit #2

The solution allows real-time monitoring of over 20 different parameters.

Benefit #3

The solution is transferrable and can be replicated with other product lines.

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Overview

AI-Driven IoT Solution Enhances Product Quality and Customer Service for Mabe

Operational Impact

Quantitative Benefit