Eric Whitley Eric Whitley
Director of Smart Manufacturing

Integrating Advanced Data Analytics into Metal Forming Operations

January 9, 2024

The metal forming industry faces complex challenges, ranging from increasing material costs to stringent quality standards. Utilizing data analytics promises to enhance operational efficiency and equips businesses with the tools they need to adapt swiftly to market changes and customer needs.

Precision in metal forming represents much more than just a way to maintain quality. It's also a critical factor in ensuring cost-effectiveness and resource optimization. Data analytics plays a vital role here, offering insights into process parameters and enabling manufacturers to achieve the highest levels of precision.

With the following data-driven processes, companies can significantly enhance their operational efficiency:

  • Real-time monitoring through data analytics transforms how companies manage their metal forming operations. Data analytics allows managers to immediately identify and correct process deviations, ensuring optimal performance. This proactive approach to process control boosts efficiency and minimizes the risk of defects and rework, leading to reliable and consistent production outcomes.
  • Predictive analytics serves as a powerful tool in extending machine life and proactively addressing maintenance needs. It allows companies to analyze patterns and predict tool wear in order to proactively schedule maintenance activities and avoid unexpected downtime. This predictive approach enhances tool lifespan while enabling smooth and uninterrupted operations.

Reduced Reject Rates, Refined Process Accuracy

For a practical example of data analytics in action, consider its application in reducing reject rates. Through detailed data analysis, manufacturers can fine-tune their processes and make precise adjustments to address quality issues. The result: reduced reject rates and enhanced efficiency and product quality.

Closed-loop control systems, guided by data analytics, continually adjust operations based on real-time data, ensuring that production stays within specified tolerances. These systems improve process reliability and consistency, essential for maintaining quality standards.

Too, machine-learning algorithms are revolutionizing process-parameter optimization by analyzing vast amounts of production data, which helps identify patterns and optimize parameters for peak performance. This adaptive approach results in processes with improved accuracy and efficiency, reducing waste and increasing output quality.

One tangible outcome of employing data analytics is the achievement of micron-level tolerances. Leveraging data for precise control of machinery and processes enables metal formers to produce components with exceptional accuracy.

Reducing Waste and Downtime

Waste and downtime represent two of the most significant cost drivers in metal forming. Addressing these issues helps metal formers maintain profitability and sustainability, and data analytics plays a role here as well. Predictive maintenance, powered by data analytics, transforms equipment upkeep, enabling shops to schedule maintenance when it is needed and preventing unexpected machine failures that cause costly downtime. It ensures that machinery continuously operates at peak efficiency, essential for minimizing waste and maximizing productivity.

Scrap reduction also benefits from the use of data analytics. By analyzing production data, manufacturers can identify and address the root causes of scrap, leading to more efficient material usage. This reduces waste while contributing to cost savings and environmental sustainability.

To examine how data analytics can help manufacturers reduce downtime, consider Wiscon Products, Racine, WI, a contract machine shop that manufactures precision parts for a variety of industries, including automotive. The firm launched its Industry 4.0 journey some 5 yr. ago, including the installation of equipment to gather machine metrics. A visual dashboard displays parts goals and machine utilization, providing accurate data that workers and managers can use in real time.

As a result, Wiscon has increased its overall capacity by 30% and operator efficiency by 48%. It also has seen machine utilization rise by 30%, operator productivity climb by 250%, and sales per employee increase by $84,000/yr. As a result of these cost savings and efficiency gains, Wiscon increased operator pay by 7%.

Other Examples of Practical Applications

The metal forming industry showcases numerous real-world examples of the transformative impact of data analytics. Appliance manufacturers, for example, use Internet of Things and data analytics to enhance operational efficiency.

Real-time data from sensors and controls assists in capturing, storing and analyzing machine and process data, leading to significant improvements in manufacturing processes. This technology enables manufacturers to formulate plans based on overall equipment effectiveness (OEE) and evaluate employee utilization, thus optimizing machinery and staffing resources.

Manufacturers serving the electronics industry also use big data and advanced analytics, from managing equipment in real-time to securing continuous improvement in production. For example, Yamaha’s Factory Tools suite and the Yamaha Dashboard, embedded with Tableau analytics software, provide live and historical production analytics. These tools help visualize operating quality status, analyze OEE metrics, and identify causes of problems or defects, thus enhancing productivity and product quality​.

Demonstrating the Relevance of Data-Driven Insights

Embracing a data-first culture proves essential for businesses looking to thrive in the modern metal forming industry. This shift involves valuing data as a primary decision-making tool across all levels of the organization.

Case in point: Ford Motor Co., which employs FTI FormingSuite virtual-manufacturing software for evaluating designs and simulating processes, to help define more efficient processes at early development stages. 

In addition, in this 2021 report from McKinsey & Company we learn of the World Economic Forum’s recognition of four steel companies (BaoSteel China, Posco South Korea, Tata Steel India and Tata Steel Netherlands) as Industry 4.0 Lighthouses, underscoring their successful implementation of digital and analytics at scale​​. This recognition emphasizes the industry-wide potential and benefits of embracing digital technologies and analytics. 

The application of digital production techniques and analytics in the steel industry has led to breakthrough increases in top-line revenues and substantial cost improvements. This approach differs from traditional methods and indicates a potential leapfrog ahead of the competition for companies that successfully harness these technologies​. MF 

Industry-Related Terms: Forming, Point, Scrap
View Glossary of Metalforming Terms


See also: L2L

Technologies: Management, Sensing/Electronics/IOT


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