What Manufacturers Can Learn From Formula One’s Industrial Optimization Model

Mercedes-AMG Petronas F1’s industrial optimization model TIBCO
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When it comes to optimizing manufacturing processes, no one is better at it than the Mercedes-AMG Petronas Formula One team. From the way the team collects data, to how it analyzes that data and optimizes its systems and processes, it serves as a model for manufacturers. Let’s take a look at what manufacturers can learn from Mercedes-AMG Petronas F1’s industrial optimization model in order to improve their own factories.

Data Collection

When it comes to data, like other manufacturers, the team produces a plethora of data that needs to be collected and analyzed.  This translates to 45 terabytes of data produced during the course of a race week, comprised of 50,000 data points from over 300 sensors. Similarly, in a factory, production machines generate large volumes of data that need to be analyzed and quickly. For example, a CPG company can generate 5,000 data samples every 33 milliseconds. Manufacturers can learn from F1’s amazing ability to collect, analyze, and act on that tremendous amount of data in near real time.

Manufacturers can learn from F1’s amazing ability to collect, analyze, and act on that tremendous amount of data in near real time. Click To Tweet

Data Analysis

For the Mercedes-AMG Petronas F1 team, one of the ways data is collected is from a digital twin simulator, which tests overall car performance. There are billions of combinations of car set-ups that are possible, so the team needs to use analysis and experience to figure out the best ones to test. 

Like F1, in a factory, Industrial Internet of Things (IIoT) data must be analyzed in real time to understand how a process is performing in order to detect anomalies. Digital twins are also used in factories to reduce waste and improve product quality; a faulty product can lead to increased costs, rework, and unhappy customers, in addition to hefty fines and business closures. Digital twins are able to achieve this by mimicking real-world processes by utilizing sensors data in real-time to hone in and predict the key elements and attributes to optimize production, or prevent unnecessary failures.

Optimization 

When everything is properly optimized, the Mercedes-AMG Petronas F1 team sees the most benefit at the track. After careful analysis of the data, the team is able to find the optimum car setup in rapidly changing circumstances leading to significant gains in performance. Other examples include a reduction in anomalies in gearbox changes, resulting in great track performance improvements, helping ensure the best race and qualifying lap times.

 Imagine what that kind of time-saving that type of optimization could do for your company.

In fact, without proper manufacturing optimization, manufacturers face unplanned outages, which translate in a lower Overall Equipment Effectiveness (OEE). However, when optimized, manufacturers see increased performance and higher quality products. 

Looking Ahead

In the coming decade, many manufacturers are going to be switching their smart factory strategy from one that was focused on technology implementation to one that is focused on process-change management. This will result in manufacturers treating their own IIoT assets like internal customers, reducing downtime, equipment failures, and diagnosing and resolving issues. Manufacturers will increasingly leverage digital twins driven by IIoT and machine learning in order to save operational expenses and optimize supply chains. 

In the coming decade, many manufacturers are going to be switching their smart factory strategy from one focused on technology implementation to one that is focused on process-change management. Click To Tweet

While Mercedes-AMG Petronas F1 pioneered the modern industrial optimization model, manufacturers are starting to implement these best practices into their own factories. From data collection, data analysis, and optimization, manufacturers have an opportunity for greater industrial optimization going forward. When utilized properly and with the right technology, factories can increase performance, reduce costs, and produce higher quality products.

Download this infographic to see in greater detail what manufacturers can learn from Mercedes-AMG Petronas F1’s industrial optimization model. And, to learn more about how TIBCO gives the team a competitive advantage, visit our partnership page