Machine Learning Solutions Optimized Manufacturing Line for a Major US-Listed Copper Manufacturer

Aug 12, 2022

Highlights of the Case Study: 

Particulars Description 
Client A US-listed copper manufacturing company partnered with Quantzig to optimize one of its mines in Arizona. 
Business Challenge The client wanted to cater to the rising demand for copper and was looking for means to increase its turnover by optimizing the copper mining and refining process within the same or reduced cost structure. 
Impact Quantzig used its machine learning-based solutions to help the client increase its turnover by increasing capacity utilization of the existing facility and optimizing the production line. 

Game-Changing Solutions for the Copper Mining Industry 

Let’s understand the role of machine learning solutions in the copper mining industry. Copper is one of the essential metals required for electrification, and it is critical to achieving the energy transition goals of growing economies by 2050. The global copper demand will probably double over the coming decades from 24 million metric tons to about 50 million tons, resulting in significant shortfalls. However, the current supply of copper cannot keep up with the surge in demand. Developing new mines is a time-consuming process spread across a decade or so before it can cater to the rising demand. In such a scenario, increasing capacity utilization and optimizing the existing manufacturing infrastructure is the quickest way to augment the supply of copper.  

Quantzig’s sensor data analytics solutions and machine learning algorithms can help copper mining and processing companies leverage technology to improve production the line, ensure compliance, perform predictive maintenance, and thus boost production. Our machine learning-based optimizers can also optimize the copper recovery and recycling processes.  

The Challenges of the Manufacturing Client 

A US-listed copper manufacturing company partnered with Quantzig to collect, analyze, and visualize logged historical machine data for one of its mines in Arizona. Despite being a world leader in copper production, the client struggled to meet the rising demand for copper. To meet the copper shortages, our client planned on switching to distributive sourcing, which required mining farther away from the sources. The client sought input from us data analytics and AI solutions to determine the location, evaluate the transportation costs, and ease the glitches inherent in the shipping of the final product.  

Manufacturing Operations Analytics Solutions for the Mining Industry


We developed a machine learning model which used the data from the sensors around the mine and suggested ways to enhance the performance of the industrial crushers and copper processing mills. Our system found that the mine was producing eight different types of ores and that the production process, which required floatation tanks, could be adjusted to retrieve a more considerable amount of copper by altering the pH level.  

We used our machine learning optimizers to develop predictive models and recover copper from printed circuit boards, thereby reducing wastage. Finally, our cross-functional analytical team assisted the client in transitioning to distributive sourcing by restructuring the mining schedules to account for transportation and shipments to mitigate shortages.  

Impact Analysis of Quantzig’s Machine Learning Optimizers  

Our machine learning optimizers helped the client to increase its turnover by increasing the capacity utilization of the existing facility and optimizing its production line. Consequently, the client could reduce the wastage involved and thus improve its productivity. Our solutions had the following impact on our client’s business:  

  • The client’s Arizona-based mine reported an increase in production by 10,000 tons. 
  • There was an increase in copper production by about 5%.  
  • There was an increase in capacity utilization (volume as a proportion of the existing mine’s total capacity). 
  • As the world leader, the client could increase its contribution to the world copper supply by up to 10%.  

In addition to rolling out Quantzig’s machine learning technology in the Arizona-based mine, the client extended the partnership by deploying our ML model in 26 other mines.  

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Critical outcomes of ML Solutions: 

Quantzig’s machine learning solutions enabled the client to optimize its production processes to deliver a higher percentage of copper. These solutions also helped to recover and recycle copper and thus prevent wastage. In addition, our analytical solutions helped the client to implement distributive sourcing by restructuring the entire process and mitigating shortages.  

A Broad Perspective on the role of ML Solutions in the Manufacturing Sector: 

According to a recent study, 50% of the industries that incorporate machine learning over the next few years will have the capacity to double their cash flow and increase turnover four-fold. Manufacturing industries will be among the foremost to adopt this technology owing to their heavy dependence on data. Machine learning and artificial intelligence can analyze patterns and solve specific problems in manufacturing by leveraging data. For instance, tracing anomalies in equipment, gaps in the production process, recovering residue from the pipelines, and reducing wastage are ways to optimize the manufacturing line. Implementing ML can generate an ROI (return on investment) of 2x and 4x in a year. Therefore, more than 50% of industrialists devote 20% of their budget to ML-based projects.   

Key Takeaways 

  • The client achieved a 5% increase in copper production. 
  • Our solutions helped reduce downtime by using predictive analytics solutions.   
  • Industrial automation solutions implemented by Quantzig helped to increase capacity utilization. 
  • Our solutions also enabled the recovery and recycling of copper. 

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Enhancing Inventory Planning Using Machine Learning | A Quantzig Success Story 

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Predictive Analytics Solutions Reduced Operating Costs for the After-Sales Support of a Major Medical Device Manufacturer 

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