Case Studies Aluminerie Alouette implements STATISTICA Data Miner and MSPC

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	Aluminerie Alouette implements STATISTICA Data Miner and MSPC

Aluminerie Alouette implements STATISTICA Data Miner and MSPC

Technology Category	Analytics & Modeling - Data Mining Analytics & Modeling - Machine Learning Analytics & Modeling - Predictive Analytics
Applicable Industries	Metals
Applicable Functions	Process Manufacturing Quality Assurance
Use Cases	Machine Condition Monitoring Predictive Maintenance Process Control & Optimization
Services	Software Design & Engineering Services Training
Challenge	Aluminerie Alouette needed to continuously improve its production processes to stay among the worldwide leaders in aluminum manufacturing. The company faced the challenge of understanding the influence of several hundred inputs on the aluminum manufacturing output. Some inputs could be controlled, such as the dosage of additives and energy management, while others, like outside temperature and raw material composition, could not. To address this, Aluminerie Alouette required a solution that could identify significant inputs and develop multivariate models to monitor key performance indicators. Read More
About Customer	Aluminerie Alouette, established in 1992, is an independently operated company producing primary aluminum. With a workforce of 1,000 employees and an annual production capacity exceeding 600,000 tons, it stands as the largest employer in Sept-Îles, Canada, and the leading aluminum smelter in the Americas. The Sept-Îles smelter is renowned globally for its energy consumption efficiency and state-of-the-art technology, surpassing government environmental standards. Aluminerie Alouette has been utilizing STATISTICA Enterprise for several years to monitor key performance indicators and control the production process efficiently. Read More
Solution	To better understand the influence of various inputs on the aluminum manufacturing process, Aluminerie Alouette augmented its existing STATISTICA Enterprise with STATISTICA Data Miner and MSPC software for multivariate analyses. These StatSoft modules were expected to identify inputs with significant influence on key performance indicators and develop multivariate models for monitoring additional indicators. The implementation of these modules allowed Aluminerie Alouette to conduct several analyses, validating that STATISTICA Data Miner and MSPC met their needs. Automated neural networks in STATISTICA enabled the development of models representing different operating scenarios based on historical data. These models allowed for the adjustment of relevant inputs without compromising the quality of operations and facilitated medium-term production predictions considering future events. Read More Log in to view content
Contents

Technology Category

Analytics & Modeling - Data Mining

Analytics & Modeling - Machine Learning

Analytics & Modeling - Predictive Analytics

Applicable Industries

Metals

Applicable Functions

Process Manufacturing

Quality Assurance

Use Cases

Machine Condition Monitoring

Predictive Maintenance

Process Control & Optimization

Services

Software Design & Engineering Services

Training

Challenge

Aluminerie Alouette needed to continuously improve its production processes to stay among the worldwide leaders in aluminum manufacturing. The company faced the challenge of understanding the influence of several hundred inputs on the aluminum manufacturing output. Some inputs could be controlled, such as the dosage of additives and energy management, while others, like outside temperature and raw material composition, could not. To address this, Aluminerie Alouette required a solution that could identify significant inputs and develop multivariate models to monitor key performance indicators.

About Customer

Aluminerie Alouette, established in 1992, is an independently operated company producing primary aluminum. With a workforce of 1,000 employees and an annual production capacity exceeding 600,000 tons, it stands as the largest employer in Sept-Îles, Canada, and the leading aluminum smelter in the Americas. The Sept-Îles smelter is renowned globally for its energy consumption efficiency and state-of-the-art technology, surpassing government environmental standards. Aluminerie Alouette has been utilizing STATISTICA Enterprise for several years to monitor key performance indicators and control the production process efficiently.

Solution

To better understand the influence of various inputs on the aluminum manufacturing process, Aluminerie Alouette augmented its existing STATISTICA Enterprise with STATISTICA Data Miner and MSPC software for multivariate analyses. These StatSoft modules were expected to identify inputs with significant influence on key performance indicators and develop multivariate models for monitoring additional indicators. The implementation of these modules allowed Aluminerie Alouette to conduct several analyses, validating that STATISTICA Data Miner and MSPC met their needs. Automated neural networks in STATISTICA enabled the development of models representing different operating scenarios based on historical data. These models allowed for the adjustment of relevant inputs without compromising the quality of operations and facilitated medium-term production predictions considering future events.

Impact #1	The implementation of STATISTICA Data Miner and MSPC initiated a new approach at Aluminerie Alouette to identify the source of problems related to the production process.
Impact #2	The software solutions provided a better understanding of the interaction between different inputs, allowing for more informed decision-making.
Impact #3	The deployment of models enabled the exploration of different scenarios by extrapolating outside the current domain, enhancing operational flexibility.

Impact #1

The implementation of STATISTICA Data Miner and MSPC initiated a new approach at Aluminerie Alouette to identify the source of problems related to the production process.

Impact #2

The software solutions provided a better understanding of the interaction between different inputs, allowing for more informed decision-making.

Impact #3

The deployment of models enabled the exploration of different scenarios by extrapolating outside the current domain, enhancing operational flexibility.

Benefit #1	Aluminerie Alouette achieved an annual production capacity of over 600,000 tons of aluminum.
Benefit #2	The company employs 1,000 people, making it the largest employer in Sept-Îles, Canada.

Benefit #1

Aluminerie Alouette achieved an annual production capacity of over 600,000 tons of aluminum.

Benefit #2

The company employs 1,000 people, making it the largest employer in Sept-Îles, Canada.

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Overview

Aluminerie Alouette implements STATISTICA Data Miner and MSPC

Operational Impact

Quantitative Benefit