Minimize Costs, Maximize Production Volume With Live Optimization

Keeping costs low and boosting production volume are two of the biggest challenges manufacturers face. In this post, you'll learn how a live machine learning solution can help you address both these concerns while ensuring that the quality stays within the necessary specs, whether you produce steel or cement, chemicals or CPG.

A live machine learning solution streams data directly from the production line and tells engineers and operators how to adjust settings based on this real-time data. Instead of applying static assumptions to a broad range of processes, you get live predictions and optimizations tailored to your unique raw materials, or to the process setting impacting your quality at the time of the prediction/optimization. Skip unnecessary tests or add fewer raw materials, while remaining confident that your quality falls within specs.

Minimize costs

Everyone wants to keep costs low. With live machine learning, you can see exactly how much raw material you need to reach a given quality standard, keeping costs as low as possible.

Here's a look at how this works in a steel mill. Mistakes aren't cheap in steel, where a scrapped heat can cost as much as $100,000 due to wasted time and materials. So accuracy is of vital importance.

As part of the initial Fero setup phase, engineers in the steel mill tell the software what yield and tensile strengths they need to achieve. Based on the specified quality requirements, Fero can analyze a sample of each heat and tell operators the minimum amount of each alloy they need to add, in real time, to ensure they meet those requirements. As a result, costs and raw materials are kept as low as possible.

It's crucial to note that the software's recommendations don't come out of a mysterious black box. Rather, they're explainable. Confidence bands and buffer zones displayed prominently on the interface ensure that these recommendations fall within quality standards. If there's a slight probability—even 5%—of the heat being scrapped given the current settings, the prediction tells the operator how to change the settings to mitigate such an outcome.

What's the ROI? Within a month of enabling Fero's live optimization feature to provide engineers and operators with constant real-time recommendations, one steel company reduced alloy consumption by 16%, saving as much as $4 per ton as a result. When you consider that your typical steel mill generates over a million tons per year, this adds up to millions in savings.

Maximize production volume

Manufacturers are under constant pressure to increase throughput. Eliminating unnecessary tests, and the production bottlenecks associated with them, is an easy way to boost production volume. However, products also need to be tested to ensure they meet quality requirements.

With live predictions, you can reduce the number of tests you do by eliminating the unnecessary ones. Fero can look at the parameters for each batch or test group and reveal when the test can be skipped because the item will certainly fall within the desired specs, allowing engineers to focus more on production rather than testing and thus, increase their throughput dramatically. This Fero feature can reduce tests by as much as 70% and, by removing the testing bottleneck, bring production volume up as a result.

With fewer tests, you might think you're reducing your chances to control the quality of the product. To the contrary, live machine learning actually offers increased quality control and visibility. Fero software can run a virtual "test" in between official test readings, a functionality that's been deployed across chemicals, oil and gas, and other continuous producers. You can improve the process and the yield more if you actually know what your quality KPIs are in real time vs. waiting for a test to be completed every eight hours.

These are just a few examples of the many ways that live machine learning can benefit your production line. From cost savings and yield improvements to increasingly valuable sustainability metrics such as emissions reduction, basing decision-making on existing factory data rather than conservative estimates can be transformative, optimizing both the production process and your bottom line. With thousands of sensors in your factory, shouldn't you turn that data into your competitive advantage?

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